V olume 27 Issue 3 Ar ticle 3 September 2025 Ser vicification of Manufacturing in Global V alue Chains: How Ser vicification of Manufacturing in Global V alue Chains: How Ser vices T rade and F or eign Dir ect Inv estment Shape Expor t Ser vices T rade and F or eign Dir ect Inv estment Shape Expor t Quality and V olume Quality and V olume Jakob Stember ger Univ ersity of Ljubljana, School of E conomics and Business, Ljubljana, Slo v enia , jakob.stember ger@ef.uni- lj.si Katja Zajc K ej ž ar Univ ersity of Ljubljana, School of E conomics and Business, Ljubljana, Slo v enia F ollow this and additional works at: https:/ /www .ebrjournal.net/home P ar t of the International E conomics Commons Recommended Citation Recommended Citation Stember ger , J., & K ej ž ar , K. (2025). Servicification of Manufacturing in Global V alue Chains: How Ser vices T rade and F or eign Dir ect Inv estment Shape Expor t Quality and V olume. E conomic and Business Re view , 27(3), 160-174. https:/ /doi.or g/10.15458/2335-4216.1358 This Original Ar ticle is br ought t o you for fr ee and open access b y Economic and Business Re view . It has been accepted for inclusion in E conomic and Business Re view b y an authoriz ed edit or of E conomic and Business Re view . ORIGINAL ARTICLE Servicication of Manufacturing in Global Value Chains: How Services Trade and Foreign Direct Investment Shape Export Quality and Volume JakobStemberger * ,KatjaZajcKejžar University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia Abstract This study examines the role of services trade and foreign direct investment (FDI) in shaping export performance among manufacturing rms participating in global value chains. Using rm-product-destination level panel data for Slovenia (2008–2020), the analysis investigates whether servicication—the growing integration of services into manufacturing—enhances rms’ export quality and export volume. The ndings reveal that services imports at the destination level signicantly enhance export quality, particularly for consumer and intermediate goods, while services exports positively inuence export volumes, suggesting strong complementarities between goods and services trade. Outward FDI is a key driver of both higher export quality and volume, while inward FDI has mixed effects, beneting quality but occasionally reducing export volumes. These results highlight the critical role of services trade and FDI in global value chain upgrading and suggest that policies promoting servicication and strategic international investments can enhance rms’ competitiveness in global markets. Keywords: Services trade, Global value chains (GVCs), Export quality and volume, Servicication, Foreign direct investment (FDI) JEL classication: F14, F23, L80 1 Introduction A s per the Organisation for Economic Co- operation and Development (OECD, n.d.), services are the cornerstone of the global economy, contributing over two thirds of the world’s GDP , account for more than three quarters of foreign direct investment in developed nations, serve as the largest source of employment, and drive the creation of the majority of new jobs on a global scale. Nayyar and Davies (2023) report that services account for the largest portion of global GDP and are the primary engine of output and employment growth worldwide. However, capturing cross-border trade in services remains a persistent challenge (Bohn et al., 2018). Furthermore, around a fth of world gross trade consists of services trade, whereas in terms of gross value-added trade, services account for roughly a half (up from 30% in 1995). The larger share of services in value-added trade is explained by the fact that the value added of services is often integrated into manufacturing activities and consequently exported as manufacturing goods. Additionally, pertaining to the servicication of manufacturing, the share of value added attributable to services in manufacturing has grown over time, with services contributing around 30% to global value added in manufacturing in 2015, up from 20% in 2005 (Cigna et al., 2022). Clearly, services play an outsized role in international trade generally and global value chains (GVCs) specically, as the latter are not only the glue linking value chains together but are also value-adding activities (Heuser & Mattoo, 2017; Low, 2013; Miroudot & Cadestin, 2017). Principally, GVCs depend on efcient logistics, transport, communication, nance, other business, Received 18 March 2025; accepted 14 July 2025. Available online 10 September 2025 * Corresponding author. E-mail address: jakob.stemberger@ef.uni-lj.si (J. Stemberger). https://doi.org/10.15458/2335-4216.1358 2335-4216/© 2025 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/). ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 161 and professional services to facilitate the movement of goods and coordinate production throughout the value chain (Miroudot & Cadestin, 2017). In this re- gard, one can perceive the emergence and existence of GVCs as a direct result of advancements in services such as transportation, communication, and IT, which have enabled the fragmentation and coordination of production processes across the globe. Resultantly, this paper argues that services can be considered analogously to goods in both the analysis and mea- surement of GVCs. However, services play a distinct role in GVCs compared to goods, as they enable the formation of GVCs in ways that goods cannot. Ser- vices function as unique components within GVCs, differing from the conventional cross-border or arm’s- length trade typically associated with goods (Heuser & Mattoo, 2017). In essence, industrialised coun- tries’ benets from globalisation increasingly rely on intangible assets 1 rather than physical goods, with Al- samawi et al. (2020) suggesting that intangible assets contribute to 27% of the income generated by man- ufacturing GVCs in OECD countries. Furthermore, services play a critical role in facilitating transactions across space (transport, telecommunications) and time (nancial services) and constitute signicant and increasing portions of national incomes and employ- ment, making them systemically important. They are essential inputs for all economic activities and signif- icantly inuence the productivity of core production factors: labour and capital. Thus, they are crucial for the broader economy and the efcient functioning of both domestic and international value chains. Lastly, with higher growth rates than agriculture and man- ufacturing, they have been a key driver of GDP and employment growth (Kowalski et al., 2015). This paper contributes to the literature on GVCs by addressing several interrelated theoretical and empirical gaps. While existing GVC research has highlighted mechanisms of upgrading and rm par- ticipation, it often treats rms as homogenous ac- tors and focuses predominantly on manufacturing, overlooking the increasing complexity of rm-level behaviour and the growing importance of services. This paper responds to three key limitations in the literature. First, despite increasing interest in rm participation in GVCs, little is known about the link between export product quality and rm-level GVC integration. This represents a deeper theoreti- cal gap: current GVC frameworks rarely explain how rm-specic capabilities related to quality upgrading inuence participation trajectories. Second, although the servicication of manufacturing is a growing trend, existing theory tends to focus solely on man- ufacturing processes and has yet to fully incorporate how the integration of services such as logistics, R & D, and business support shapes rms’ upgrading potential within GVCs. This challenges foundational assumptions in GVC theory about where and how value is created. Fundamentally, both theoretical and empirical gaps remain in understanding how trade in services inuences rms’ participation in GVCs. Notably, even in the comprehensive overview of the current state of GVC literature by Antràs and Chor (2022), there is a lack of theoretical models and em- pirical studies that explicitly differentiate between participation through goods trade and services trade. Third, there is a disconnect between rm-level and macro-level analyses: while trade economists often model GVCs from a country or industry perspec- tive, they underappreciate the heterogeneity of rm strategies, a shortcoming that international business (IB) research—see, for example, Kano et al. (2020)— has begun to address. By combining servicication, export product quality and rm-level GVC partici- pation, this paper builds a more nuanced, multilevel perspective on value chain dynamics and offers a the- oretically grounded explanation of rm heterogeneity in GVC engagement and performance. Recognising multifaceted roles of services provides a broader context for analysing export performance, especially because improvements in service efciency (e.g., logistics, nance, or communication) may also indirectly raise the quality of goods exports by reducing production and coordination costs. More specically, this paper argues that the incorporation of such services within a rm can generate similar positive spillovers as described in Anwar and Sun (2018) and presented in the literature review section, implying that services may enhance a rm’s productivity, which results in them producing higher-quality goods and thus leads to higher export unit values. Conversely, service tasks themselves can form the main source of a rm’s sales and exports, challenging the traditional goods-centric paradigm of trade analysis. Bringing these two strands, export quality and the services dimension of GVCs, together highlights how foreign presence and service-based activities collectively inuence the competitiveness of a rm and an industry. Specically, while foreign presence may elevate quality and productivity in goods exports, the simultaneous role of services can further 1 Intangible assets include trademarks, copyrights, patents, brand names, software, product designs, databases, and certain types of business organisation structures (Cummins, 2005). 162 ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 modify the nature and trajectory of value creation. For instance, if a rm specialises in high-end service tasks within GVCs, it may also generate positive spillovers that enhance the broader trade ecosystem. On the other hand, the entry of lower-capability rms or less sophisticated service providers can complicate the net impact on measured export quality, prompting an empirical examination to ascertain the balance of these forces. Furthermore, foreign direct investment (FDI) plays a pivotal role in rms’ export performance, with po- tential impacts on both the quality and volume of goods they export (e.g., Harding & Javorcik, 2012; Zhu & Fu, 2013). By injecting capital, technology, and managerial know-how, FDI can spur productiv- ity gains that enable rms to produce higher-quality products and scale up their export volumes. In line with this reasoning, Anwar and Sun (2018) and Swen- son and Chen (2012) nd that foreign rm presence signicantly boosts the quality of local exporters’ products, enhancing their international competitive- ness. Also, Chun et al. (2021) state that outward FDI by domestic rms facilitates greater export quantities through technology transfer and improved market access. However, FDI’s impact is not uniform across all contexts—for example, while some research docu- ments clear quality gains, other work nds mixed or even negative effects under certain conditions (e.g., Liu et al., 2024; Sun, 2009). This mix of ndings underscores the importance of examining how FDI inuences both export product quality and volume. The aim of this paper is to determine, among man- ufacturing rms that are GVC participants, which internationalisation activity contributes most to the rm’s export product quality and volume: trade in services or FDI. This research focuses exclusively on manufacturing rms to specically examine the effects of the servicication of manufacturing—the in- creasing integration of services into traditional manu- facturing processes. Ultimately, this paper denes the key transmission channels for the rm’s quality of ex- ported products, outlines the exact role services trade has in the process, and assesses the role of servicica- tion of manufacturing rms. By doing so, the paper aims to provide new insights into the drivers of ex- port quality and volume, paying particular attention to how services integrate and reshape value chains in a globally interconnected economy. More specically, this paper answers the following research question: Which activities affect a GVC-participating rm’s ex- port quality and volume, and what are the effects of these activities? As such, this paper has empirical implications, as the literature examining export prod- uct quality within GVCs at rm level is limited and as most other rm-level GVC studies focus solely on trade in goods. A key strength of this study lies in its use of a comprehensive rm-level dataset for Slovenia—an economy deeply integrated into global trade. In 2024, exports of goods and services accounted for 81.5% of its GDP (World Bank, 2025), placing it 19th globally in trade openness. Its strong participation in European value chains makes Slovenia an empirically rich and analytically appropriate context for examining how services trade and FDI affect export quality and vol- ume. The country’s GVC participation index stood at 56.7 in 2018 (World Trade Organization, n.d.), well above the European average of 48.8, underscoring its deep embedment in international production net- works. With a large number of small manufacturing suppliers, Slovenia also demonstrates robust back- ward GVC participation. In addition, Slovenia offers a theoretically relevant setting: as a small, open EU economy with a strong manufacturing base and a postsocialist institutional legacy, it reects structural features central to GVC participation and servicica- tion dynamics. Moreover, its manufacturing sector is highly export-oriented and increasingly intertwined with services, as Jakliˇ c et al. (2020) note that Slove- nian manufacturers rely heavily on imported goods and services, while Stare et al. (2019) nd that for- eign services account for a growing share of export value added. As Michailova (2011) and Welch et al. (2022) argue, explicitly leveraging national context enhances the theoretical contribution of IB research. The insights from this study may thus extend beyond Slovenia to inform policy and strategy in other small or peripheral EU economies and emerging markets with similar integration trajectories. The database constructed for the purposes of this paper includes the following databases for the 2008–2020 period that have been—via unique rm identiers—merged together: (i) transaction-level trade data at the 8-digit level of the European Combined Nomenclature classication provided by the Statistical Ofce of the Republic of Slovenia, (ii) detailed transaction-level trade in services data for a sample of Slovenian rms by the Bank of Slovenia, (iii) a rm-level database of nancial statements col- lected by the Agency of the Republic of Slovenia for Public Legal Records and Related Services, containing all nancial data for all rms registered in Republic of Slovenia, and (iv) information on rms’ cross-border direct investment (FDI) inows and outows provided by the Bank of Slovenia. These combined datasets offer detailed panel data at the rm-product- destination level, making Slovenia an empirically illustrative and well-suited case for such a study. ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 163 For the empirical analysis, this paper employs a xed-effects panel regression to estimate the impact of a rm’s trade in services and FDI ows on its export quality and volume, focusing specically on manu- facturing rms that are also participants in GVCs. Further, it distinguishes between types of goods (in- termediate, consumer, and capital goods) to examine whether the effects of the abovementioned interna- tionalisation activities vary with respect to the type of goods in question. This paper highlights the transformative role of ser- vices trade and FDI in shaping the export quality and volume of Slovenian manufacturing rms participat- ing in GVCs. The ndings reveal that market-specic services imports signicantly enhance export quality to that market, especially for consumer and interme- diate goods, while services exports positively inu- ence goods export volumes. Outward FDI emerges as a key driver of both export quality and volume, which underscores its role in technology transfer and market access, whereas inward FDI exhibits mixed effects, beneting export quality but occasionally di- minishing export volumes for consumer goods. The paper underscores the nuanced effects of interna- tionalisation strategies, which vary across different types of goods, and offers actionable insights for pol- icymakers to foster competitiveness by prioritising servicication, strategic FDI, and enhanced access to high-quality service inputs. These ndings contribute to understanding the evolving dynamics of GVCs and the growing importance of intangible assets in mod- ern trade. The remainder of the paper is organised as follows. In the next section, a summary of the relevant liter- ature is given. Section 3 introduces the methodology utilised in the empirical part and presents data and descriptive statistics. The empirical analysis and re- sults’ discussion are included in Section 4. The last section summarises and offers conclusions. 2 Literature review 2.1 Theoretical background Global production has become increasingly frag- mented across countries, resulting in complex GVCs coordinated by lead rms overseeing dispersed sup- pliers and afliates. This trend marks a shift from the hierarchical multinational enterprise (MNE) model toward networks of specialised partners (Coe & Yeung, 2015; Yeung, 2016). Kano et al. (2020) concep- tualise a GVC as a distinct governance form that must deliver efciency gains over simpler alternatives (e.g., internal hierarchy or arm’s-length trade) to persist. In their framework, governance issues arise at the rm level (strategies and capabilities), the GVC level (net- work structure and coordination), and the macro level (institutional environment). This implies that inter- national economics (IE) theories of trade and quality must be integrated with IB concepts of governance and coordination. In particular, rms’ export quality depends not only on their productivity (an IE per- spective) but also on how they orchestrate and adapt within global networks (an IB perspective). From an IE perspective, rm heterogeneity drives selection into export markets. Only the most produc- tive producers export, and those rms tend to deliver higher-quality outputs (Bernard & Bradford Jensen, 1999; Kugler & Verhoogen, 2012; Melitz, 2003). More- over, exporters are a select group: Typically only a minority of rms export, and exporters are gener- ally larger and more productive than nonexporters (see e.g. Bernard & Bradford Jensen, 1995, 1999, 2004; Clerides et al., 1998; Eaton et al., 2004; Pavcnik, 2002). Building on the Melitz model, Anwar and Sun (2018) show that the presence of foreign rms in an industry can boost that industry’s export quality via productiv- ity spillovers—consistent with this, the authors nd that foreign presence in China’s manufacturing sig- nicantly raises the industry’s export quality—, an effect directly reected in higher export prices. This provides a theoretical rationale for using export unit value (a price-based measure) as an observable proxy for underlying export quality. However, foreign en- try may also lower barriers for less productive rms, whose lower-quality outputs can offset some gains. Thus, the net impact of foreign presence on overall industry quality is ambiguous ex ante, which under- scores the need for empirical verication. Despite the limitations of price-based quality measures, export unit values remain a widely used and practical indi- cator of export quality in empirical research. From an IB perspective, the fragmented nature of GVCs makes coordination and governance cen- tral concerns. IB theory highlights the role of the lead rm in orchestrating the value chain (Rug- man & D’Cruz, 1997). Lead-rm headquarters must manage complex interrm linkages: ne-slicing the value chain into tasks, controlling critical informa- tion ows, and coordinating independent partners across borders. This role often demands advanced management capabilities (Buckley, 2009; Kano, 2018), sometimes more so than in a conventional MNE. In effect, the headquarter becomes a hub that bundles resources, aligns incentives, and steers the network. Kano et al. (2022) further argue that managerial governance adaptations—the routines and decision processes leaders adjust in response to disruption or new information—are crucial for long-run GVC re- silience. That is, beyond relocating activities, rms 164 ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 must adapt how they govern (e.g., switching between hierarchical control and collaborative contracts) to maintain efciency under change. From a theoreti- cal perspective, these ideas support transaction-cost and internalisation views: A GVC as a governance form exists because it economises on coordination costs. Kano et al. (2020) underscore that a GVC will thrive only if it aligns its structure with the at- tributes of transactions in a cost-efcient way. Modern ICT, logistics, and transport services facilitate this alignment: by improving communication and linking distant stages, these services have allowed rms to manage geographically dispersed production (Jones & Kierzkowski, 2001). In other words, coordination across a chain often relies on complementary service inputs to reduce delays and uncertainties. Together, these IE and IB perspectives provide a foundation for understanding how rm-level capabilities and gover- nance, along with market selection, jointly determine export quality in GVCs. 2.2 Firm-level evidence on export quality The quality of exported products has signicant implications for trade performance. Higher-quality goods fetch higher prices in international markets (see, e.g., Hallak & Sivadasan, 2009; Kugler & Ver- hoogen, 2012) and higher-income countries tend to export relatively higher-quality goods (see, e.g., Hal- lak & Schott, 2011; Khandelwal, 2010). These patterns further justify using unit values as quality proxies and underscore why export quality is a pivotal fac- tor in international trade and development. However, such ndings also underscore rm-level differences: Kano et al. (2020) emphasise that even within the same industry and region, lead rms vary widely in their control and governance strategies, driven by their own capabilities and goals. In other words, rm-level strategic choices (not just country or sec- tor factors) shape how GVCs are organised and, ultimately, what quality outcomes emerge. Thus, un- derstanding export quality requires examining both the productivity-driven selection emphasised by IE and the governance and strategic adaptation empha- sised by IB. At the rm-product level, evidence shows a posi- tive link between productivity and export quality. For example, more productive Portuguese manufacturers export larger quantities at higher unit prices within the same product categories, which indicates they produce higher-quality goods (Bastos & Silva, 2010). Such rms also set higher unit values for identical products in more distant or wealthier markets, and productivity further amplies this pricing-to-market effect. Similar patterns have been observed in other countries; see, for example, Manova and Zhang (2012) for Chinese data, Görg et al. (2016) for Hungarian data, Martin (2012) for French data, and Harrigan et al. (2015) for U.S. data. Consistent with this evi- dence, export unit value is widely used as a rm-level proxy for product quality. GVC participation appears to be another factor associated with higher export quality. Deep participa- tion in GVCs is indeed associated with higher export product quality, via access to advanced foreign inputs and knowledge—relevant to this paper, albeit on a macro level, is the study by Ndubuisi and Owusu (2021). Similarly, Brambilla and Porto (2016) suggest that producing high-quality goods comes with higher production costs. These goods are typically exported to wealthier countries and rms producing them tend to offer higher wages to their employees. Addition- ally, greater foreign services value added in exports is linked to longer-lasting trade relationships (Díaz- Mora et al., 2018). Lastly, Bernini et al. (2015) nd, using French rm-level data, that higher nancial leverage is associated with lower export quality. Opposingly, the impact of FDI on export qual- ity is more ambiguous. Some studies nd that FDI brings positive spillovers, boosting local rms’ ex- port quality (Swenson & Chen, 2012; Anwar & Sun, 2018). However, research on Chinese rms often nds small or even negative FDI effects on export qual- ity (Liu et al., 2024; Lu et al., 2022; Sun, 2009). On the other hand, FDI can have indirect benets: the presence of MNEs in upstream sectors is associated with improved export quality for downstream rms (Bajgar & Javorcik, 2020). In sum, the net impact of FDI on export quality remains context-dependent. Finally, some relevant macro-level papers include Harding and Javorcik (2012), Guerra (2024), Khandel- wal (2010), and Zhu and Fu (2013). 2.3 Servicication of manufacturing rms Although trade in services plays a pivotal role in GVCs, it remains underrepresented in much of the ex- isting literature, especially within IE. Studies—within IB literature—that recognise the enabling function of services in supporting manufacturing GVCs typi- cally rely on analytical frameworks originally devel- oped for manufacturing value creation; most notably Porter’s value chain (1985) and the “smiley curve” (Mudambi, 2008). Stabell and Fjeldstad (1998) provide a notable extension by proposing distinct value con- guration models—value chains, value shops, and value networks—to capture diverse ways services generate value. In this context, the growing infusion of services into manufacturing is signicant. In the IB literature on service growth dynamics, a distinction is often made between service infusion and servitisation (in ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 165 this paper referred to as servicication; see, e.g., Fork- mann et al., 2017; Kowalkowski et al., 2017; Raddats et al., 2019; Raškovi´ c et al., 2025). The former refers to the growing importance of service offerings within a company or business unit, reecting an increase in service business orientation, which, according to Homburg et al. (2002), can be assessed through three dimensions: the number of services offered, the num- ber of customers receiving those services, and the relative emphasis placed on services. This concept aligns with Shostack’s (1977) product-to-service con- tinuum, which posits that a rm’s service orientation strengthens as intangible elements become more cen- tral to its offerings. The latter, by contrast, goes beyond service infusion. It entails a fundamental shift from a product-centric to a service-centric business model and logic. This transformation often requires reconguring organisational structures and capabili- ties (Baines et al., 2009), redening the rm’s strategic mission, and reshaping internal routines, values, and norms (Kindström & Kowalkowski, 2014). Even simple infusion can upgrade product offer- ings: for example, manufacturers may bundle R & D, design, or after-sales support with goods. Miroudot and Cadestin (2017) show that many manufacturers not only purchase more services as inputs but also export services either bundled with their goods or on their own. This shift is not limited to large MNEs; small and medium-sized enterprises (SMEs) also leverage services to add value and build long-term customer relationships. For example, many manufac- turing rms develop support services (e.g., R & D and IT) in-house and even export these services to their foreign afliates (Kelle, 2013). As a result, services now account for a substantial share of manufactur- ing output and trade—over half of manufacturing export value once in-house service activities are in- cluded. Likewise, roughly one quarter to over one half of all employees in manufacturing rms work in service-related roles such as R & D, design, logistics, marketing or other support functions. Such evidence suggests that the traditional boundary between goods and services is blurring (see, e.g., Kowalski et al., 2015; Miroudot & Cadestin, 2017). Importantly for theory, services differ from goods in how value is added: Whereas goods production typically follows a linear “snake” sequence, service value chains often create value through a networked “spider” pattern, with multiple activities contributing concurrently (Baldwin & Venables, 2013). In addition, services play multiple roles within GVCs. Heuser and Mattoo (2017) and Miroudot and Cadestin (2017) identify four key roles: Services can form their own value chains; they link and coordinate dispersed production (via transport, logistics, and ICT); they act as outsourced inputs (such as R & D at an early stage or marketing at the end); and they can be produced in-house as support (e.g., IT and nance). These dimensions suggest that servicication can raise export quality through several channels: High-value services (e.g., engineering, quality control, and branding) can directly improve product quality, and supporting services (e.g., faster logistics or better design) reduce costs or delays, allowing rms to use higher-quality inputs. Empirical evidence supports such dynamics. Lodefalk (2013) reveals a signicant shift toward servicication among Swedish manufacturing rms. Chun et al. (2021) nd that Korean GVC participants—via trade and FDI—have restructured their domestic labour force to provide high-value- added headquarters services for their foreign manufacturing operations in proximity. Further, Reddy et al. (2022) show that servicication positively impacts GVC participation, with notable benets for SMEs and less technology-intensive rms. However, the effects can be nuanced: Du and Agbola (2022) discover that although domestic and aggregate servicication strengthens GVC upgrading, foreign servicication diminishes it. In sum, theory predicts that servicication and GVC participation are intertwined: Efcient service inputs help knit the chain together and support value creation at every stage. Finally, macro-level studies of servicication of manufacturing in GVCs include Lanz and Maurer (2015), Thangavelu et al. (2018), Sharma et al. (2024), and Díaz-Mora et al. (2022). To conclude and to integrate the aspects of GVC participation, services, and export quality, a multi- level story emerges. At the rm level, rm produc- tivity and capability heterogeneity determine which rms opt for exporting and how they perform. At the network level, governance choices—from contract structures to coordination routines—shape how in- puts ow and value is created. At the macro level, the policy and institutional environment inuences trade costs and the availability of foreign inputs. Services are the glue that connects these levels: ad- vances in transport, ICT, and nance services have enabled ne-sliced chains and allowed rms to spe- cialise (Heuser & Mattoo, 2017). Indeed, two thirds of recent growth in services value-added trade comes from services embedded in other sectors, under- lining their critical GVC role. Crucially, improved service efciency (e.g., better shipping or commu- nications) lowers coordination costs and thus can indirectly raise the quality of exported goods, even if the goods themselves are unchanged. In the context of this paper, this suggests that rms deeply integrated into GVCs—especially those with signicant foreign- sourced services content—have access to superior inputs and coordination capabilities, which should 166 ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 manifest in higher export quality. The empirical anal- ysis tests these theoretical linkages: namely, whether servicication of GVC participants enhances a rm’s export quality, consistent with a theory-driven view that spans IE and IB perspectives. 3 Empirical analysis 3.1 Data and descriptive statistics This analysis is based on the transaction-level trade data at the 8-digit level of the European Combined Nomenclature classication provided by the Statistical Ofce of the Republic of Slovenia (SURS) and on the detailed transaction-level trade in services data for a sample of Slovenian rms collected by the Bank of Slovenia. Furthermore, the following rm-level databases were also used and ultimately, via unique rm identiers, merged together: (i) the detailed database of rms’ nancial statements collected by the Agency of the Republic of Slovenia for Public Legal Records and Related Services (AJPES), which spans the population of Slovenian rms, and (ii) information on rms’ cross-border direct investment (FDI) inows and outows provided by the Bank of Slovenia. The examined period in this paper is 2008–2020, as this is the period for which data in services trade is available. Note that this analysis only includes manufacturing rms that are GVC participants, as it aims to examine the effects of the servicication of manufacturing. Hence, for this paper, data availability resulted in transaction-level trade data for 6957 Slovenian manufacturing rms, which amounted to 1,749,082 observations at the rm-product-destination level. As expected, the number of observations per year follows a generally increasing trend during the observed period (from 104,298 in 2008 to 172,621 in 2020), with decreases in the number of annual observations being related to the effects of the global nancial crisis in 2008, 2009, and 2012 and to the COVID-19 pandemic in 2020. Table 1 reports mean values and standard deviations of the dependent variable and control variables of the empirical model and some other relevant nancial indicators. Note that the descriptive statistics are presented separately for all cohorts of the type of goods that this paper accounts for and researches the effects for (i) all types of goods, (ii) intermediate goods, (iii) consumer goods, and (iv) capital goods. Overall, the table shows that rms exporting these different types of goods exhibit note- worthy differences in export values, quantities, and rm characteristics. Firms exporting capital goods command the highest export unit values, which suggests that these products tend to be more complex or technologically intensive, yet their exports—both nominally and in volume—are on average the lowest. Consumer goods, meanwhile, show higher average exports of products than intermediate or capital goods, but are shipped in lower volumes compared to intermediate goods. In terms of services trade, consumer and capital goods exporters generally engage in slightly higher services exports and imports than intermediate goods exporters, although their respective ratios of services exports and imports orientation is lower than that of intermediate goods exporters. Firms exporting consumer or capital goods also tend to be larger, more mature, and more likely to engage in outward or inward FDI. These patterns underscore the distinct nature of intermediate, consumer, and capital goods producers, highlighting differences in their product characteristics, reliance on services, and degree of global integration. As for the trade in services data, the Bank of Slove- nia collects information on the trade in services for a sample of Slovenian rms, which means that this dataset does not encompass the entire population of Slovenian rms, as is the case for other databases used for the purposes of this paper. Nonetheless, we still believe that the use of this dataset is applicable and advantageous, as it includes information on approx- imately 1100–1500 rms per year, with around a fth of them being manufacturing rms (for more detail see Table 2). Further, most existing rm-level GVC studies do not benet from having access to any data on trade in services—as such, literature focusing on rm-level trade in services is still rare, largely due to difculty in obtaining suitable and complete data. As a result, this study is among the few that integrate rm-level data on trade in services, thereby offering a more comprehensive understanding of rms’ inter- nationalisation activities and lling an important gap in the existing literature. 3.2 Methodology This paper analyses the effects of distinct inter- nationalisation activities of rms on two dependent variables: the rst is the rm’s product export qual- ity (at the rm-country-product level), proxied by the natural logarithm of its product export unit value, whereas the second is the natural logarithm value of the euro value of the rm’s exports of a given product to a given country. In essence, these two depen- dent variables are proxies for a rm’s export quality and export volume, respectively. The focus on these variables allows for a deeper understanding of how internationalisation affects not only the volume of ex- ports but also the quality aspect, which is increasingly recognised as critical in maintaining competitive- ness in global markets. The dual approach ensures a ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 167 Table 1. Descriptive statistics. Intermediate Consumer Capital Mean value (SD) Population goods goods goods Ln of export unit value 2.732 (1.860) 2.596 (1.932) 2.607 (1.606) 3.469 (1.700) Product exports (in million €) 0.123 (3.320) 0.109 (1.161) 0.151 (6.280) 0.077 (0.637) Product exports (in kg) 46,829 (1,064,807) 60,416 (1,298,129) 27,850 (333,804) 8,004 (87,075) Services exports (in million €) 1.690 (10.968) 1.699 (11.003) 2.069 (12.819) 2.049 (12.621) Services imports (in million €) 3.007 (16.751) 3.017 (16.801) 3.774 (19.578) 3.709 (19.327) Services exports orientation 0.067 (0.204) 0.067 (0.204) 0.045 (0.165) 0.052 (0.176) Services imports orientation 0.039 (0.067) 0.039 (0.067) 0.035 (0.058) 0.037 (0.063) Ln of total factor productivity 9.408 (0.567) 9.412 (0.562) 9.380 (0.559) 9.423 (0.557) Total sales (in million €) 9.131 (49.037) 9.551 (50.273) 13.359 (62.547) 14.106 (66.301) Ln of rm age 2.535 (0.848) 2.550 (0.840) 2.581 (0.841) 2.587 (0.835) Ln of rm size 2.530 (1.688) 2.580 (1.686) 2.841 (1.782) 2.909 (1.749) Outward foreign direct investment dummy 0.085 (0.278) 0.088 (0.284) 0.128 (0.334) 0.129 (0.336) Inward foreign direct investment dummy 0.108 (0.311) 0.111 (0.314) 0.131 (0.337) 0.140 (0.347) Debt to assets 0.594 (5.476) 0.589 (5.615) 0.569 (0.706) 0.540 (0.478) Ln of capital intensity 10.197 (1.491) 10.214 (1.477) 10.181 (1.456) 10.219 (1.424) Source: SURS, AJPES, Bank of Slovenia; authors’ own calculations. Table 2. Number of rms included in the trade in services database. Number of included Year Number of included rms manufacturing rms 2008 1314 167 2009 1411 238 2010 1372 232 2011 1323 241 2012 1310 247 2013 1135 220 2014 1167 225 2015 1164 240 2016 1228 258 2017 1253 263 2018 1305 269 2019 1458 295 2020 1579 347 Total 17019 3242 Source: Bank of Slovenia; authors’ own calculations. comprehensive evaluation of both tangible and qual- itative outcomes of internationalisation strategies. Further, to address potential endogeneity issues in the xed effects model, we follow the common empirical approach of employing lagged values of independent variables to mitigate biases arising from reverse causality by imposing a clearer temporal or- dering. Also, time-varying confounders and granular xed effects are included to help control for unob- served factors that vary over time and may inuence both the independent and dependent variables, thus mitigating potential endogeneity issues (in line with, e.g., Angrist & Pischke, 2009; Baltagi, 2008; Cameron & Trivedi, 2005; Stock & Watson, 2019; Verbeek, 2012; Wooldridge, 2010). This specication is well established in empirical studies of international trade and rm behaviour, as it systematically accounts for unobserved heterogeneity while exploiting the within-rm (or within-rm-country-product) varia- tion over time. By reducing the risk of simultaneity bias and omitted variable bias, the xed effects model with lagged regressors ultimately strengthens the credibility of the estimated relationships. Additionally, the comprehensive panel dataset with multiple cross-sectional and temporal dimensions further justies the use of a xed effects specication. By exploiting the repeated observations for each rm- country-product, the model can difference out any time-invariant unobserved heterogeneity and focus on within-unit variations over time. This structure allows for more precise estimation of how changes in 168 ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 a rm’s international involvement, such as services’ exports and imports or FDI, translate into changes in export unit values and export volumes. Further, because this paper’s specications are not dynamic in nature (i.e., they do not include a lagged dependent variable among the regressors), employing a xed effects estimator is both appropriate and sufcient to control for time-invariant unobserved heterogene- ity (see, e.g., Baltagi, 2008; Greene, 2012; Verbeek, 2012; Wooldridge, 2010). Although the generalised method of moments is powerful for dynamic panel data settings, it depends on a more complex set of assumptions—such as valid instruments (see, e.g., Blundell & Bond, 1998) and the absence of second- order autocorrelation (see, e.g., Arellano, 2003; Arel- lano & Bond, 1991; Blundell & Bond, 1998)—and can suffer from instrument proliferation when the panel is large in both the cross-sectional and temporal di- mensions (see, e.g., Bowsher, 2002; Roodman, 2009). Consequently, the xed effects estimation is a more straightforward and transparent approach in this context, mitigating endogeneity primarily through lagged regressors and delivering credible estimates without the added complications of instrument selec- tion and validation. As stated above, in its aim to examine the ef- fects of the servicication of manufacturing, this paper focuses exclusively on GVC-participating man- ufacturing rms. The applied approach to identify GVC-participating rms follows Stemberger and Zajc Kejžar (2025) by rening the usual “importer plus ex- porter equals GVC participant” logic through explic- itly capturing both upstream (forward) and down- stream (backward) links. First, it quanties a rm’s backward participation through the share of foreign inputs in its exports, reecting how much of its ex- ported goods rely on imported content. Second, it measures forward participation by identifying the share of a rm’s own value added in intermediate exports that other producers use in subsequent stages. These two ratios sum to form the rm-level GVC participation score. Finally, to distinguish genuine engagement from minimal foreign transactions, the method sets a threshold of 10% (in line with, e.g., Cie´ slik et al., 2019) of combined backward and for- ward participation above which rms are classied as true GVC participants. As the paper tests the effects on two dependent variables, it follows a distinct specication for each variable of interest. The rst specication is a xed ef- fects panel regression estimation, where export qual- ity (lnunitvalue i jkt ) is expressed as a function of the lagged value of a rm’s services’ exports and imports orientation (both total and at a destination-level) and the lagged status of a rm’s outward and inward FDI. Moreover, the lags of the following controlling vari- ables are included: (i) rm productivity, dened as the natural logarithm of the estimated total factor pro- ductivity (TFP; lnTFP it ), estimated using the value- added-based approach of Ackerberg et al. (2015)—as suggested by the authors and following Manjón and Mañez (2016)—where value added is regressed on xed assets, rm age, material costs, and number of employees, with all variables included in their respec- tive natural logarithm values. In this framework, TFP reects the portion of a rm’s value added that is not explained by observed input usage, capturing rm- specic efciency or technology differences, (ii) rm size, dened as the natural logarithm of the number of employees (lnsize it ), (iii) rm age, dened as the natural logarithm of the years since the rm’s incor- poration (lnage it ), (iv) capital intensity, dened as the natural logarithm of the ratio of xed assets to the number of employees (lnkintensity it ), and (v) a rm’s ratio of debt to assets (da it ). Further, the specication includes high-dimensional time-varying destination xed effects k jt to control for unobserved heterogene- ity and capture country-level macroeconomic condi- tions and rm-product-destination ! i jk xed effects to absorb any time-invariant unobserved heterogeneity specic to each rm-product-destination relationship, thereby improving the precision of the estimates. Fi- nally, the estimation takes advantage of clustered standard errors at the rm-product-destination level, which enhances reliability by adjusting for intra- group correlation, leading to more robust and credible statistical inferences. The actual regression model for the rst specication is as follows: lnunitvalue i jkt Db 0 Cb 1 exs_orient i;t–1 Cb 2 ims_orient i;t–1 Cb 3 exs_orient_cntry i j;t–1 Cb 4 ims_orient_cntry i j;t–1 Cb 5 doutFDI i;t–1 Cb 6 dinFDI i;t–1 Cb 7 lnTFP i;t–1 Cb 8 lnage i;t–1 Cb 9 lnsize i;t–1 Cb 10 lnkintensity i;t–1 Cb 11 da i;t–1 Cb 12 k jt Cb 13 ! i jk Cm i jkt whereby the rm’s unit value of exports is computed as the ratio of a rm’s exports of a certain product at the 8-digit Combined Nomenclature (CN8) level to a certain country and the net weight (in kilograms) of this exported product to this country: lnunitvalue i jkt D ln exproduct i jkt net_weight i jkt ! and the import and export of services orientation (both total and at destination-level) are respectively computed as the ratio between either the imports or ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 169 exports of services and the rm’s total sales: exs_orient it D exs it sales it ims_orient it D ims it sales it exs_orient_cntry i jt D exs_cntry i jt sales it ims_orient_cntry i jt D ims_cntry i jt sales it The second specication is also a xed effects panel regression estimation with clustered standard errors at the rm-product-destination level to adjust for both heteroskedasticity and within-cluster correlation. Here, the natural logarithm value of the euro value of a rm’s exports of a certain product at the CN8 product level to a certain country (lnexports i jkt ) is expressed as a function of the lagged natural logarithm value of a rm’s services’ exports and imports (both total and at country level) and the lagged status of a rm’s outward and inward FDI. Moreover, the lags of the same controlling variables and xed effects as in the rst specication are included. The actual regression model for the second specication is presented below. lnexports i jkt Db 0 Cb 1 lnims i;t–1 Cb 2 lnexs i;t–1 Cb 3 lnims_cntry i j;t–1 Cb 4 lnexs_cntry i j;t–1 Cb 5 doutFDI i;t–1 Cb 6 dinFDI i;t–1 Cb 7 lnTFP i;t–1 Cb 8 lnage i;t–1 Cb 9 lnsize i;t–1 Cb 10 lnkintensity i;t–1 Cb 11 da i;t–1 Cb 12 k jt Cb 13 ! i jk Cm i jkt After performing the above analysis, we also exam- ined these same specications for each different type of exported goods (i.e., intermediate, 2 consumer, 3 and capital 4 goods). 4 Empirical results This section presents econometric results derived from estimating the effects of trade in services and FDI ows on export product quality and volume of manufacturing rms participating in GVCs. This is due to this paper aiming to thoroughly analyse the role trade in services plays in the enhancement of a rm’s export product quality and volume. The focus on Slovenian manufacturing GVC participants between 2008 and 2020 provides an intriguing case study given the unique position of Slovenia as a small open economy deeply embedded in GVCs. The results can serve as a microcosm for understanding similar dynamics in other small, export-oriented economies. Table 3 provides the results of the estimation of a manufacturing rm’s internationalisation activities on its export quality for the case of Slovenian rms in the 2008–2020 period. The results in the rst col- umn refer to any type of goods, whereas the following three columns each include results differentiated by the type of goods. Clearly, by far the largest and most signicant effect on rm export quality can be attributed to its services imports orientation at coun- try level, as the corresponding coefcient is highly signicant in all but the capital goods case. More specically, this implies that a one percentage point increase in the ratio of the rm’s services import ori- entation at destination level is associated with a 1.15% increase in its export quality when all types of goods are considered. The corresponding effect is 1.03% for intermediate goods (hereafter referred to as supply chain trade) and 2.72% for consumer goods (hereafter referred to as nal goods trade). Furthermore, the ex- port quality of rms with outward FDI is 2.8% greater than in comparable rms without outward FDI when all types of goods are considered (and 4.1% greater in the case of supply chain trade), with other factors held constant. Opposingly, the export quality of rms with inward FDI is 1.3% greater than in comparable rms without inward FDI when all types of goods are considered (and 4.1% greater in the case of nal goods trade), with other factors held constant. From Table 3, one can clearly see that the type of exported goods matters for the effects on a rm’s export quality; whereas the effect of the orientation of services imports at country-level is signicant and positive for both supply chain trade and nal goods trade, a distinction is drawn in the type of the FDI ow, as the positive outward FDI effect is signicant for the case of supply chain trade, and the positive inward FDI effect is signicant for nal goods trade. Lastly, for the exported capital goods, none of the analysed internationalisation strategies has a signif- icant effect on the rm’s export quality. This result likely reects the structural characteristics of capital goods markets, where demand is often driven by long-term projects or government policies, and such goods are often bundled with engineering services and sold in turnkey projects, making them less re- sponsive to short-term changes in internationalisation activities such as services trade or FDI. Nonetheless, 2 Dened as the following categories of goods as per BEC Rev. 4: 111, 121, 21, 22, 31, 322, 42, and 53. 3 Dened as the following categories of goods as per BEC Rev. 4: 112, 122, 522, and 6. 4 Dened as the following categories of goods as per BEC Rev. 4: 41 and 521. 170 ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 Table 3. Effects of a rm’s internationalisation activities on its export quality in the 2008–2020 period. 5 All types of goods Intermediate goods Consumer goods Capital goods Total services’ exports 0.008 0.049 0.085 0.129 (0.073) (0.089) (0.172) (0.221) Total services’ imports orientation 0.013 0.025 0.033 0.024 (0.110) (0.142) (0.207) (0.369) Country-level services’ exports orientation 0.122 0.136 0.498 0.180 (0.210) (0.243) (0.613) (0.561) Country-level services’ imports orientation 1.147*** 1.031** 2.716*** 0.274 (0.414) (0.503) (1.052) (0.991) Outward FDI dummy 0.028*** 0.040*** 0.010 0.009 (0.007) (0.010) (0.013) (0.029) Inward FDI dummy 0.013** 0.009 0.040*** 0.005 (0.006) (0.009) (0.009) (0.020) Number of observations 572,786 352,384 137,248 81,994 Number of clusters 192,551 118,203 42,661 31,408 Clustered SE rm-product- rm-product- rm-product- rm-product- destination level destination level destination level destination level Covariates lnTFP , lnage, lnsize, lnkintensity, and da Fixed effects rm-product-destination and destination-year xed effects Standard errors in the parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. Source: SURS, AJPES, Bank of Slovenia; authors’ own calculations. Table 4. Effects of a rm’s internationalisation activities on its export volume in the 2008–2020 period. 6 All types of goods Intermediate goods Consumer goods Capital goods Ln of services exports 0.022*** 0.019** 0.015 0.019 (0.007) (0.009) (0.018) (0.019) Ln of services imports 0.011 0.018 0.022 0.040 (0.012) (0.014) (0.033) (0.030) Ln of services exports at country level 0.013*** 0.010* 0.005 0.018** (0.004) (0.006) (0.009) (0.009) Ln of services imports at country level 0.005 0.013*** 0.006 0.022** (0.004) (0.005) (0.010) (0.011) Outward FDI dummy 0.086** 0.028 0.190** 0.069 (0.041) (0.051) (0.087) (0.118) Inward FDI dummy 0.080*** 0.024 0.126** 0.096 (0.026) (0.034) (0.052) (0.066) Number of observations 201,425 125,651 42,778 32,555 Number of clusters 74,900 46,643 14,566 13,559 Clustered SE rm-product- rm-product- rm-product- rm-product- destination level destination level destination level destination level Covariates lnTFP , lnage, lnsize, lnkintensity, and da Fixed effects rm-product-destination and destination-year xed effects Standard errors in the parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. Source: SURS, AJPES, Bank of Slovenia; authors’ own calculations. as Table 1 shows, capital goods have substantially higher unit values than other goods, which is consis- tent with them being produced by a few specialised exporters (see, e.g., Eaton & Kortum, 2001; Mutreja et al., 2018). In line with the literature (e.g., Lian et al., 2020), we conclude that the relatively high price of capital goods and their concentrated supply imply that export outcomes for these goods depend on dif- ferent channels. As such, the high unit values of capital goods warrant special attention when assess- ing policy implications. Table 4 provides the results of the estimation of a manufacturing rm’s internationalisation activities on its export volume for the case of Slovenian rms in 5 The F statistic is not reported due to the inclusion of clustered standard errors and high-dimensional xed effects, which reduce the effective degrees of freedom. Adjusted R 2 is not reported due to the inclusion of high-dimensional xed effects, which absorb most of the systematic variation across products and destination-year combinations. Here, the focus is on signicance and magnitude of estimated coefcients rather than model t. 6 The F statistic is not reported due to the inclusion of clustered standard errors and high-dimensional xed effects, which reduce the effective degrees of freedom. Adjusted R 2 is not reported due to the inclusion of high-dimensional xed effects, which absorb most of the systematic variation across products and destination-year combinations. Here, the focus is on signicance and magnitude of estimated coefcients rather than model t. ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 171 the 2008–2020 period. As in Table 3, the rst column corresponds to the scenario that considers all types of goods, whereas the following three columns each give results per good type. Generally, the complementarity of the value of a rm’s exports of goods and the value of its services exports is conrmed for both total and destination-level cases. Nonetheless, these results are not as straightforward as in the export quality case, as the effects are now signicant for both imports and exports of services (and not only for imports as above), which suggests that these transactions have a more profound effect for the rm’s export volume than on its export quality. More precisely, the results show that a one percentage point increase in the rm’s services exports is related to a 0.022% increase in its export volume when all types of goods are considered (the effect is 0.019% for supply chain trade; when one differentiates by country, the effect is 0.013% for all types of goods, 0.010% for supply chain trade. and 0.018% for capital goods). On the other hand, for ser- vices imports at country level, the effect is 0.013% for supply chain trade and 0.022% for capital goods. In addition, the export volume of rms with outward FDI is 9.0% greater than in comparable rms without outward FDI when all types of goods are considered (and 20.9% in case of nal goods trade), with other factors held constant. Opposingly, the export volume of rms with inward FDI is 7.7% smaller than in comparable rms without inward FDI when all types of goods are considered (and 11.8% for nal goods trade), with other factors held constant. In line with expectations, Table 4 also shows that the effect is distinct between the good types, as for capital goods and especially for supply chain trade, services imports and exports have a signicant and largely positive effect on the volume of a rm’s ex- ports of these goods, whereas for nal goods trade, services imports and exports have no observed effect. Rather, for the latter, the presence of FDI ows has a signicant impact, with outward FDI having positive effects and inward FDI having negative effects on the rm’s export volume. This relationship is present and conrmed also when there is no distinction between the types of goods. Comparing the results from the two specications yields several key insights. First, the determinants of export quality and volume are not only distinct but of- ten show diverging patterns depending on the type of goods under analysis. For instance, the signicance of services imports at the country level for export quality highlights the importance of accessing high-quality service inputs in enhancing the qualitative aspect of exports. Conversely, for export volume, the comple- mentarity between goods and services trade is more pronounced, as seen in the signicant effects of both services imports and exports in total as well as at the destination level. The role of FDI also diverges across the two out- comes. Outward FDI consistently exhibits a positive impact on both export quality and volume (at least in the case of all types of goods), which underscores its role in facilitating technology transfer and mar- ket access. However, the presence of inward FDI produces mixed effects—enhancing export quality in some cases while reducing export volume, partic- ularly for nal goods trade. This suggests that the nature of inward FDI, whether it entails vertical or horizontal integration, may play a critical role in de- termining its impact. Finally, the nuanced differences across good types—supply chain trade, nal goods trade, and capital goods—further highlight the complexity of the relationship between internationalisation strategies and export outcomes. In fact, the quality and volume of supply chain trade (and to a slightly lesser extent also nal goods trade) benet substantially from services trade and outward FDI, while capital goods show minimal responsiveness, which reects their unique production and market dynamics. Policymakers and rms should consider these distinctions when designing strategies to integrate into and benet from both partaking in GVCs and trading in services. These ndings underline the need for tailored approaches that account for the specic contexts of product type, market destination, and rm-level capabilities. 5 Conclusion This paper provides a comprehensive analysis of the role that services trade and FDI play in shaping export quality and volume among Slovenian man- ufacturing rms engaged in GVCs between 2008 and 2020. By integrating rm-level data, it bridges important gaps in understanding the dynamics of ser- vicication in manufacturing and the nuanced effects of internationalisation on export performance. As global trade increasingly shifts toward knowledge- intensive and service-oriented value creation, the ndings underscore the transformative potential of services trade and FDI in driving upgrading within GVC-integrated manufacturing. This study contributes to multiple streams of liter- ature. First, within IE, it advances the understanding of how services imports, particularly at the country level, enhance export quality by providing rms with access to high-value intermediate inputs. It comple- ments Melitz-type models by showing that quality gains also stem from services-driven efciency, not just goods-based trade liberalisation. This aligns with 172 ECONOMIC AND BUSINESS REVIEW 2025;27:160–174 the broader understanding that services trade facili- tates both the efciency and sophistication required to compete in global markets and complements goods trade in expanding a rm’s international footprint. Second, for IB theory, the paper reinforces the role of rm-level strategic choices—especially outward FDI and servicication—as mechanisms of upgrading within GVCs. It highlights how rms adapt gover- nance structures and resource congurations to access knowledge, coordination capabilities, and markets, thus echoing the governance adaptation framework of Kano et al. (2022). Third, for the industrial mar- keting and service marketing literatures, the ndings underscore that services are not merely supplemen- tary to product strategies but are increasingly central to value creation in manufacturing. Servicication enables rms to bundle high-quality services with goods, differentiate offerings, and enhance customer relationships—especially for nal goods and supply chain trade. This paper contributes to academic, managerial, and policy debates. Theoretically, it demonstrates the interconnectedness of goods and services trade within GVCs, emphasising the growing role of ser- vices in modern manufacturing processes. For man- agers, the results suggest that investing in service capabilities can signicantly enhance product qual- ity and global competitiveness. This is particularly relevant for MNEs pursuing servicication strategies: developing high-value support functions close to pro- duction sites and leveraging outward FDI to scale these capabilities internationally can drive upgrad- ing. Additionally, managers should evaluate inward FDI not only in terms of capital inow but also in light of its implications for value chain positioning, as its effects are not necessarily straightforward. From a policy perspective, the ndings highlight the importance of liberalised access to high-quality service inputs. Strategic promotion of outward FDI by domestic rms can help transfer technology and market insights back into the home country, improving export outcomes. However, inward FDI should be selectively managed to maximise local spillovers, such as through targeted R & D incentives, coinnovation frameworks, and performance-based investment conditions. Another key implication concerns participation in GVCs. Governments should avoid protectionist policies that limit rms’ access to global intermediates, particularly services. 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