GVC embeddedness and innovation performance – an analysis across 28 European countries

Alissa Lysanne van Zijl (Erasmus Universiteit Rotterdam, Rotterdam, Netherlands)
Ferry Koster (Erasmus Universiteit Rotterdam, Rotterdam, Netherlands)

European Journal of Management Studies

ISSN: 2183-4172

Article publication date: 27 June 2024

Issue publication date: 12 September 2024

302

Abstract

Purpose

This study has the following aims. First, it tries to clarify the relationship between global value chain (GVC) participation and innovation performance. To date, research generated mixed results. Secondly, it explores a novel way of investigating GVC participation by focusing on the sectoral level in which organizations operate using the concept of GVC embeddedness, which has not been investigated to date.

Design/methodology/approach

Data from two sources are combined. The European Company Survey of 2019 provides data about the innovation performance of organizations. These data include measures about production and process. At the sectoral level, these company-level data are combined with data from the Trade in Value Added from the Organization for Economic Cooperation and Development. In total, more than 20,000 companies were investigated, which operate in 15 sectors in 28 countries. This three-level structure is analysed using multilevel ordered probit analyses.

Findings

The results show that forward participation and backward participation at the sectoral level are positively related to product innovation and process innovation. The probability of not being engaged in innovation is reduced through GVC embeddedness. And, the probability of generating incremental and radical innovations (both regarding products and processes) increases if GVC participating at the sectoral level is higher.

Originality/value

This study is the first to relate sectoral GVC embeddedness to the innovation performance of organizations.

Keywords

Citation

van Zijl, A.L. and Koster, F. (2024), "GVC embeddedness and innovation performance – an analysis across 28 European countries", European Journal of Management Studies , Vol. 29 No. 2, pp. 195-209. https://doi.org/10.1108/EJMS-12-2023-0091

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Alissa Lysanne van Zijl and Ferry Koster

License

Published in European Journal of Management Studies. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode


Introduction

Global value chains (GVCs) consist of linkages between organizations adding value to a good or service while operating in different countries (Gereffi et al., 2021). These links are, for example, established through outsourcing of (previously internalized) production processes or via collaboration with suppliers and buyers in other countries (Contractor et al., 2010). GVC participation is examined at different levels, such as nations, sectors and organizations (Antras and Chor, 2022), each reflecting their position within the global economy. This article combines the sectoral and the organizational level. In accordance with Hu et al. (2022), the term GVC embeddedness is used to refer to this concept. In the current study, GVC embeddedness reflects the openness of the sector in relation to global production flows. As such, this study employs a “meso-level” approach by including the sectoral level, which is situated between micro-analysis of specific organizations and workplaces and macro-analysis of national economies (Ponte et al., 2019). Regarding the impact of GVCs, two recent reviews of GVC research (Kano et al., 2020; McWilliam et al., 2020) discuss theoretical and empirical research, emphasizing the need for investigating the link between GVCs and the innovation performance of organizations.

While it has been proposed that GVC participation and innovation performance are related to each other, empirical research into this area remained surprisingly scarce (Elshaarawy and Ezzat, 2022). As Elshaarawy and Ezzat (2022) note, the literature connecting GVC participation and organizational innovation to date is largely theoretical. The only exceptions of empirical investigations of innovation that they mention are Dang and Dang (2020) and Tajoli and Felice (2018). An additional literature search does generate some other examples, such as Turkina and Van Assche (2018), who investigate the link between GVC participation in a number of economic clusters (e.g. Aerospace, Biomed and IT) and innovation and Lema et al. (2019) and Yang et al. (2020), who both investigate the link between GVCs and innovation performance in developing countries. Other studies investigated related topics such as the use of technology and knowledge diffusion (e.g. Brancati et al., 2021), but these do not address the topic of innovation performance. In that sense, Van Assche's (2017) remark of the field’s situation still applies: “A question that has hitherto received surprisingly little scholarly attention, however, is how the globalization of value chains affects a firm’s innovation capabilities” (p. 739). Hence, understanding the relationship between GVC participation and innovation performance requires additional empirical research.

The need to investigate the link between GVCs and innovation performance is further stressed, as it is also debated whether there is a positive or a negative link between GVCs and the innovation performance of organizations as well as how these relationships are explained. The literature mainly emphasizes positive effects of GVCs, and empirical results also point in that direction (Van Assche, 2017; Elshaarawy and Ezzat, 2022). Offshoring and outsourcing of activities imply that organizations can use their remaining resources to innovate and that organizations gain by upgrading their technology and knowledge via external linkages (Bardhan and Jaffee, 2005; Phene and Almeida, 2008; Pietrobelli and Rabellotti, 2011). While others have countered these arguments stating that GVC participation constrains the innovation performance of organizations due to a weakened financial position of organizations or production processes becoming dispersed and fractionalized (Steinberg et al., 2017; Ambos et al., 2021; Baum et al., 2022; Elshaarawy and Ezzat, 2022), the gains seem to outweigh these restrictions.

The question that the present study therefore aims to answer is how the GVC participation of an organization’s sector of operation relates to its innovation performance. This study is positioned as follows in the GVC research domain. The study distinguishes itself from studies that remain at the macro level and that generated insights into (trends) in GVCs, GVC governance and outcomes such as inequality and innovation at the national level (e.g. Tajoli and Felice, 2018). In addition, it goes beyond the focus on a specific sector (e.g. Turkina and Van Assche, 2018) as it includes several sectors in a single analysis. And, finally, it investigates these sectors and organizations across several countries.

Theoretically, the present study integrates insights derived from GVC research with organizational theories of innovation performance. Empirically, it does so by combining Trade in Added Value (TiVA) data at the sectoral level, which are available from the OECD, with company-level data collected by the European Company Survey (ECS) 2019. These data allowed to include data about 15 sectors in 28 European countries. To get a good understanding of how GVCs relate to innovation performance, backward and forward participation in GVCs are investigated (OECD, 2021).

Theory and hypotheses

It is commonly believed that there is a link between GVC participation and the innovativeness of organizations. Combining insights from GVC research with theories of innovation performance sheds a light on these diverging outcomes. McWilliam et al.’s (2020) shows that research explaining how GVC participation affects the innovation performance of organizations relies on theories of internal organization such as the resource based view (RBV), the knowledge-based view (KBV) and the Dynamic Capabilities Approach (DCA), as well as theories of inter-organizational relationships such as the relational view (RV), the transaction cost approach (TCA) and network theories (Crossan and Apaydin, 2010; Bai et al., 2020; Szambelan et al., 2020). Theoretically, these approaches differ. RBV emphasises the role of resources in explaining innovation, whereas the KBV argues that knowledge is the main resource and the DCA implies that it is not so much the resources per se that matter but the way in which organizations are capable to recombine them. RV, TCA and network theories focus much more on how organizations get access to resources, namely by building relations (the relational view and networks) or via formal control (TCA). While these approaches differ, they also complement each other in several respects, for example, by emphasizing that innovation performance requires that organizations have unique resources, develop, access and apply knowledge and are capable of seizing opportunities. All these mechanisms rely on internal organization or are structured via external ties. Hence, the relationship between GVC participation and innovative performance can be explained with theoretical frameworks based on resources, knowledge and capabilities of organizations.

GVC embeddedness is conceptualized as the external environment of organizations, which is of vital importance here for several reasons. First, characteristics of the external environment define the need for having unique resources, knowledge and capabilities (Pandza and Thorpe, 2009; Fawcett et al., 2011). Secondly, the external environment provides opportunities to access, develop and exchange them (Mol, 2001; Holmqvist, 2003; Ruiz-Ortega et al., 2023). Based on the assumption that GVCs constitute a core part of the environment in which organizations operate, the connection between organizations and their environment can be more direct (if they participate directly in a GVC) or indirect (if the organization belongs to a sector with a high incidence of GVC participation). In other words, organizations may be affected by GVCs directly as well as indirectly, as GVCs affect the wider environment in which organizations operate.

The sector in which organizations operate is a particular important point of reference compared to the more general level of the country (Ciani and Gregori, 2022). In several studies, the sectoral level received attention. In these studies, the direct and indirect effects of sectors are examined by taking into account the structure of the sector and the dynamics taking place within sectors. These studies provide explanations of sectoral differences regarding innovation performance (Wakelin, 2001). Innovations are believed to diffuse within sectors (Cai et al., 2022). Kitsos et al. (2023) argue that regional embeddedness can create positive externalities for all organizations belonging to the same region. Similarly, organizations can gain from the innovation performance of other organizations in the sector (Moreira et al., 2018). Thus, the sector to which organizations belong may provide important resources and opportunities for learning. Nevertheless, to date, this assumption has not been tested by combining the sectoral level (the environment of organizations) to the organizational level (the unit where innovations take place). To conduct such an analysis, available data about the sectoral level are combined with organizational level data. What is novel here is that the sectoral level constitutes an environment or context in which the organizations operate and are hence interpreted in terms of sectoral embeddedness.

The relationship between GVC embeddedness and innovation performance

Whereas prior research into GVC participation and innovation performance generated some mixed outcomes (Ambos et al., 2021), there is more evidence for a positive relationship than a negative relationship. The main reason for this is that those who emphasise the negative side of GVCs focus on the extremes. They do so by arguing that production processes become too dispersed that organizations become more focused on efficiency instead of innovation or that innovation processes become too complex or interdependent (Yamin, 2011; Narula, 2014; Steinberg et al., 2017; Ambos et al., 2021). Nevertheless, these seem to be exceptional cases. Whereas the development of resources, knowledge and capabilities and the management of external resources may require additional attention, it is assumed here that on average organizations are capable of handling these complexities. A positive relationship between GVC embeddedness and innovation is explained along several lines of argumentation. One of the main arguments is that GVC participation leads to increased competition, which translated into increasing the need for innovation (Schmitz, 2005). In addition to that, research shows that especially backward participation (making use of foreign inputs) can have a positive impact on innovation since foreign knowledge is incorporated in the processes of organizations (Eissa and Zaki, 2023), which may serve as a form of social capital, which in turn contributes to the innovation performance of organizations (Inkinen et al., 2015). More generally, the existence of GVCs means that production processes are dispersed across countries, which in turn allows for the exchange of knowledge between organizations operating in different countries (Tajoli and Felice, 2018). Furthermore, GVCs can put pressure on participants to increase their standards to fulfil demand (OECD, 2013). GVCs may therefore also spur learning and technological upgrading (Schmitz and Knorringa, 2000; Sampath and Vallejo, 2018). Research into the impact of offshoring on innovation performance also shows that organizations can gain in that way (Nieto and Rodriguez, 2011; Thakur-Wernz and Wernz, 2022). Having said that, along with opportunities to acquire improved technologies and know-how, GVC participation can also help companies to enhance their trade networks (Ernst and Kim, 2002; Gereffi, 2014; Ndubuisi and Owusu, 2021; Reddy et al., 2021). This means that organizations participating in GVCs develop their resources, knowledge and capabilities internally to deal with their external environment or that they develop external relationships in such a way that mutual learning and information sharing are enabled.

These arguments are integrated as follows. It is expected that the negative effects or hindrances apply to the organizations that have a low level of GVC participation and thus have yet to develop the necessary capabilities to participate and to reap the benefits from participation. The organizations on the other hand of the spectrum have developed these capabilities and are expected to generate more returns in terms of innovation performance.

H1.

There is a positive relationship between GVC embeddedness (sectoral level GVC participation) and the product and process innovation performance of organizations.

Data and analytic strategy

Data sources

Data from different sources are combined to investigate the link between GVCs and innovation performance of organizations.

Organizational level

To assess the outcomes at the organizational level, data from the European Company Survey (ECS; https://www.eurofound.europa.eu/en/european-company-survey-2019) are analysed. The ECS is a survey to gather data from companies with at least ten employees in 28 European countries (EU member states and the UK). Interviewees are the managers who are responsible for the human resources in those companies. One of the aims of the ECS is to provide insights into the practices of these establishments in a harmonized manner. The fourth round was collected in 2019. The 2019 edition of the ECS contains information about 21,869 establishments in 28 European countries (27 EU member states and the UK) in total. The analyses for product innovation are conducted on a dataset with 21,744 companies and the analyses of process innovation include 21,646 establishments. To make sure that a large share of the organizations is included in the analyses, for the independent categorical variables, the categories “missing” or “not applicable” are also presented.

Sectoral level: GVC embeddedness

The company data are combined with data from the TiVA dataset provided by the OECD (https://www.oecd.org/sti/ind/measuring-trade-in-value-added.htm), which contains information about a large number of GVC participation indicators (OECD, 2021). The TiVA indicators are available for different levels of analysis (e.g. sector and country) and for different years. For the present analyses, the data at the sectoral level for the 28 ECS-countries are of interest. The ECS contains information about the sector in which an establishment operates. The following 15 sectors are included (Table 1).

Measures

Innovation performance

The questions measuring innovation performance are:

  1. Since 2016, has this establishment introduced any new or significantly changed products or services? [Product innovation]

  2. Since 2016, has this establishment introduced any new or significantly changed processes, either for producing goods or supplying services? [Process innovation]

The answer categories of the two questions are the same. Respondents could choose between “no”, “yes, new to the organization” and “yes, new to the market”. In this paper, these response categories are labelled “no innovation”, “incremental innovation” and “radical innovation”. As such, they reflect an ordering in the level of innovativeness. Nevertheless, since the distances between the categories are unknown, they are treated as an ordinal scale and analysed accordingly.

GVC embeddedness: forward and backward participation at the sectoral level

As explained in the theoretical section, this study aims at understanding the link between GVC participation at the sectoral level. By combining this information with organizational level data, it is possible to interpret this as the extent to which an organization is embedded in a GVC. The TiVA-data at the sectoral level have been used in other studies (see, for example, Anzolin and Benassi, 2024). However, the studies have not combined these sectoral-level data with company-level data.

Several GVC indicators are included in the analyses. Backward participation (exgr_fvash) indicates that the sector is mainly a buyer of external inputs. Forward participation (exgr_dvafcsh) indicates whether the sector is a seller. Domestic added value (exgr_dvafcsh) is also included as an indicator of how much the sector adds to exports via domestic inputs. The natural logarithms of these GVC indicators are added to the models. Using these transformations is often applied in research investigating GVC participation (Hu et al., 2021). Since some sectors have a score of 0, the logarithms are calculated after a 1 is added to the raw score.

Knowledge-intensive work practices

To measure the knowledge-intensity of the companies, a variable measuring knowledge-intensive work practices (KIWPs) is constructed. Based on DCA and organizational learning theories, Koster (2022) developed this construct spanning the ability to adapt to changing circumstances, to enhance learning and the use of technology. With the ECS, these aspects can be measured with the following six items:

  1. For how many employees in this establishment does their job include finding solutions to unfamiliar problems they are confronted with? Your best estimate is good enough. [Unfamiliar problems].

  2. For how many employees in this establishment does their job include independently organising their own time and scheduling their own tasks? Your best estimate is good enough. [Scheduling].

  3. How many employees in this establishment are in jobs that require continuous training? Your best estimate is good enough. [Continuous training].

  4. In 2018, how many employees in this establishment participated in training sessions on the establishment premises or at other locations during paid working time? Your best estimate is good enough. [Participation in training].

  5. In 2018, how many employees in this establishment have received on-the-job training or other forms of direct instruction in the workplace from more experienced colleagues? Your best estimate is good enough. [On-the-job training].

  6. How many employees in this establishment use personal computers or laptops to carry out their daily tasks? Your best estimate is good enough. [Computers].

For each of these questions, respondents indicate on a seven-point scale to how many of the employees it applies. The categories of these items are as follows: none at all, less than 20%, 20–39%, 40–59%, 60–79%, 80–99% and all.

A reliability analysis of these items resulted in a Cronbach's alpha of 0.71. Therefore, it is concluded that the items can be combined into a single scale. This scale is created by adding the scores on the items. These scores are divided by 6. Hence the final scale also runs from 1 to 7.

Digitalization

Three variables are included to measure the level of digitalization of the establishments. Each of these variables was measured with a 0 indicating that these technologies are not present and a 1 that they are.

  1. The variable robots is measured with the question “Robots are programmable machines that are capable of carrying out a complex series of actions automatically, which may include the interaction with people. Does this establishment use robots?”.

  2. The variable DA production is measured with the question “Does this establishment use data analytics (Data analytics refers to the use of digital tools for analysing data collected at this establishment or from other sources) to improve the processes of production or service delivery?”.

  3. The variable DA monitoring is measured with the question “Does this establishment use data analytics to monitor employee performance?”.

These dummy variables are separately added to the analyses. Exploratory factor analyses did not yield an underlying dimension of these variables. Hence, they indicate different aspects of digitalization of the company.

Organizational size

The variable organizational size is measured by asking the question “Approximately how many people work in this establishment?”. This variable has three categories, namely 10–49, 50–249 and 250 and more. The descriptive statistics of the data are presented in Table 2.

Analyses

The theoretical framework developed in this paper relies on the notion that organizations are embedded in a context – the sector to which they belong – and the structure and dynamics taking place within these sectors – due to how they are placed within a GVC – is expected to affect the innovation performance of organizations. To investigate this, a dataset is constructed in which characteristics of sectors are linked with characteristics of organizations. Moreover, the empirical data are available for 28 countries. Finally, the dependent variables investigated in this study have three levels, namely 0, 1 and 2, which stand for no innovation, incremental innovation and radical innovation. Hence, the dataset consists of three levels (organizations nested in sectors, which are nested in countries), and the responses are ordered categories. To fully take advantage of the information at hand, a multilevel ordered probit analysis is performed with STATA version 18 (using MEOPROBIT). The probit model uses the independent variables to predict a latent variable based on the ordered responses (Liddell and Kruschke, 2018). To simplify the interpretation, the outcomes of the multilevel probit analysis are used to calculate marginal effects. These marginal effects show the probability of a change in the dependent variable with a change in one unit of the independent variable, given that all other variables are set to their mean value. The parameters in the tables can therefore be interpreted as the probability of the occurrence of the three possible outcomes (e.g. no innovation, incremental innovation and radical innovation). Because forward and backward participation are correlated (the correlation coefficient is 0.39 at the sectoral level), they are added separately to the models.

Results

Tables 3–6 present the results of the multilevel probit analyses for the two dependent variables (product innovation and process innovation).

Tables 3 and 5 show that product innovation is explained by backward participation and forward participation at the sectoral level. First, an increase in backward participation reduces the probability that organizations in the sectors are not innovative with 6.2% (the predicted probability is 0.062; p < 0.001) and forward participation of the sector reduces this probability with 1.8% (p < 0.001). The probabilities that organizations are engaged in incremental innovations increase with 2.3 and 0.7% for a unit increase in backward participation and forward participation, respectively. And, finally, the probability of radical innovations increases with 3.6% (backward participation) and 1.1% (forward participation); the level of GVC participation at the sectoral level increases with 1 unit.

Tables 4 and 6 show the results for process innovation. Backward participation and forward participation at the sectoral level also explain these kinds of innovation. A unit increase in backward participation at the sectoral level reduces the probability that organizations in the sectors are not innovative with 5.3% and a unit increase in forward participation of the sector reduces this probability with 0.9%. The probability of incremental innovations increases with 3 and 0.7% for a unit increase in backward participation and forward participation at the sectoral level, respectively. And, finally, the probability of radical innovations increases with 2.3% (for backward participation) and 0.4% (forward participation); the level of GVC participation at the sectoral level increases with 1 unit.

Overall, no negative results were found regarding the relationship between GVC embeddedness and the innovativeness of organizations. Instead, hypothesis 1 is fully supported. Both backward and forward participation at the sectoral level are associated with higher levels of innovativeness in organizations. There are some notable differences across the findings. First, the outcomes are stronger for backward participation than for forward participation. Secondly, the outcomes show that backward participation at the sectoral level is more strongly related to radical product innovations, while in the case of process innovation, backward participation seems to lead to more incremental innovations.

Finally, it is worth noting that the control variables contribute to a further understanding of the innovativeness of organizations. Knowledge-intensive work practices have a profound impact in that regard. Knowledge-intensive organizations are far more likely to be innovative. The absence of innovations reduces with more than 5% for each unit increase in these practices (which amount to a difference of 35% from the lowest to the highest level of knowledge intensity). Digitalization also plays a major role. Organizations that do not use digital tools are almost 10% more likely to refrain from innovations. The outcomes for incremental and radical innovation are somewhere in the range of a 1 and 7% increase in innovativeness per 1 unit change (since these are dummy variables, this is the difference between applying these tools or not applying them). Finally, organizational size matters. The largest companies are the most innovative, both with regard to incremental and radical innovations.

Conclusion and discussion

Conclusion

This paper sets out to answer the question whether the innovation performance of organizations is related to GVC participation at the sectoral level, that is, GVC embeddedness. This question is addressed using the ECS (2019) in combination with sectoral-level data about backward participation, forward participation and domestic added value. The analyses lead to the following insights and conclusions.

Theoretical contributions and implications

First, GVC embeddedness at the sectoral level is associated with higher levels of innovation performance of organizations. These associations were found for product and process innovation in relation to both backward and forward participation. What the research shows is that these globalized external ties particularly matter for production and process innovation and reach organizations via the sector in which they operate. Clearly, the findings are in line with what may be expected from theories emphasizing the resource, knowledge and capability advantages that GVC may provide. This also means that no evidence was found for the opposite view that GVCs are a hindrance because they lead to fragmentation, complexity and increasing, which undermine the development of resources and routines. This means that overall, GVC embeddedness and innovation performance of organizations are positively related. Of course, this is not to say that there may be organizations going against this general trend, experiencing negative outcomes of innovation performance. That other studies have found some of the negative outcomes of GVCs adds to this argumentation. While we do see that on average GVC embeddedness goes along with the ability to maintain and develop resources, information and capabilities, this does not have to hold for every organization under all circumstances. Instead, we may expect these negative outcomes to appear under specific circumstances, which were not investigated here.

Secondly, the research shows that the sectoral level matters for the innovation performance of organizations. In part, this is due to the direct relations that these organizations have, but it also underscores that organizations are indirectly affected by the GVC participation of their sector. This can be understood as a form of GVC embeddedness in which learning and the transfer of knowledge take place through the GVC channels – between the organizations that are connected globally – as well as the diffusion of information across organizations in the same sector, even if not all of them are participating in the GVC.

Practical implications

For managers and consultants, this research offers several practical insights. Organizations aiming to increase their innovation performance – by developing new products and services and new forms of organizing – are advised to explore the possibilities of connecting to GVCs. This research suggests that particular attention should be geared towards managing the relationships with other organizations. Via these GVC relationships they may get access to new resources and learn from their GVC-partners. And by investing in these relations and by managing them carefully, they may benefit the most from them. This research also points to another way of improving the innovation performance of organizations, namely by partnering with other organizations that already participate in a GVC. Instead of tapping into a GVC themselves, they may access resources, information and capabilities indirectly via these partners. One of the main benefits of this approach is that it is associated with less risk and may therefore be an attractive option for many organizations, in particular for smaller organizations and those with fewer resources.

Limitations and future research

The study has several restrictions that should be considered. To begin with, the dataset did not contain direct measures of GVC participation at the organizational level. While the research shows that the sectoral level matters, future research should complement the present study by including more fine-grained measures of GVC participation at the organizational and the sectoral level. Having such data allows for deeper understanding of the dynamics and exchanges taking place between organizations, both within and outside the sector. In addition to that, the theoretical mechanisms that were used to develop the theoretical framework are not tested directly. The present research suggests that resources, knowledge and capabilities are developed and maintained as a result of GVC participation. But as the theoretical mechanisms could not be tested directly, this also remains a matter of interpretation. Future research would gain by making these mechanisms explicit and investigating them empirically. Such research should also pay attention to the condition under which GVC embeddedness and innovation performance occur.

Sectors included in the analyses

SectorNACE (ECS)STAN (TIVA)%
Mining and quarryingBD05T090.4
ManufacturingCD10T3325.6
Electricity, gas, steam and air conditioning supplyDD350.9
Water supply; sewerage, waste management and remediation activitiesED36T391.6
ConstructionFD41T4310.3
Wholesale and retail trade; repair of motor vehiclesGD45T4720.8
Transportation and storageHD49T536.2
Accommodation and food service activitiesID55T566.0
Information and communicationJD58T634.1
Financial and insurance activitiesKD64T662.1
Real estate activitiesLD681.4
Professional, scientific and technical activitiesMD69T756.8
Administrative and support services activitiesND77T823.9
Arts, entertainment and recreationRD90T933.3
Other service activitiesSD94T966.6

Source(s): Table by authors

Descriptive statistics

Min/MaxMeanS.D.Percentage
Backward participation0/67.2125.8212.43
Forward participation0/17.547.574.94
Product innovation
No0/1 65
Incremental (new to the organization)0/1 19
Radical (new to the market)0/1 16
Process innovation
No0/1 67
Incremental (new to the organization)0/1 25
Radical (new to the market)0/1 9
KIWPs1/73.601.16
Robots 18.0
DA processes 49.8
DA monitoring 31.8
Size
Small (10–49)0/1 62.4
Medium (50–249)0/1 27.3
Large (250+)0/1 10.3

Source(s): European Company Survey (ECS), Table by authors

Predicted probabilities for product innovation and backward participation

No innovationIncremental innovationRadical innovation
Predicted probabilitys.e.pPredicted probabilitys.e.pPredicted probabilitys.e.p
Backward participation−0.0620.0140.0000.0230.0010.0000.0390.0090.000
Knowledge-intensive work practices−0.0550.0030.0000.0210.0010.0000.0340.0020.000
Organization size
Medium−0.0190.0070.0080.0070.0030.0080.0120.0050.009
Large−0.0400.0110.0000.0150.0040.0000.0250.0070.000
Robots−0.0820.0100.0000.0310.0040.0000.0510.0060.000
DA production−0.1080.0070.0000.0410.0030.0000.0670.0050.000
DA monitoring−0.0350.0070.0000.0130.0030.0000.0220.0050.000
Wald χ21085.300 (p = 0.000)

Note(s): 18,854 organizations in 15 sectors in 28 countries

Source(s): European Company Survey (ECS), Table by authors

Predicted probabilities for process innovation and backward participation

No innovationIncremental innovationRadical innovation
Predicted probabilitys.e.pPredicted probabilitys.e.pPredicted probabilitys.e.p
Backward participation−0.0530.0130.0000.0300.0070.0000.0230.0060.000
Knowledge-intensive work practices−0.0540.0030.0000.0300.0020.0000.0240.0020.000
Organization size
Medium−0.0280.0070.0000.0160.0040.0000.0120.0030.000
Large−0.0420.0110.0000.0230.0060.0000.0190.0050.000
Robots−0.1270.0100.0000.0710.0050.0000.0600.0050.000
DA production−0.1200.0070.0000.0670.0040.0000.0530.0040.000
DA monitoring−0.0620.0070.0000.0350.0040.0000.0270.0030.000
Wald χ21441.910 (p = 0.000)

Note(s): 18,811 organizations in 15 sectors in 28 countries

Source(s): European Company Survey (ECS), Table by authors

Predicted probabilities for product innovation and forward participation

No innovationIncremental innovationRadical innovation
Predicted probabilitys.e.pPredicted probabilitys.e.pPredicted probabilitys.e.p
Forward participation−0.0180.0030.0000.0070.0010.0000.0110.0020.000
Knowledge-intensive work practices−0.0540.0030.0000.0200.0010.0000.0340.0020.000
Organization size
Medium−0.0180.0070.0110.0070.0030.0110.0110.0050.012
Large−0.0380.0110.0060.0140.0040.0000.0240.0070.000
Robots−0.0840.0100.0000.0310.0040.0000.0530.0060.000
DA production−0.1080.0070.0000.0400.0030.0000.0680.0050.000
DA monitoring−0.0360.0070.0000.0130.0030.0000.0220.0040.000
Wald χ21130.870 (p = 0.000)

Note(s): 19,069 organizations in 15 sectors in 28 countries

Source(s): European Company Survey (ECS), Table by authors

Predicted probabilities for process innovation and forward participation

No innovationIncremental innovationRadical innovation
Predicted probabilitys.e.pPredicted probabilitys.e.pPredicted probabilitys.e.p
Forward participation−0.0090.0030.0000.0050.0020.0000.0040.0010.000
Knowledge-intensive work practices−0.0520.0030.0000.0290.0020.0000.0230.0020.000
Organization size
Medium−0.0280.0070.0000.0150.0040.0000.0120.0040.000
Large−0.0410.0110.0000.0230.0060.0000.0190.0050.000
Robots−0.1290.0100.0000.0720.0050.0000.0570.0050.000
DA production−0.1200.0070.0000.0670.0040.0000.0530.0040.000
DA monitoring−0.0620.0070.0000.0350.0040.0000.0280.0030.000
Wald χ21466.880 (p = 0.000)

Note(s): 19,024 organizations in 15 sectors in 28 countries

Source(s): European Company Survey (ECS), Table by authors

References

Ambos, B., Brandl, K., Perri, A., Scalera, V.G. and Van Assche, A. (2021), “The nature of innovation in global value chains”, Journal of World Business, Vol. 56 No. 4, 101221, doi: 10.1016/j.jwb.2021.101221.

Antràs, P. and Chor, D. (2022), “Global value chains”, in Handbook of International Economics, Vol. 5, pp. 297-376, doi: 10.1016/bs.hesint.2022.02.005.

Anzolin, G. and Benassi, C. (2024), “How do countries shift their export specialization? The role of technological capabilities and industrial policy in Ireland, Spain and Sweden (1995–2018)”, Socio-Economic Review, doi: 10.1093/ser/mwae010.

Bai, C., Dallasega, P., Orzes, G. and Sarkis, J. (2020), “Industry 4.0 technologies assessment: a sustainability perspective”, International Journal of Production Economics, Vol. 229, 107776, doi: 10.1016/j.ijpe.2020.107776.

Bardhan, A.D. and Jaffee, D.M. (2005), Innovation, R&D and Offshoring, Fisher Center, Berkeley.

Baum, C.F., Lööf, H., Stephan, A. and Viklund-Ros, I. (2022), “The impact of offshoring on technical change: evidence from Swedish manufacturing firms”, Review of International Economics, Vol. 30 No. 3, pp. 796-818, doi: 10.1111/roie.12586.

Brancati, E., Pietrobelli, C. and Mazzi, C. (2021), “The influence of value-chain governance on innovation performance: a study of Italian suppliers”, IZA paper 14330, Bonn.

Cai, J., Li, N. and Santacreu, A.M. (2022), “Knowledge diffusion, trade, and innovation across countries and sectors”, American Economic Journal: Macroeconomics, Vol. 14 No. 1, pp. 104-145.

Ciani, A. and Gregori, W.D. (2022), “Participation in global value chains and M&A flows”, JRC Working Papers in Economics and Finance No 2022/9.

Contractor, F.J., Kumar, V., Kundu, S.K. and Pedersen, T. (2010), “Reconceptualizing the firm in a world of outsourcing and offshoring: the organizational and geographical relocation of high-value company functions”, Journal of Management Studies, Vol. 47 No. 8, pp. 1417-1433, doi: 10.1111/j.1467-6486.2010.00945.x.

Crossan, M.M. and Apaydin, M. (2010), “A multi-dimensional framework of organizational innovation: a systematic review of the literature”, Journal of Management Studies, Vol. 47 No. 6, pp. 1154-1191, doi: 10.1111/j.1467-6486.2009.00880.x.

Dang, D.A. and Dang, V.A. (2020), “Global value chain participation and firms' innovations: evidence from small and medium-sized enterprises in Viet Nam”, No. 1138, ADBI Working Paper Series.

Eissa, Y. and Zaki, C. (2023), “On GVC and innovation: the moderating role of policy”, Journal of Industrial and Business Economics, Vol. 50, pp. 1-23, doi: 10.1007/s40812-022-00255-9.

Elshaarawy, R. and Ezzat, R.A. (2022), “Global value chains, financial constraints, and innovation”, Small Business Economics, Vol. 61, pp. 1-35, doi: 10.1007/s11187-022-00685-8.

Ernst, D. and Kim, L. (2002), “Global production networks, knowledge diffusion, and local capability formation”, Research Policy, Vol. 31 Nos 8-9, pp. 1417-1429, doi: 10.1016/s0048-7333(02)00072-0.

Fawcett, S.E., Wallin, C., Allred, C., Fawcett, A.M. and Magnan, G.M. (2011), “Information technology as an enabler of supply chain collaboration: a dynamic capabilities perspective”, Journal of Supply Chain Management, Vol. 47 No. 1, pp. 38-59, doi: 10.1111/j.1745-493x.2010.03213.x.

Gereffi, G. (2014), “Global value chains in a post-Washington Consensus world”, Review of International Political Economy, Vol. 21 No. 1, pp. 9-37, doi: 10.1080/09692290.2012.756414.

Gereffi, G., Lim, H.C. and Lee, J. (2021), “Trade policies, firm strategies, and adaptive reconfigurations of global value chains”, Journal of International Business Policy, Vol. 4 No. 4, pp. 1-17, doi: 10.1057/s42214-021-00102-z.

Holmqvist, M. (2003), “A dynamic model of intra-and interorganizational learning”, Organization Studies, Vol. 24 No. 1, pp. 95-123, doi: 10.1177/0170840603024001684.

Hu, D., Jiao, J., Tang, Y., Han, X. and Sun, H. (2021), “The effect of global value chain position on green technology innovation efficiency: from the perspective of environmental regulation”, Ecological Indicators, Vol. 121, 107195, doi: 10.1016/j.ecolind.2020.107195.

Hu, D., Jiao, J., Tang, Y., Xu, Y. and Zha, J. (2022), “How global value chain participation affects green technology innovation processes: a moderated mediation model”, Technology in Society, Vol. 68, 101916, doi: 10.1016/j.techsoc.2022.101916.

Inkinen, H.T., Kianto, A. and Vanhala, M. (2015), “Knowledge management practices and innovation performance in Finland”, Baltic Journal of Management, Vol. 10 No. 4, pp. 432-455, doi: 10.1108/bjm-10-2014-0178.

Kano, L., Tsang, E.W. and Yeung, H.W.C. (2020), “Global value chains: a review of the multi-disciplinary literature”, Journal of International Business Studies, Vol. 51 No. 4, pp. 577-622, doi: 10.1057/s41267-020-00304-2.

Kitsos, T., Grabner, S.M. and Carrascal-Incera, A. (2023), “Industrial embeddedness and regional economic resistance in Europe”, Economic Geography, Vol. 99 No. 3, pp. 1-26, doi: 10.1080/00130095.2023.2174514.

Koster, F. (2022), “Organizations in the knowledge economy. An investigation of knowledge-intensive work practices across 28 European countries”, Journal of Advances in Management Research, Vol. 20 No. 1, pp. 140-159, (ahead-of-print), doi: 10.1108/JAMR-05-2021-0176.

Lema, R., Pietrobelli, C. and Rabellotti, R. (2019), “Innovation in global value chains”, in Handbook on Global Value Chains, Edward Elgar Publishing, pp. 370-384.

Liddell, T.M. and Kruschke, J.K. (2018), “Analyzing ordinal data with metric models: what could possibly go wrong?”, Journal of Experimental Social Psychology, Vol. 79, pp. 328-348.

McWilliam, S.E., Kim, J.K., Mudambi, R. and Nielsen, B.B. (2020), “Global value chain governance: intersections with international business”, Journal of World Business, Vol. 55 No. 4, 101067, doi: 10.1016/j.jwb.2019.101067.

Mol, M.J. (2001), “Creating wealth through working with others: interorganizational relationships”, Academy of Management Perspectives, Vol. 15 No. 1, pp. 150-152, doi: 10.5465/ame.2001.4251565.

Moreira, S., Markus, A. and Laursen, K. (2018), “Knowledge diversity and coordination: the effect of intrafirm inventor task networks on absorption speed”, Strategic Management Journal, Vol. 39 No. 9, pp. 2517-2546.

Narula, R. (2014), Globalization and Technology: Interdependence, Innovation Systems and Industrial Policy, Polity Press, Cambridge.

Ndubuisi, G. and Owusu, S. (2021), “How important is GVC participation to export upgrading?”, The World Economy, Vol. 44 No. 10, pp. 2887-2908, doi: 10.1111/twec.13102.

Nieto, M.J. and Rodríguez, A. (2011), “Offshoring of R&D: looking abroad to improve innovation performance”, Journal of International Business Studies, Vol. 42 No. 3, pp. 345-361, doi: 10.1057/jibs.2010.59.

OECD (2013), Interconnected Economies: Benefiting from Global Value Chains, OECD, Paris.

OECD (2021), Guide to OECD's Trade in Value Added (TiVA) Indicators, 2021 edition, OECD.

Pandza, K. and Thorpe, R. (2009), “Creative search and strategic sense-making: missing dimensions in the concept of dynamic capabilities”, British Journal of Management, Vol. 20 No. S1, pp. S118-S131, doi: 10.1111/j.1467-8551.2008.00616.x.

Phene, A. and Almeida, P. (2008), “Innovation in multinational subsidiaries: the role of knowledge assimilation and subsidiary capabilities”, Journal of International Business Studies, Vol. 39 No. 5, pp. 901-919, doi: 10.1057/palgrave.jibs.8400383.

Pietrobelli, C. and Rabellotti, R. (2011), “Global value chains meet innovation systems: are there learning opportunities for developing countries?”, World Development, Vol. 39 No. 7, pp. 1261-1269, doi: 10.1016/j.worlddev.2010.05.013.

Ponte, S., Gereffi, G. and Raj-Reichert, G. (2019), “Introduction to the handbook on global value chains”, in Handbook on Global Value Chains, Edward Elgar Publishing, pp. 1-27.

Reddy, K., Chundakkadan, R. and Sasidharan, S. (2021), “Firm innovation and global value chain participation”, Small Business Economics, Vol. 57 No. 4, pp. 1995-2015, doi: 10.1007/s11187-020-00391-3.

Ruiz-Ortega, M.J., Rodrigo-Alarcón, J. and Parra-Requena, G. (2023), “New directions to create dynamic capabilities: the role of pioneering orientation and interorganizational relationships”, European Management Journal, Vol. 24 No. 3, pp. 371-384, doi: 10.1016/j.emj.2023.01.005.

Sampath, P.G. and Vallejo, B. (2018), “Global value chains and upgrading: what, when and how?MERIT Working Papers 016.

Schmitz, H. (2005), Value Chain Analysis for Policy-Makers and Practitioners, International Labour Office, Geneva.

Schmitz, H. and Knorringa, P. (2000), “Learning from global buyers”, Journal of Development Studies, Vol. 37 No. 2, pp. 177-205, doi: 10.1080/713600073.

Steinberg, P.J., Procher, V.D. and Urbig, D. (2017), “Too much or too little of R&D offshoring: the impact of captive offshoring and contract offshoring on innovation performance”, Research Policy, Vol. 46 No. 10, pp. 1810-1823, doi: 10.1016/j.respol.2017.08.008.

Szambelan, S., Jiang, Y. and Mauer, R. (2020), “Breaking through innovation barriers: linking effectuation orientation to innovation performance”, European Management Journal, Vol. 38 No. 3, pp. 425-434, doi: 10.1016/j.emj.2019.11.001.

Tajoli, L. and Felice, G. (2018), “Global value chains participation and knowledge spillovers in developed and developing countries: an empirical investigation”, The European Journal of Development Research, Vol. 30 No. 3, pp. 505-532, doi: 10.1057/s41287-017-0127-y.

Thakur-Wernz, P. and Wernz, C. (2022), “Does R&D offshore outsourcing improve innovation in vendor firms from emerging economies? A study of biopharmaceutical industry in India”, International Journal of Emerging Markets, Vol. 17 No. 6, pp. 1373-1403, doi: 10.1108/ijoem-03-2020-0308.

Turkina, E. and Van Assche, A. (2018), “Global connectedness and local innovation in industrial clusters”, Journal of International Business Studies, Vol. 49 No. 6, pp. 706-728, doi: 10.1057/s41267-018-0153-9.

Van Assche, A. (2017), “Global value chains and innovation”, in The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, pp. 739-751.

Wakelin, K. (2001), “Productivity growth and R&D expenditure in UK manufacturing firms”, Research Policy, Vol. 30 No. 7, pp. 1079-1090, doi: 10.1016/s0048-7333(00)00136-0.

Yamin, M. (2011), “A commentary on Peter Buckley's writings on the global factory”, Management International Review, Vol. 51 No. 2, pp. 285-293, doi: 10.1007/s11575-011-0074-3.

Yang, N., Hong, J., Wang, H. and Liu, Q. (2020), “Global value chain, industrial agglomeration and innovation performance in developing countries: insights from China's manufacturing industries”, Technology Analysis and Strategic Management, Vol. 32 No. 11, pp. 1307-1321, doi: 10.1080/09537325.2020.1767772.

Further reading

Barthelemy, J. (2001), “The hidden costs of IT outsourcing”, MIT Sloan Management Review, Vol. 42 No. 3, p. 60.

Cuypers, I.R., Hennart, J.F., Silverman, B.S. and Ertug, G. (2021), “Transaction cost theory: past progress, current challenges, and suggestions for the future”, Academy of Management Annals, Vol. 15 No. 1, pp. 111-150, doi: 10.5465/annals.2019.0051.

Damanpour, F. (1991), “Organizational innovation: a meta-analysis of effects of determinants and moderators”, Academy of Management Journal, Vol. 34 No. 3, pp. 555-590, doi: 10.2307/256406.

Ehab, M. and Zaki, C.R. (2021), “Global value chains and service liberalization: do they matter for skill-upgrading?”, Applied Economics, Vol. 53 No. 12, pp. 1342-1360, doi: 10.1080/00036846.2020.1830938.

Gambal, M.J., Asatiani, A. and Kotlarsky, J. (2022), “Strategic innovation through outsourcing–a theoretical review”, The Journal of Strategic Information Systems, Vol. 31 No. 2, 101718, doi: 10.1016/j.jsis.2022.101718.

Görg, H. and Hanley, A. (2011), “Services outsourcing and innovation: an empirical investigation”, Economic Inquiry, Vol. 49 No. 2, pp. 321-333, doi: 10.1111/j.1465-7295.2010.00299.x.

Hendry, J. (1995), “Culture, community and networks: the hidden cost of outsourcing”, European Management Journal, Vol. 13 No. 2, pp. 193-200, doi: 10.1016/0263-2373(95)00007-8.

Ivarsson, I. and Alvstam, C.G. (2011), “Upgrading in global value-chains: a case study of technology-learning among IKEA-suppliers in China and Southeast Asia”, Journal of Economic Geography, Vol. 11 No. 4, pp. 731-752, doi: 10.1093/jeg/lbq009.

Kano, L. (2018), “Global value chain governance: a relational perspective”, Journal of International Business Studies, Vol. 49 No. 6, pp. 684-705, doi: 10.1057/s41267-017-0086-8.

Koster, F. (2021), “Organisational antecedents of innovation performance: an analysis across 32 European countries”, International Journal of Innovation Management, Vol. 25 No. 04, 2150037, doi: 10.1142/s1363919621500377.

McDermott, G.A. and Corredoira, R.A. (2010), “Network composition, collaborative ties, and upgrading in emerging-market firms: lessons from the Argentine autoparts sector”, Journal of International Business Studies, Vol. 41 No. 2, pp. 308-329, doi: 10.1057/jibs.2009.7.

Mederos, L.A. (2021), “The outsourcing manifesto: the history, rise, and potential fall of the outsourcing industry”, in The Future of Outsourcing: Strategic Outsourcing Controls and the Backsourcing Evolution, Springer International Publishing, Cham, pp. 1-30.

Soontornthum, T., Cui, L., Lu, V.N. and Su, J. (2020), “Enabling SMEs' learning from global value chains: linking the logic of power and the logic of embeddedness of interfirm relations”, Management International Review, Vol. 60 No. 4, pp. 543-571, doi: 10.1007/s11575-020-00425-8.

Young, S. (2007), “Outsourcing: uncovering the complexity of the decision”, International Public Management Journal, Vol. 10 No. 3, pp. 307-325, doi: 10.1080/10967490701515572.

Acknowledgements

This research is part of the Horizon Europe program Twin Seeds (ID: 101056793).

Corresponding author

Ferry Koster can be contacted at: koster@essb.eur.nl

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