The effect of R & D laboratories on MNE subsidiary performance: a primary survey in a European Mediterranean country Free

MNEs increasingly carry out R&D and innovative activities in foreign locations hence setting up a rising number of R&D labs abroad (Castellani et al., 2013) exhibiting different characteristics and functions (e.g. Manolopoulos et al., 2007; Athreye et al., 2016; Sapouna et al., 2016). Their nature and characteristics are shaped both by their local environment and by the special features of the subsidiary units that own them (Bouquet and Birkinshaw, 2008; Kottaridi et al., 2009; Castellani et al., 2013; Pereira et al., 2020). These labs are the outcome of historical evolution (Isaac et al., 2019; Pereira et al., 2020). Whereas in the earlier development phase foreign R&D activities were aimed basically at adapting products to the local market, in recent years an increasing number of R&D labs have been set up abroad to undertake advanced technological roles, and in many cases subsidiaries' mandates have evolved into competence creation (Castellani et al., 2013). The specific evolution has created a globally dispersed portfolio of heterogeneous R&D labs at subsidiary level. Their development in diverse local markets has led several researchers to recognize the significant contribution of subsidiaries to the increase of innovations within MNEs (Isaac et al., 2019), operating increasingly in knowledge-based economies (Hassen, 2021) and in emerging economies as well (Patra and Krishna, 2015; Pereira et al., 2020; Bahl et al., 2021; Elia et al., 2020; Zhang et al., 2022). Thus, investment in knowledge-based resources such as diverse technological facilities has contributed to the creation of heterogeneous subsidiaries embedded in different environmental conditions (Ghoshal and Nohria, 1989).

Our analysis is designed to explore different types of R&D laboratories, according to the extent and depth of their technological functions (Manolopoulos et al., 2007; Athreye et al., 2016; Sapouna et al., 2016), and identify the following respective categories (Table 1). First, the operation of locally integrated laboratories (LILs) which pursue the creation of new products and/or new processes (Zhang et al., 2022; Beugelsdijk and Jindra, 2018), mostly for regional or global markets. Second, the operation of internationally interdependent laboratories (IILs) (Manolopoulos et al., 2007; Athreye et al., 2016; Sapouna et al., 2016) that provide basic research inputs for the technological needs of their MNE group, being extensively integrated with other globally based R&D units. Such laboratories enable respective subsidiaries to operate as “global creators” (Ambos and Schlegelmilch, 2007), taking on strategic positions in strongly interdependent research networks, augmenting the scientific and technological knowledge base of the corporate group (Sapouna et al., 2016). Third, support laboratories (SLs) which engage in the adaptation of existing products and/or processes to local market needs (Ambos and Schlegelmilch, 2007; Manolopoulos et al., 2007; Athreye et al., 2016; Sapouna et al., 2016). This involves incremental innovation activity primarily connected with market-seeking MNE motivations. In addition to the active technological subsidiaries, there are several MNE subsidiaries that do not develop any research activity (absence of R&D labs).

Recent systematic reviews on R&D internationalization show that the specific literature is fragmented (Du et al., 2022), with a multi-disciplinary nature (Papanastassiou et al., 2020; Vrontis and Christofi, 2021) dealing in a relatively small area with the issue of technological laboratories. Most of the related R&D literature focuses on research and development expenditures (e.g. Srinivasan et al., 2011; Steenkamp and Fang, 2011), and innovation performance, emphasizing the outcome of technological activity in terms of new products, processes, and systems (e.g. Beugelsdijk and Jindra, 2018). The distinct research stream related to R&D labs that is placed in the broad framework of the R&D internationalization literature is quite fragmented not having a clear research objective. Some scholars concentrate on the roles of R&D labs (Manolopoulos et al., 2007; Sapouna et al., 2016), in connection with the respective subsidiary types that own them (Pearce, 1999; Manolopoulos et al., 2007). Others investigate the sourcing mechanisms of labs (e.g. Athreye et al., 2016), and their link with employee issues (Manolopoulos, 2006; Sapouna et al., 2016). Castellani et al. (2013) investigate the impact of geographic distance and institutional proximity on the probability of setting up international R&D labs. In the same vein, Kottaridi et al. (2009) explore the determinants of MNE subsidiaries decisions to set up own R&D labs drawing on evidence from UK regions.

To our best knowledge, there is no study to date that examines the effect of technological laboratories on financial performance an issue that, in our opinion, is of important research interest. From a resource- and knowledge-based perspective, knowledge resources such as R&D labs may be viewed as a key innovation mechanism to engender business growth and renewal (Lee and Sung, 2005; Naidoo, 2010; Sapouna et al., 2016) helping MNE subsidiaries to outperform their counterparts and to overcome unfavorable external conditions that endanger their economic performance (Naidoo, 2010; Fainshmidt et al., 2017; White et al., 2014). Scholars emphasize the great importance of R&D activity for firms' long-term sustainability, performance (e.g. Du et al., 2022), and productivity due to knowledge spillovers (Audretsch and Belitski, 2020). It is reasonable to assume that the market will reward superior knowledge-based resource allocation (Zhang et al., 2022; Fang et al., 2013) reflected in advanced R&D labs that provide a crucial base for product (Tada and Ida, 2021; Audretsch and Belitski, 2020; AL-Kwifi et al., 2021) and process innovations, upgrading the roles of well-performing subsidiaries in the overall innovation system of MNEs (Zhang et al., 2022). Especially in recession, such knowledge-based resource allocation decisions have a direct impact on performance and survival (Steenkamp and Fang, 2011). From this point of view, advanced R&D laboratory types such as LILs & IILs basically involve radical innovation activity and high-quality business factors such as greater human capital, expressing superior resource allocation that provides substantial competitive tools to improve performance in a demanding suboptimal economic environment. Castelani et al. (2013) argue that R&D activities within technological labs play a crucial role in subsidiaries' value creation and performance, facilitating the accumulation of specific assets, primarily intangible in nature created from human intellect such as patents, copyrights, and trademarks (Elia et al., 2020). Kafouros et al. (2022) underline that the question of how MNEs create new technologies in different locations in a way that is optimal for strengthening their profitability has not been addressed until now. Whereas the link between technological inputs (e.g. laboratories, research, and development expenditures) and innovative performance (e.g. new products and processes) is rather obvious, the question whether technological facilities in terms of R&D labs affect overall business performance remains completely unanswered.

Our study fills this research gap investigating how MNE subsidiary performance varies across diverse R&D facilities given their heterogeneity (Sapouna et al., 2016). In this context, we utilize the R&D labs as the main explanatory variables, whereas we use subsidiary financial performance as the dependent variable of our study. We explicitly focus on the perceived performance contribution of R&D laboratories operated by ΜΝΕ subsidiaries in a crisis environment that tests the quality of R&D centers and their contribution to the financial performance of the subsidiaries that own them (Fainshmidt et al., 2017; Georgopoulos and Glaister, 2018). Our overarching research target is to investigate whether variations in the respective labs differentiate perceived subsidiary performance in turbulent times. We develop 4 research questions by taking in pairs different groups of subsidiaries according to whether they have a technological laboratory and if so, what type.

MNE manufacturing subsidiaries located in Greece over the 2009–2016 period constitute the empirical site of our study, which constitutes a suitable setting for the examination of our research questions for the following reasons. The global economic crisis that seriously affected the Greek economy in 2009, with the culmination of capital controls in 2015, offers an appropriate context to test the performance effectiveness of R&D labs. Also, due to the rise of innovative competition on the European scale, the local MNE subsidiaries, especially after the entry of Greece into the Eurozone in 2002, were forced more and more to develop technological activities which were reflected in the establishment of their own R&D facilities to contribute to the overall innovative and economic performance of their corporate groups. In doing so we explore the financial performance contribution of R&D labs at subsidiary level during economic recession (2009–2016). We are thus able to capture performance differences at MNE subsidiary level, arising from the possession of different research and development laboratories. Our unique sample comprises 171 MNE manufacturing subsidiaries from which we obtain primary data through a questionnaire administered to top executives of the subsidiaries.

We find that financial performance tends to vary between different MNE subsidiaries that follow different technological trajectories and possess different R&D labs. Subsidiaries with advanced labs are better able to capitalize on the perceived opportunity created by the economic downturn (Srinivasan et al., 2005; Zhang et al., 2022; Naidoo, 2010; Steenkamp and Fang, 2011) hence standing out from the crowd (Srinivasan et al., 2011). Generally, the operation of R&D labs regardless of type helps the subsidiary to climb the value chain and develop stronger competitive advantages (Elia et al., 2020) expressed in superior financial performance compared to the alternative scenario of the absence of technological laboratories. So, we attribute the different performances of the MNE subsidiaries to their diversified R&D labs portfolio that contains different types of labs with quite different functions (e.g. Manolopoulos et al., 2007). Concepts such as technological resources, knowledge resources, and R&D facilities/laboratories are considered synonymous throughout the paper.

Our study adds value to existing R&D internationalization literature. Embedded in the literature of technological laboratories, it provides useful insights into the environmentally derived “knowledge-based - performance” context, so filling an important research gap, given that internationalization of R&D by MNEs has undergone a comprehensive change in perspective over the past 50 years (Papanastassiou et al., 2020). Also, we shed light on important crisis effects that can significantly impact on subsidiary structure and performance (Venaik et al., 2005; Birkinshaw et al., 2005; Steenkamp and Fang, 2011; Tian and Slocum, 2014; Meyer et al., 2011; Tian and Slocum, 2014, ; Liu et al., 2016; Chatzoudes et al., 2022).

The remainder of the paper is set out as follows: The next section provides the conceptual background and develops the study's hypotheses. We then set out the research methods, followed by presentation of results. A discussion and conclusions are in the final section.

The contemporary resourced-based view of the MNE considers subsidiaries as strategic actors that augment the knowledge resources and capabilities of the MNE at foreign locations by developing subsidiary-specific advantages (Beugelsdijk and Jindra, 2018). The theory suggests that the MNE subsidiary acquires resources from the external environment to build new specialized capabilities unavailable to rivals, to re-combine existing critical resources and to design innovative business strategies for performance improvement and long-term survival. The theory also suggests that heterogeneous, unique tangible and intangible resources and capabilities that are normally valuable, rare, inimitable, and non-substitutable may create the potential for sustained technology-based competitive advantage (e.g. Qu and Zhang, 2015) and lead to superior performance at subsidiary level (Mallon and Fainshmidt, 2017). Thus, the resource-based view explains performance heterogeneity because of firms owning resources with different productivities.

These considerations set a general framework for our empirical analysis. We enrich the specific framework by incorporating crucial elements of knowledge-based theory. This theory is an extension of the resource-based view as it emphasizes heterogeneous entities loaded with knowledge (Kogut and Zander, 1993). The specific theory argues that the performance of MNE relies on firm-specific capabilities and resources for knowledge creation in different locations coupled with the management of knowledge transfer. Knowledge converted into products or services is highly valued and considered to be a special strategic resource that is the basis of the mechanism for economic rent creation. Unique intangible resources such as information, patents, know-how, expert insight etc. along with physical facilities such as technological laboratories are particularly important for the achievement of sustainable competitive advantages and performance superiority.

The MNE subsidiary often faces performance adversities caused by environmental deterioration in the host economy. The subsidiary's knowledge-dependent performance is thus context-dependent (e.g. Meyer et al., 2011; Liu et al., 2016; Fainshmidt et al., 2017). A superior knowledge base can be associated with higher strategic flexibility and faster reaction to environmental deterioration. Our primary question therefore is related to the performance contribution of different R&D labs in periods of economic recession. Performance success in a turbulent environment normally requires high absorptive capacity (e.g. Fang et al., 2013) to manage unusual conditions, risks and uncertainties which threaten the profitability and survival of the subsidiaries themselves (e.g. Liu et al., 2016). This capacity enables not only the extraction of rents from current sets of resources, but also the design of new resource packages, so building new capabilities for the purpose of generating unique applications from those resources (White et al., 2014). Put differently, absorptive capacity indicates the ability to integrate and reconfigure existing internal and external resources to build new capabilities for the purpose of addressing opportunities and threats in rapidly deteriorating environments (e.g. Fainshmidt et al., 2017).

Closely allied to the resource-based view and the knowledge-based perspective, the approach of resource recombination and business restructuring posits that the way the firm reconfigures its crucial knowledge resources, especially those that bring innovation, can provide substantial value creation (Sapouna et al., 2016). Hence, the development of dynamic knowledge-related resources and capabilities in terms of R&D labs that should be viewed as an evolutionary and cumulative process of resource learning is necessary for strong competitiveness and performance improvement. This path-dependent process derived through a continuous improvement may lead to a sustainable competitive advantage (e.g. Wang et al., 2005). This means that over time the respective subsidiaries will have developed their own systems of innovation in terms of R&D labs, cultivated their own network of relationships with local players that feed into their technological know-how, built their own tacit skills that constitute their own competencies, and integrated into global technological networks for knowledge and information exchange (Wang et al., 2005).

An important consideration in this context is the MNE subsidiary's strategic decision to set up their own R&D laboratory in their host locality and the type of R&D laboratory. Relevant literature indicates three diverse types of decentralized R&D laboratories. First, there are locally integrated laboratories (LILs) that principally extend the scope of their subsidiaries by expanding the competitive product range of the MNE group. These subsidiaries normally develop a distinctive product that can be supplied to a regional or global market and are likely to have a broad strategic role since they contribute to knowledge and product innovations within the MNE group (Manolopoulos et al., 2007; Sapouna et al., 2016). By undertaking greater levels of responsibility and authority these units develop greater flexibility and adaptability regarding accumulating, enriching, and bundling resource processes, that enable them to overcome environmentally derived disadvantages.

Second, internationally interdependent laboratories (IILs) aim to provide the MNE group with a full range of basic research inputs, being extensively integrated with other globally–based R&D units. Such laboratories enable respective subsidiaries to operate as “global creators” (Ambos and Schlegelmilch, 2007), taking on strategic positions in strongly interdependent research networks, augmenting the scientific and technological knowledge base of their corporate group (Sapouna et al., 2016).

Third, the operation of support laboratories (SLs) by local product adapters (Ambos and Schlegelmilch, 2007) contributes by making minor product or process adaptations, when needed, that mostly cannot provide effective and global solutions to the complex problems of the subsidiary's clients in recession (e.g. new techniques to reduce costs and increase productivity, high value-added products that fulfill the principle of value for money). Support laboratories mostly work in isolation from other R&D laboratories of a MNE and interact only with local subsidiary functional departments, specifically those that support marketing activities (Manolopoulos et al., 2007).

Relevant studies (e.g. Manolopoulos et al., 2007; Sapouna et al., 2016) and anecdotal evidence support the view that the accumulation of advanced knowledge-based resources embodied in LILs and IILs, substantially upgrades innovative capacity and evolutionary potential over time. The utilization of creative resources and dynamic technical capabilities provides the necessary technological platform for innovations that can be viewed as a key business strategy to generate a sustained, technology-based competitive advantage (Beugelsdijk and Jindra (2018), even under severe economic conditions (Chang et al., 2012). Intensive knowledge utilization within LILs and IILs and greater responsibilities undertaken within the MNE group's overall innovative strategy (Zhang et al., 2022) may be reflected in superior resource allocation and improved subsidiary performance due to better exploitation of a steep learning curve through learning-by-doing effects (Lee and Sung, 2005). We expect that superior resource allocation reflected in advanced technological facilities such as in LILS and IILs (Sapouna et al., 2016) will lead to increased subsidiary sales and profits (Zhang et al., 2022), even in times of economic contraction.

Despite their marginal/small technological contribution, the role of support laboratories should not be underestimated. These laboratories will have accumulated significant experience in the context of market-seeking activities through continuous differentiation and customization of products. This gives their subsidiaries a competitive advantage in the local market, mainly over passive technological subsidiaries, as they can seek more credible solutions during recession by further differentiating their products, reducing their costs, or by discovering new market segments meeting new consumer needs, caused by the contraction of disposable income.

In the absence of R&D laboratories, the respective MNE subsidiaries become “passive technology players”, relying exclusively on the technological support of the parent MNE. These subsidiaries do not have the necessary technological resources/facilities effectively to face considerable performance adversities caused by recession.

Advanced technological laboratories such as in LILS and IILs normally reveal superior performance for additional reasons. More specifically, they deploy high-quality human capital (Fainshmidt et al., 2017; Bouguerra et al., 2020). High-quality human capital comprises both R&D employees with inventive engineering knowledge and technical know-how (Manolopoulos et al., 2007) and managerial expertise, especially in strategic management. The exploitation of market opportunities especially during recession (Srinivasan et al., 2005), requires sophisticated managerial action and responses at MNE subsidiary level. This assumes that management properly assesses and identifies the idiosyncratic characteristics of rapidly changing host country environmental dynamics (White et al., 2014; Fainshmidt et al., 2017), so having the capability of market responsiveness (Bouguerra et al., 2020) effectively combined with coordination capability, organizational systems, and socialization processes (Bouguerra et al., 2020). In a similar vein, Fainshmidt et al. (2017, 1,088) refer to “… asset management capability which captures the extent to which managers are able to configure resources, so as to extract more value from the firm's resource base”. This describes an evolutionary perspective that comprises key dynamic factors in strategic resource-allocation decisions and capability-building processes among business units (e.g. Barney, 1991) in difficult times (Fainshmidt et al., 2017). Research on dynamic managerial capabilities focuses on the role of managers in systematically altering the resource base, and continuously orchestrating technological and other critical resources into bundles that extract more value from the subsidiary's resource pool (Fang et al., 2013; Fainshmidt et al., 2017). Managers of this kind will be able to recognize the unique features of the volatile environment, proactively building inimitable assets to fulfill current, and influence future, environmental demands, so that they are congruent with the organization's operational needs and objectives (White et al., 2014; Fainshmidt et al., 2017). Innovative change and crisis management that substantially differentiates knowledge-based subsidiaries from their rivals increases subsidiaries' ability to successfully tackle environmental risks that threaten their performance (White et al., 2014). Overall, we expect that the availability of high-level knowledge-based resources in terms of strong managerial know-how and expertise effectively combined with the operation of advanced technological laboratories will significantly enhance MNE subsidiary responsiveness to an economic recession. Finally, subsidiaries with LILS and IILs exhibit a high export intensity, a fact that increases their operational flexibility and reduces dependence on the local crisis market.

Contemporary resource- and knowledge-based perspectives largely support the above considerations suggesting that different technological laboratories have in recession a differentiated financial performance impact at subsidiary level. Due to the complete absence of relevant research activity, there is uncertainty regarding the outcome of our empirical research. Therefore, we posit the following research questions (QR):

We adopt Greece as the site of the research, underpinned through extensive field research conducted with subsidiary CEOs in 2019. Greece provides a highly relevant research context for the study since it includes MNE subsidiaries that have successfully adapted to the European integrated environment where technological competition is fierce and innovation activity is crucial for firm performance and survival. The data obtained from CEOs refer to the economic downturn (2009–2016).

We identified local MNE subsidiaries from the official lists provided by seven major Foreign Chambers of Industry and Commerce located in Greece (US, British, German, French, Italian, Dutch and Swiss) that include 400 subsidiaries. The CEO of each of subsidiary was sent a questionnaire. We chose to communicate with senior executives as they had a complete picture of the technological strategy of their subsidiary and their multinational group, while they were able to evaluate the consequences of the specific strategy on the overall financial performance of the subsidiary. Also, they had experienced the economic crisis and had made difficult decisions with performance and survival implications. Another very important reason for the communication with the CEOs was the identification of the type of technological laboratories. The top executives were not simply able to confirm the operation of a laboratory from their subsidiary, but above all to classify their laboratory in one of the individual laboratory categories recorded by extant literature. Based on the specific characteristics of the laboratories (as described in Table 1), the top executives made the relevant evaluation and ranking.

Because the risk of refusal of high-ranking executives to participate in the process was relatively high, we sent the questionnaires electronically to all subsidiary units to collect as many responses as possible. A total of 171 manufacturing subsidiaries participated in the survey, a response rate of 42.75%. Consequently, the sample subsidiaries were the result of random selection and mainly of their willingness to participate in the research and not the result of a balanced sampling.

The questionnaire was designed to produce 12 variables including the dependent perceived performance variable. The questionnaire was developed from prior literature and with the assistance of a small group of subsidiary CEOs operating in diverse product groups, particularly regarding the final configuration. Thus, we initially followed a pilot method of formulating the questionnaire, discussing in depth with senior executives whom we knew very well from previous related research. This was important to see how the executives understood our questions, especially those that concerned the central characteristics of the individual laboratories, so that an easy distinction could be made between them. This was important to see how the executives understood our questions, especially those that concerned the central characteristics of the individual laboratories, so that they could be easily distinguished from the respondents.

The questionnaire was administered electronically. We used an electronic survey because it was the most effective way to collect answers from as many subsidiaries as possible. Attempting to approach the subsidiaries in person would be particularly costly and time-consuming due to their geographical dispersion and their relatively high population. At the same time, this procedure gave great time flexibility to the managers as they were not bound by a personal on-site meeting and could answer the questions whenever it was possible for them. When necessary to clarify any questionnaire responses, we conducted face-to-face interviews with the corresponding CEOs.

Following other studies (Qu and Zhang, 2015), non-response bias was assessed by comparing early respondents with late respondents for several variables, including subsidiary performance and technological facilities. None of the differences were found to be statistically significant indicating that non-response bias is unlikely to be a problem.

Table 1 classifies the sample subsidiaries according to whether they have a R&D laboratory, and if so, the kind of laboratory. Table 1 shows that 40 subsidiaries have a locally integrated laboratory to create new products or processes basically for regional or global markets, 10 subsidiaries possess an internationally interdependent laboratory working within global networks to offer basic research inputs on an intra-firm basis, 72 units operate a support laboratory to make minor product and process adaptations to local market needs, while 49 subsidiaries do not have an R&D laboratory. The units with LILS or IILs can be viewed as knowledge-based units, since they have valuable technological facilities that employ highly qualified scientists and technicians in relation to those who work in SLs. The subsidiaries with SLs can be considered as local adapters, whereas the subsidiaries without an R&D laboratory are passive technology players, exclusively depending on the technological support of the corporate group.

Table 2 classifies the sample subsidiaries according to their staffing type and export intensity. As regards staffing, the Table shows the percentage of executives in the fields of management and technology who had worked abroad either in the parent company or in another multinational firm with a duration of more than three years export intensity is measured by the percentage of exports in total sales. It is shown that units with advanced labs such as LILS and IILs far surpass the rest of the sample units based on the international experience of their executives and their export performance.

Table 3 presents the industrial distribution of the sample subsidiaries. As can be seen from the Table, the subsidiaries of the sample come from both traditional branches such as food and beverages, as well as high-tech branches such as machine equipment, electronic and electrical products.

The dependent variable refers to perceived performance (PERFORM) year by year over the investigation period, 2009–2016. Like previous studies (Liu et al., 2016; Singh et al., 2016), we used a perceptual performance measure due to lack of access to accounting-based data. Perceptual performance measurement although perhaps reflecting some subjectivity, enables researchers to understand the interpretation of a firm's performance achievements and goals by managers (Liu et al., 2016). Also, studies have found that management perceptions of firm performance and objective accounting data are broadly similar (Tzafrir, 2005). For the perceptual measure of performance, we asked subsidiary managers to rate on a 5-point Likert scale the performance of: (1) the sales growth, (2) the market share, (3) the profit growth, and (4) the profitability in terms of ROE, with 1 = “very low performance” and 5 = “very high performance”. The correlations between the items all exceeded 0.6. Since these items formed a reliable scale (Cronbach's alpha = 0.82), we averaged their scores into a composite measure of subsidiary performance. As performance indices are critical variables in our study, we tested for common method bias in our dataset, using Harman's Single Factor test. Subsequently, we confirmed the factor structure that we extracted in the exploratory factor analysis through a Confirmatory Factor Analysis indicating that Common Method Variance (CMV) was unlikely to be a problem in our dataset.

We depart from prior research that uses more conventional technological measures such as R&D costs (e.g. Srinivasan et al., 2011; Fang et al., 2013), and incorporate into the analysis technological facilities in terms of R&D labs. From the theoretical analysis but also from our contacts with the top executives, we kept in mind that there is very likely a diverse performance contribution of R&D labs. We used three different types of R&D labs as an indication of diverse innovative activity (Sapouna et al., 2016) that might have differentiated performance effects at MNE subsidiary level. Asking CEOs if they owned any technological laboratory, we concluded that some subsidiaries did not operate an R&D laboratory. So, we ended up with four main explanatory variables: LILs, ILLs, SLs and NoLabs (subsidiaries without labs). These variables are binary. Each time the value 1 represents the selected lab type (e.g. LILs) and the value 0 reflects the remaining three types (e.g. ILLs, SLs and NoLabs). Through the utilization of the variables LILs, ILLs, SLs and NoLabs, we could evaluate the relative performance contribution of the respective labs and thus to test RQ1.

The next critical step was to link these four variables with the other three research questions, i.e. RQ2, RQ3, and RQ4, comparing the sample subsidiaries in pairs. We employed three different combinations, making a comparative performance analysis of the sample units as follows:

• Know1 is a dummy variable coded 1 for units with LILs and 0 for SLs and was used to test RQ2;

• Know2 is a dummy variable coded 1 for units with ILLs and 0 for SLs, and was used to test RQ3;

• Know3 is a dummy variable coded 1 for units with SLs and 0 for units characterized as passive technology players, possessing no labs, and was used to test RQ4.

Subsidiary Age acts as a proxy for the experience of the subsidiary in the local market that might influence business performance positively (Ghauri and Park, 2012; Wang et al., 2005; Fang et al., 2013; Liu et al., 2016; Beugelsdijk and Jindra, 2018). Wang et al. (2005) found that subsidiary experiential knowledge was most significant in picking the winners from the losers during the Asian economic crisis. This variable is measured by the time period between the year of establishment and the year of data collection. It examines whether the length of tenure (associated with the learning curve) increases performance.

Subsidiary Size tests whether economies of scale reinforce performance (Wang et al., 2005; Ghauri and Park, 2012; Fang et al., 2013; Liu et al., 2016; Beugelsdijk and Jindra, 2018). This variable is measured by the total labor force employed by the subsidiary (ln).

Subsidiary capital intensity (Cap) is an indicator of an efficient manufacturing operation and a useful tool for business rationalization and re-engineering in times of economic recession. This variable is measured by the ratio of fixed assets per employee (Georgopoulos and Preusse, 2009).

An industry-specific variable (Industry) is used to capture the performance impact of industries of different technological intensity, in which the sample subsidiaries operate (Chen and Zeng, 2004). This is a dummy variable, coded 1 for the high and medium-high tech industries, and coded 0 for the medium-low and low-tech industries. The classification is based on the glossary of Eurostat Statistics, NACE, rev. 2 3 - digit level.

(https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:High-tech_classification_of_manufacturing_industries).

Table 4 displays the correlations and the descriptive statistics for the variables included in the study. The performance analysis focuses on the explanatory variables, exploring their perceived performance impact within the recession period (Table 5). The ordinary least-squares (OLS) analysis explores whether and how knowledge-based resources in terms of R&D facilities can influence subsidiary performance. We utilized OLS models for perceived performance in the following general form:

In each model, the dependent variable is explained by a set of independent variables (X1). A Hausman test did not confirm the existence of endogeneity between the independent variables and the dependent variable and so the OLS models were appropriate for our performance analysis.

We expect that the explanatory variables that include different comparisons of technological facilities/R&D labs will exercise a differentiated impact on subsidiary performance in difficult times.

Table 4 shows that most of the pairwise correlations of the explanatory variables are low with some exceptions that do not exceed 0.44. This indicates that multicollinearity is not an issue in our study. We also calculated the Variance Inflation Factors (VIFs) for each model to account for potential multicollinearity issues. VIFs values were less than 4.10, and therefore below the cut-off value of 10.00 (Kutner et al., 2004), further indicating that multicollinearity does not affect our findings.

Table 5 shows the OLS estimations over the investigation period and includes five (5) models. The first is the baseline model (control variables). Because the explanatory variables involve different combinations of technology laboratories, we run four (4) different models respectively (columns 2 to 5 of Τable 5). Models 2 to 5 incorporate the explanatory variables LILs, IILs, SLs, and NoLabs. Also, models 3 to 5 integrate the explanatory variables Know1, Know2, and Know3.

The results show that the operation of advanced R&D laboratories have a positive performance impact (Table 5, Models 2 to 5). More specifically, the variables LILs and IILs are statistically significant with a positive sign (p < 0.01 in all four models), clearly indicating their enhancing subsidiary performance effect. There are no differences in the level of significance between the 2 types of laboratories although the first ones show a somewhat higher coefficient. Also, the variable SLs has a positive performance impact however with a much lower level of significance (Table 5, models 2 and 5). The variable NoLabs exercises a negative impact on subsidiary performance (Table 5, p < 0.05 in models 4 and 5, and p < 0.10 in models 2 and 3). These findings exhibit the leading performance role of the two types of advanced laboratories and positively answer RQ1.

In addition, Know1 is statistically significant with a positive sign (Table 5, model 3, p < 0.01) indicating that LILs clearly outperform SLs. This finding positively answers RQ2. Know2 is statistically significant with a positive sign (Table 5, model 4, p < 0.01) indicating that IILs outperform SLs as well. This result positively responds RQ3. Also, SLs provide a better performance outcome compared to subsidiaries that have no laboratories at all (Table 5, model 5, p < 0.10). The specific finding positively answers RQ4.

As regards the control variables of the study, the subsidiary-specific variables Age, and Size are statistically insignificant, whereas Cap is statistically significant in all 5 models (Table 5, p < 0.10), showing the positive performance role of capital intensity in difficult times. The industry-specific variable (Industry) is statistically significant in all 5 models (Table 5, p < 0.10), indicating the enhancing performance role of high-tech industries.

Overall, our results answer positively all four research questions. The findings underline that advanced technological facilities (LILs, IILs) constitute important performance factors and might support the creation of a sustained, technology-based competitive advantage. In addition, SLs also provide a positive contribution to performance in recession when compared to having no R&D laboratories.

This study contributes to the MNE subsidiary performance literature, emphasizing the role of different R&D labs in a suboptimal economic setting, closely allied to the resource-based view and knowledge-based theory. We investigated the perceived performance of a unique sample of 171 heterogeneous subsidiaries located in the Greek economy over the recession period 2009–2016. We distinguish between subsidiaries that operate different types of R&D laboratories. Such labs possess quite different characteristics that substantially contribute to subsidiary heterogeneity (Ghoshal and Nohria, 1989). Resource- and knowledge -based theory suggest that strategic knowledge-oriented resources such as R&D labs determine economic success (Qu and Zhang, 2015; Mallon and Fainshmidt, 2017). Thus, our overarching research question was whether the laboratory type affects the financial performance of MNE subsidiary. The main findings showed that the operation of advanced technological facilities, such as LILs and IILs, strengthened business performance than SLs. Also, the main findings revealed that the deployment of SLs helped to improve performance better than having no technological laboratories at all. Hence, we concluded that MNE subsidiaries with more advanced technological infrastructure performed relatively well during poor economic conditions. These results answer positively all four research questions of the study.

We can give some interpretations for the asymmetric subsidiary performance contribution of the examined R&D labs in the downward phase of the business cycle, which we thoroughly discussed with the top executives CEOS of the sample subsidiaries. The operation of LILs and IILs might guarantee a significant product/process innovation performance (Pearce, 1999; Zhang et al., 2022; Manolopoulos et al., 2007; Sapouna et al., 2016). Further, it may augment absorptive capacity (Fang et al., 2013) increasing the capabilities for resource reconfiguration for the purpose of addressing opportunities and risks in rapidly deteriorating contexts, as is the topic we examined. Also, the operation of advanced labs might comprise an evolutionary learning process leading to a sustainable competitive advantage (Barney, 1991; Wang et al., 2005) and to superior economic performance. Moreover, as compared to SLs, LILs and IILs as “global creators” (Ambos and Schlegelmilch, 2007) may have a strong international orientation and enjoy global internal networking economies in terms of knowledge and information exchange (Wang et al., 2005), which also offer a way out of the severe constraints posed by the local context during a recession. Market-seeking subsidiaries with SLs that work in isolation from other foreign R&D labs of MNEs and interact only with local subsidiary functional departments (Sapouna et al., 2016) are much more exposed to the adverse conditions of the local market. However, even SLs with incremental technological improvements can boost subsidiary performance during recession compared to the scenario of full absence of R&D laboratories (scenario of passive technology players).

As mentioned in the introduction section, there is an abundance of literature dealing with different aspects of R&D and innovation internationalization, multi-disciplinary in nature, and rather fragmented (Du et al., 2022; Vrontis and Christofi, 2021; Papanastassiou et al., 2020) emphasizing innovation performance in terms of new products and processes (e.g. Audretche and Belitski, 2020). Our study joins a distinct emerging stream of literature that exclusively concentrates on research and development laboratories (and not on conventional technological inputs such as research and development expenditures). We directed our research toward this field as MNEs increasingly carry out R&D and innovative activities in foreign locations hence setting up a rising number of R&D labs abroad (Castellani et al., 2013). Research on R&D labs is also quite fragmented (see the introduction section). For instance, Manolopoulos et al. (2007) investigate R&D labs in connection with the respective subsidiary types that own them. Others combine R&D labs with employee issues (Sapouna et al., 2016), or they seek for sourcing mechanisms of labs (e.g. Athreye et al., 2016). To the best of our knowledge, there is still no study on the effect of R&D facilities on MNE subsidiary financial performance. We chose this topic as scholars emphasize the great importance of R&D activity for firms' long-term sustainability, performance (e.g. Du et al., 2022), and productivity due to knowledge spillovers (Audretsch and Belitski, 2020), whereas they highlight the absence of research activity concerning the profitability effect of innovation creation by MNEs in different locations (Kafouros et al., 2022). Our study fills this research gap, revealing that MNE subsidiary performance varies with the different roles of R&D facilities.

The research findings have several implications for managers. Its most important contribution is that it offers valuable insights into innovation management perspectives in turbulent times. Studies coming inter alia from the field of marketing explore the critical question of whether research and development activities should be reduced in periods of recession (e.g. Srinivasan et al., 2011; Steenkamp and Fang, 2011). Obviously, many businesses face this dilemma. In case that management views recession as a threat, activity of R&D labs which may have limited ability to increase short-term cash flows, receives close attention for scrutiny. Thus, management may cut back on R&D investment to reduce operating costs and preserve liquidity. Nevertheless, such a conservative policy risks losing long-term technological advantages. In turn, crisis might provide high-quality units operating advanced labs to outperform competitors with restricted, low-quality R&D activity. Our findings support a positive association between the operation of advanced R&D facilities and subsidiary performance in economic contractions (see also Steenkamp and Fang, 2011) and help MNE management to effectively assess performance perspectives of R&D labs under suboptimal environmental conditions. They suggest that the market normally rewards management actions in a downward economic trajectory that favors superior knowledge-based resource allocation reflected in advanced R&D labs. Moreover, they indicate that the development of such advanced knowledge-related resources is a long-term evolutionary and cumulative learning process that not all subsidiaries can apply. In addition, our research suggests the need for a creative synthesis of innovation management with crisis and change management. Such a synthesis seems to be achieved by developed technology labs that are under the control of units with high absorptive capacity, high ability to restructure assets/resources, and strong global internal networking economies.

As regards policy implications, our research sheds light on governance quality of MNE investment helping policy makers in host countries to use effective evaluation criteria for new foreign investment projects. This is important in the new global economic order characterized among others by new forms of protectionism and new tools of techno-nationalism (e.g. Petricevic and Teece, 2019) suggesting that the presence of national R&D efforts and their effectiveness are key drivers to the overall growth, sustainability, and prosperity of a nation/society. Policymakers must be aware of the strategic importance of attracting foreign investments linked to the development of high-quality technological activities as opposed to those promoting traditional, standardized MNE operations intertwined with a high risk of divestment and relocation to low-cost locations. Consequently, the utilization of our research results can help decision-makers in a selective investment promotion policy with the aim of technological upgrading, unemployment reduction, and economic acceleration.

As with any study, ours has limitations. One concerns the generalizability of the findings as the study focuses on MNE subsidiaries operating in one country, although Greece is deeply integrated in the European economy and shows many structural similarities with other member countries, especially Southern European economies. Further, the investigation concentrated on the input-side of innovation activity (R&D laboratories) and was not able to cover systematically the output-side, reflecting the performance impact of new products and innovative process patterns. Moreover, the application of other methodological tools such as structural equation modeling (SEM) would offer a different, interesting perspective in the analysis of the examined phenomenon shedding light on how its various aspects are causally structurally related to one another. Nevertheless, we hope that our findings will stimulate future research to address subsidiary performance in other integrated national contexts or individual economies with suboptimal and volatile settings, considering multiple knowledge-related resources to help close the research gap in this challenging area.

In summary, this study adds new insights into MNE subsidiary performance, highlighting the role of knowledge-based resources in terms of R&D labs that help to overcome external performance adversities in turbulent economic contexts. In this way, the study places performance analysis in a more demanding, changing environment thus linking research and development considerations with external economic shocks. The study adds to the literature on subsidiary performance in suboptimal and volatile economic settings clearly indicating that the establishment of advanced R&Ds can exercise a positive effect on subsidiary performance during recession.