A model for international production relocation: Multinationals' operational flexibility and requirements at production plant level

Journal of Business Research 77 (2017) 95–101

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A model for international production relocation: Multinationals' operational flexibility and requirements at production plant level

MARK

Jesús F. Lampóna, Pablo Cabanelasb,⁎, Francisco Carballo-Cruzc a b c

Department of Business Management and Marketing, Faculty of Business and Tourism, University of Vigo, Campus As Lagoas, 32004 Ourense, Spain Department of Business Management and Marketing, School of Business Studies, University of Vigo, Torrecedeira 105, 36208 Vigo, Spain Department of Economics, School of Economics and Business (EEG), University of Minho, Campus de Gualtar, 4715-057 Braga, Portugal

A R T I C L E I N F O

A B S T R A C T

Keywords: International production relocation model Location theory Multinational enterprises Operational flexibility Production plant requirements

This paper reviews location theory to formulate a model for international relocation of production. The results highlight the role of internal factors and the appropriateness of the behavioural approach of location theory for explaining international production relocation. From a theoretical standpoint, the operational flexibility of multinational enterprises for transferring resources internationally proposes a transition from an economic geography perspective to a managerial perspective. With empowered multinationals and in a context in which internal factors have a great influence on decision-making, plant level variables gain importance for improving understanding of international production mobility.

1. Introduction The forces driving globalization, such as de-regulation of markets, and advances in information and communication technologies have led to strong processes of international relocation of production, with the corresponding economic and social impacts (Cavusgil, Knight, & Riesenberger, 2008; Farrell, 2005). The extent and consequences of this phenomenon in several industries have been analysed from both political and academic standpoints (Pennings & Sleuwaegen, 2000; Sleuwaegen & Pennings, 2006). It is also a topical phenomenon due to the amount of recent backshoring processes, defined as the return of production to its initial location (Arlbjørn & Mikkelsen, 2014; Kinkel, 2014; Stentoft, Olhager, Heikkilä, & Thoms, 2016). In the academic literature, location theory (Hayter, 1997) is a recurrent background for the study of relocation. This theory, which was formulated and developed in economic geography and spatial economics, aims to explain the forces that push a firm from its current location to an alternative (optimal) one. Various models for relocation processes have been formulated based on the neoclassical, institutional and behavioural approaches of location theory (Brouwer, Mariotti, & Van Ommeren, 2004; Hayter, 1997), with location and external and internal factors as explanatory variables (Holl, 2004; Knoben, 2011; Knoben & Oerlemans, 2008; Van Dijk & Pellenbarg, 2000). Although these contributions have allowed us to find out more about the motivations for relocation, from a managerial and interna-

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tional perspective they have two major shortcomings. First, they overlook the specificities of multinational enterprises (MNEs), particularly their operational flexibility which allows them to transfer resources and capacities internationally (Beugelsdijk & Mudambi, 2013; Dasu & Li, 1997; Kogut & Kulatilaka, 1994). Greater attention should therefore be paid to parent companies' internal strategies in the framework of international relocation. Second, relocation studies have given little relevance to factors at production plant level. So, from the theoretical point of view, this study contributes to an open debate on the various trends in location theory for explaining international production relocation. Internal factors within MNEs have gained weight over external or location factors in decision-making. This is in line with the principles of the Resource Based View, which explain corporate differences mainly on the basis of internal factors. A second contribution lies in the use of the production plant as a new level of analysis. This could complement the parent company perspective allowing for the inclusion, among others, of plant-level requirements in relocation decisions. The combination of both contributions gives a predictive model for relocation based on operational flexibility in the parent company and plant-level factors. The model is tested using a new and thorough database with information on firms in the automobile parts manufacturing sector, created specifically for this research. The choice of this sector is justified by its great worldwide impact in terms of production and employment and because it involves very heterogeneous products, processes, technologies and supply chain conditions, and includes many

Corresponding author. E-mail addresses: [email protected] (J.F. Lampón), [email protected] (P. Cabanelas), [email protected] (F. Carballo-Cruz).

http://dx.doi.org/10.1016/j.jbusres.2017.04.007 Received 5 August 2016; Received in revised form 7 April 2017; Accepted 8 April 2017 Available online 19 April 2017 0148-2963/ © 2017 Elsevier Inc. All rights reserved.

Journal of Business Research 77 (2017) 95–101

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3. Towards a model for international production relocation: hypotheses

multinationals that are highly internationalised in terms of both production plants and consumer markets. The paper is organized as follows. The different approaches of location theory for explaining relocation are reviewed in Section 2. Section 3 reviews the relevant literature and states the corresponding hypotheses. The sample of production plants analysed is described, and the variables used in the empirical exercise are defined in Section 4. Section 5 analyses the data, and presents and discusses the empirical results. Finally, Section 6 presents the main conclusions and suggests several recommendations for both company managers and policy makers.

3.1. The parent company's operational flexibility The literature has shown that MNEs have a prominent role in international relocation processes (Barba, Falzoni, & Turrini, 2001; Belderbos & Zou, 2006; Buckley & Mucchielli, 1997; Konings & Murphy, 2006). Particularly, MNEs' operational capabilities have been identified as a key driver for achieving higher levels of performance (Tan, Kannan, & Narasimhan, 2007); managers thus allocate resources and capabilities to the areas that can contribute most to improving the company's outcomes. Operational flexibility therefore becomes a strategic tool, as it allows MNEs to coordinate and transfer resources internationally (Dasu & Li, 1997; Huchzermeier & Cohen, 1996; Kogut & Kulatilaka, 1994). Such flexibility explains MNEs' production configurations more efficiently than specific location advantages (Buckley & Casson, 1998; Fisch & Zschoche, 2012; Lo & Lin, 2015), because it allows for international transfers and better adaptation to environmental conditions (Chung, Lee, Beamish, & Isobe, 2010; Kogut & Chang, 1996). Production restructuring strategies such as specialisation, concentration or rationalisation allow MNEs to improve their operational flexibility and therefore to optimise their production configuration. They also force firms to change their organisational and spatial structure, and may lead to total or partial relocation of some of their activities within their production network. According to the behavioural approach, some internal characteristics of MNEs, such as the configuration of their international production network, their size or presence in a large number of countries may increase their operational flexibility for transferring activities internationally (Allen & Pantzalis, 1996; Tong & Reuer, 2007). The more alternative plants an MNE has in other countries, the easier it will be for it to transfer activities and the more likely it will be to relocate production among its own plants. Therefore, the internal resources and capabilities a MNE possesses determine its operational flexibility and its capacity to transfer resources internationally. In this context, external and location factors become relevant to the extent that they can be internalized in the firm's production strategies. For example, a competitive advantage between countries in terms of labour costs may be a key factor in relocation decisions if the firm bases its production strategy on labour-intensive activities (Lampón, Lago-Peñas, et al., 2015). Thus, the first hypothesis is:

2. Theoretical background Location theory has a significant concern with the identification of optimal locations and the understanding of the main forces that motivate spatial relocations (Hayter, 1997). Three different approaches of location theory analyse relocation: neoclassical, institutional and behavioural (Brouwer et al., 2004). Each one uses different factors to explain relocation, ranging from location factors related to the physical place where the firm carries out its activity, to external factors associated with the environment in each country or region and internal factors within each individual firm. The neoclassical approach explains relocation as a process in which firms aim to maximize profits; the explanatory models based on this approach consider ‘location’ factors as the main motivation for relocation (Fujita, Krugman, & Venables, 1999). Such location factors include the specific characteristics of the physical place or the surrounding area where the plant is located. The literature on this topic emphasizes local agglomeration economies such as the availability of service providers and a large industrial basis (Cuervo-Cazurra, de Holan, & Sanz, 2014; Holl, 2004; Lee, 2006) or the presence of workers in the same industry (Hong, 2014), the existence of infrastructure facilities such as interregional motorways (Holl, 2004), or a favourable local environment (Cuervo-Cazurra et al., 2014). The institutional approach focuses on the ‘external’ factors, that is, the social determinants and specific values of a given region (Amin, 1999). Specifically, the external factors covered in the literature are low production costs, especially labour costs (Antras & Helpman, 2004; Cordella & Grilo, 2001), and the size of the potential market (Holl, 2004; Sleuwaegen & Pennings, 2006). Studies linking relocation to external factors have a significant explanatory capacity as they focus on the comparative advantages of different countries and regions, mainly in terms of production costs. Finally, the behavioural approach considers the location of production plants as a part of the decision-making process, in which ‘internal’ factors may lead to a sub-optimal rather than an optimal location from the neoclassical point of view (Hayter, 1997). This approach aims to understand managers' behaviour when facing relocation decisions and the factors involved in such decision processes, namely relocation costs (McCann, 2001), the economic capacity of the firm for financing relocation (Caves, 1996; Pennings & Sleuwaegen, 2000) or managerial strategies (Chan, Gau, & Wang, 1995; Maskell, 2001; Van Vilsteren & Wever, 1999). The behavioural approach is very close to the Resource Based View, which considers internal resources and capabilities to be the main predictors of a firm's behaviour and performance (Barney, 1991; Becerra, 2008). Indeed, these may become the main drivers of decision making (through strategic decisions) but also the main barriers to relocation, as they may be linked to a given location due to specific previous investments (Dyer, 1996).

H1. The operational flexibility of MNEs in terms of network size and corporate restructuring strategies is a significant factor in international production relocation. 3.2. The relevance of requirements at production plant level In order to explain why, within the production network of a given MNE, some plants are relocated and others are not, factors at the production plant level should be analysed. Although location theory has stressed the importance of these requirements (Hayter, 1997; Pellenbarg, Van Wissen, & Van Dijk, 2002), studies on relocation have traditionally disregarded analyses at plant level. Nevertheless, according to the behavioural approach of location theory it seems advisable to combine traditional factors with MNEs' internal issues and decisionmaking processes. This should allow identification of plant-level requirements, the so-called keep factors (Pellenbarg et al., 2002), which may be financial or organisational nature and favour or limit production relocation (Van Dijk & Pellenbarg, 2000; Zenka, 2009). They do not, however, exclusively explain relocation propensity.

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European restructurings), resulting in a total of 38. Then each of the cases identified was validated through direct contacts with the firms' managers, and any cases in which validation was not possible were dropped from the study. The final number of relocated plants in the period with complete information for the analysis was 33. This represents an annual relocation rate of 1.44%, similar to the rate found in other studies on relocation (Artís, Ramos, & Suriñach, 2007; Brouwer et al., 2004; Sleuwaegen & Pennings, 2006). These relocations led to the loss of 9300 jobs in multinational groups' plants in Spain over the timespan of the analysis. In 28 out of the 33 cases, all production was relocated and the plant was closed down, and in the remaining 5 the relocation was partial, affecting just one of the products or a part of the production process, with the remaining activity continuing as before.

The behavioural approach stresses the importance of limited information and uncertainty, especially regarding internal costs, in relocation decisions (Clark & Wrigley, 1997). Internal relocation costs include among others the dismantling, moving, and reconstruction of facilities or the hiring and training of the workforce (McCann, 2001). Thus, among production plant processes, production and logistic routines gain relevance in the assessment of internal cost. From a production plant perspective, greater technological complexity of processes leads to a greater need for skills, capabilities and knowledge (Danese & Vinelli, 2009; Guilhon, 1992) and, therefore, to higher costs and requirements for industrialisation and quality assurance in the case of transfer. Additionally, complex processes are capital-intensive and require significant investments in facilities. On the other hand, standard production processes, with low levels of technological complexity, are easy to replicate and transfer (Camuffo, Furlan, Romano, & Vinelli, 2006; Dicken, 2003; Nassimbeni & Sartor, 2006). With regard to logistics, internal transport costs are also important in the production location because they affect supply conditions (Puga, 2002). And from a managerial perspective, the complexity of logistic conditions has a significant impact on transport costs (Cox, 2001). Short lead times, pull ordering, minimal inventories and small and frequent deliveries make the link between production and location particularly relevant (De Toni & Nassimbeni, 2000; González-Benito & Spring, 2000). With complex supply systems, the geographical distance between customer and supplier may entail a significant increase in transport costs (Vonderembse, Tracey, Tan, & Bardi, 1995). Firms do not always consider this distance to be a barrier when adopting such supply systems (Das & Handfield, 1997; Wafa, Yasin, & Swinehart, 1996): load consolidation and the use of buffer warehouses have been the most widely-used solutions for overcoming the drawbacks of the lack of proximity (Handfield, 1993; Miemczyk & Holweg, 2004). However, as complex supply conditions involve a change in internal transport costs and have strategic implications, they may affect the relocation of production activities. Therefore, the second hypothesis is as follows.

4.1.2. Non-relocated plants The AMADEUS database was used to determine the universe of nonrelocated plants. The selection only includes plants located in Spain, classified as Motor Vehicle Parts and Accessories (SIC 3714) and belonging to MNEs whose production was not relocated (either partially or fully) in the 2001–2008 period. 254 plants met these criteria. After the individual contacts with managers, data on 153 plants was obtained, with a response rate of 60.2% (sample error + 5.01%, for a confidence level of 95%, considering equal population proportions of the characteristics under analysis). 4.2. Variables Empirical studies on relocation have generally resorted to manager's opinions to evaluate objective facts. In many of them the reasons for relocation are often ranked by using the percentage of responses obtained. Such opinions are frequently biased by many cognitive factors (Sudman, Bradburn, & Schwarz, 1996) so they generate measurement errors that affect both the validity and the reliability of the models (Bertrand & Mullainathan, 2001). In order to avoid the shortcomings of studies based on primary data collected by questionnaire, in our empirical application a combination of primary and secondary data is used to test an econometric model of relocation. The variables employed in the empirical exercise are the following: Location factors

H2. Technological and logistical requirements at production plant level condition international production relocation. 4. Research methodology

– Infrastructure facilities: the main spatial effect of transport infrastructure proximity is firm densification in the vicinity of the infrastructure. There is evidence that the impacts of road infrastructure depend on the distance between the transport network and the municipality (Chandra & Thompson, 2000; Holl, 2004). This variable is calibrated as the distance from the host municipality (where the plant is located) to the nearest inter-regional motorway, using Geographic Information Systems (GIS). – Agglomeration economies: Employment within the same industry measures the level of specialisation, which in turn reflects the externalities associated with being located close to other producers in the same industry (Hong, 2014; Lee, 2006). The variable is calibrated as employment in the automobile parts manufacturing sector (SIC 3714) divided by employment in the whole industry within the municipality. The data source is AMADEUS.

4.1. Data The database used for testing the model includes a set of firms in the automobile parts manufacturing sector (Standard Industrial Classification - SIC 3714) in Spain. The automobile parts sector is an essential industry, especially in terms of employment (205,000 jobs in 2015). The value of the parts production sector amounted to €32 billion in 2015, placing Spain in third position in Europe and sixth worldwide. Over the last decade, firms have seen far-reaching changes in their size and situation within the sector's value chain. Nowadays, plants belonging to MNEs are operating at the first and second levels of supply and dominate production and technological activities in the sector. Taking into account the research goals and the level of empirical analysis, two samples of automobile parts manufacturing plants were created: one including plants that between 2001 and 2008 relocated all or part of their production (relocated plants), and another comprising plants that in that period did not relocate activities (non-relocated plants).

External factors – Market potential: The size of the market of host countries is one of the main pull factors of relocation (Sleuwaegen & Pennings, 2006). This variable is defined as a dummy that takes the value 1 if the plant is relocated to a country with a larger market than the Spanish one, and 0 if it is relocated to a country with a smaller market or if it is not re-located at all. In order to determine if the market is larger or smaller than the Spanish one, two sources of data are used. If the production plant is a first level supplier (Tier 1), the database of the

4.1.1. Relocated plants Construction of the population of relocated plants started out with a laborious process of analysis and identification of relocation processes in several sources of information (scientific literature, sector reports and studies, public and private surveys on relocation and databases on 97

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5. Analysis and discussion of results

Organisation Internationale des Constructeurs d'Automobiles (OICA - www.oica.net/category/production-statistics/) is employed; if the production plant is a second level supplier or less (Tier 2, Tier 3 …), data on auto parts consumption from Global Insight (www.ihs.com/ industry/automotive.html) is used. – Low wages: This variable is defined as a dummy that takes value 1 if the plant is relocated to a country with lower labour costs and 0 in all other cases. In order to assess if the labour cost of the new host country is higher or lower than the Spanish one the ILOSTAT database of the International Labour Organisation (ILO) on manufacturing wages is used. This database gathers labour market statistics for over 100 indicators and 230 countries.

5.1. Econometric analysis The following econometric specification is estimated: Relocationi = b0 + b1 × Infrastructure facilitiesi + b2 × Agglomeration economiesi + b3 × Market potentiali + b4 × Low wagesi + b5 × Corporate restructuringi + b6 × Alternative plantsi + b7 × Logistical requirementsi + b8 × Technological requirementsi + ei The specification includes in the same equation the main drivers of international relocation: location, external and internal factors (including the parent company and production plant-level factors). The specification allows us to observe and compare the explanatory capacity of each of them in international relocation processes. The dependent variable, Relocation, shows a binary response (0/1; nonrelocated plant/relocated plant). The corresponding logit model is estimated by using a maximum-likelihood estimator. In order to avoid scale issues in the variables, all of them were standardized. The results found are reported in Table 3. The variables Infrastructure facilities and Agglomeration economies, used as location factors, are not significant; the same occurs with the external factor that captures the market size effect, Market potential, but not with the other external factor, Low wages, although it shows a low level of significance (p < 0.1). On the other hand, the variables related to internal factors are highly significant: Corporate restructuring and Alternative plants (parent company level), and Logistical requirements and Technological requirements (production plant level). As shown in Table 4, the model offers a very good predictive capacity, especially regarding the characteristics of the firms that maintain their production in Spain. The global percentage of firm's behaviour predicted with the model reach 91.4%.

Internal factors – Corporate restructuring: measures the intensity of corporate restructuring strategies recently implemented by the firm (Dunning, 1993). This is defined by the number of European plants the firm has that were involved in processes of concentration, specialisation or rationalisation of capacities in the last 3 years over the total number of the firm's European plants. The variable is calculated using information gathered from the European Restructuring Monitor (ERM). This database covers a significant proportion of production restructuring cases in Europe, involving an increase or a loss of at least 100 jobs or affecting at least 10% of the workforce in plants with > 250 employees. – Alternative plants: reflects the size of the firm's international production network, measured as the number of plants in other countries (Allen & Pantzalis, 1996). The research technique used for obtaining this information was a survey purposely conducted for this research.1 – Logistical requirements: a dummy variable that takes the value 1 if the plant operates under complex supply requirements and 0 otherwise. These supply requirements are imposed by a pull supply system (synchronization or Kanban) or by multi-day delivery frequency (Gonzalez-Benito, 2002). This information was also obtained from the survey. – Technological requirements: it encloses the requirements of the plant's production process in terms of technology, skills and quality. It includes the extent and level of the technologies used in the production process and the difficulty of meeting quality requirements (Lampón, González-Benito, et al., 2015): Technological requirements = [Number of production process technologies] × [Skill intensity] × [Quality requirements] Skill intensity = senior engineers and graduates in the total plant staff Quality requirements = Employees in quality functions in the total plant staff The information employed for calibrating the variable was obtained from the same survey.

5.2. Discussion of results According to the results, international production relocation mainly relies on internal factors associated with both parent company flexibility and production plant requirements. One of the main internal motivations for international relocation is implementation by the parent company of restructuring processes in the MNE as a whole, aiming to achieve efficient configuration of its production network and better adaptation through flexibility in decision-making. Such processes involve strategies such as specialisation, concentration and rationalisation to optimise production capacities. The results show, firstly, that the lack of certain specific location factors in the initial place does not act as a driver for international relocation. In fact, the variables close to the neoclassical approach that are used as location factors, Infrastructure facilities and Agglomeration economies, do not condition international mobility of production. Secondly, regarding external factors, the result obtained for Market potential shows that the size of the host country market is not a driver for relocation, in contradiction with suggestions in the literature on production relocation (Holl, 2004; Sleuwaegen & Pennings, 2006). However, lower labour costs in other countries is a pull factor in relocation, although this variable has less explanatory power than MNEs' internal restructuring strategies. This external factor is only relevant for MNEs that are geared towards labour-intensive production. Thirdly, the results confirm that aspects associated with internal decision-making processes in MNEs, especially those relating to operational flexibility, exert a great influence on the international relocation of their production plants. The organisational and spatial changes involved in corporate restructuring processes help to explain the relocation of many plants. Additionally, a larger firm network increases its flexibility for coordinating and transferring resources internationally and favours international relocation. These results highlight the importance of MNEs' operational flexibility and internal factors in explaining international production relocation. Hypothesis H1 can

Table 1 lists the variables included in the model, the level of analysis corresponding to each of them and the data sources employed. Table 2 shows the basic descriptive statistics and the linear correlations between variables. Multicollinearity between regressors is not a serious concern.

1 These variables and the qualitative information on relocation come from in-depth interviews with plant managers. This empirical work was performed by the authors under the project PGIDIT05CSO3002PR: ‘Identification of international relocation factors in the Spanish automobile parts industry’, financed by the Innovation and Industry Department of the Galician Government. The final version was the result of a collaboration process between the University of Vigo (Spain) and the University of Minho (Portugal).

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Table 1 Variables, definition, level of analysis and data sources. Factor

Variable

Definition

Level of analysis

Source

Location

Infrastructure facilities

Distance from the municipality where the plant is located to the nearest inter-regional motorway In the host municipality: [Employment in the automobile parts manufacturing sector (SIC 3714)]/[Employment in the manufacturing industry] Dummy: takes the value 1 if the plant is relocated to a host country with a larger market than the Spanish one and 0 otherwise Dummy: takes the value 1 if the plant is relocated to a country with lower labour costs than the Spanish ones and 0 otherwise [Number of plants of the MNE involved in processes of production restructuring in the last 3 years]/[Total number of plants of the MNE] Number of plants of the firm located in other countries Dummy: takes the value 1 if the plant operates under lean supply (pull supply system or multi-day delivery frequency) and 0 otherwise [Number of production process technologies] × [Skill intensity] × [Quality requirements]

Municipality

Geographic Information Systems (GIS) AMADEUS Database

Agglomeration economies External

Market potential Low wages

Internal

Corporate restructuring Alternative plants Logistical requirements Technological requirements

Municipality Country

OICA Production Statistics/ Global Insight ILOSTAT Database

Country Parent company Parent company Plant

European Restructuring Monitor (ERM) Survey Survey

Plant

Survey

Table 2 Descriptive statistics and correlations between variables.

(1) (2) (3) (4) (5) (6) (7) (8)

Infrastructure facilities Agglomeration economies Market potential Low wages Corporate restructuring Alternative plants Logistical requirements Technological requirements

Mean

S.D.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

13.6 0.0776 0.04 0.13 0.0303 13.9 0.35 0.0171

8.15 0.04121 0.191 0.389 0.06631 14.77 0.478 0.04269

1 0.021 − 0.012 0.099 − 0.045 0.007 − 0.137 − 0.061

1 − 0.137 − 0.033 − 0.006 − 0.025 0.051 − 0.075

1 0.133 0.154⁎ 0.076 −0.086 0.001

1 0.203⁎⁎ 0.179⁎ − 0.193⁎⁎ − 0.248⁎⁎

1 0.123 −0.161 −0.027

1 0.086 − 0.068

1 0.099

1

Pearson correlation coefficient between pairs of quantitative variables and Spearman correlation coefficient between pairs of variables in which one of them is qualitative. ⁎ p < 0.1. ⁎⁎ p < 0.05. Table 3 Summary of the results of the logistic model regression.

Table 4 Classification of the logistic model.

Variables

Coefficient (standard error)

Infrastructure facilities

0.169 (0.238) − 0.250 (0.256) 4.409 (2615.575) 0.459⁎ (0.257) 0.564⁎⁎⁎ (0.221) 1.031⁎⁎⁎ (0.270) − 1.162⁎⁎⁎ (0.452) − 4.679⁎⁎ (2.383) 186 91.4 0.359

Agglomeration economies Market potential Low wages Corporate restructuring Alternative plants Logistical requirements Technological requirements Number of observations Predictive capacity (%) Pseudo-R2

Observed

Predicted Relocation

Relocation

Non-relocated plant Relocated plant Global percentage

Predicted percentage

Non-relocated plant

Relocated plant

151

2

98.7

14

19

57.6 91.4

Similarly, the results obtained for Technological requirements and Logistical requirements also support the relevance of internal factors; but they require a separate analysis. At plant level, such requirements act as inhibitors of international relocation processes. The results demonstrate that the inclusion of production plant factors in the explanation of international production relocation helps to clarify such processes. Only by analysing specific factors at the production plant level is it possible to find relocation barriers and to better understand the mechanisms that determine why within a given MNE some plants are relocated while others are not. These results confirm research hypothesis H2. This outcome has an important implication for future research on international production relocation, because it emphasizes the relevance of including production plants as a unit of analysis. This level of analysis allows for the identification of the financial or organisational keep factors that are highlighted in the literature as relocation barriers (Pellenbarg et al., 2002; Van Dijk & Pellenbarg, 2000; Zenka, 2009).

Standard errors in brackets. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

therefore be accepted. The behavioural approach thus becomes the most suitable approach in location theory for understanding international production relocation, and its connection with the assumptions of the Resource Based View of the firm can help identify internal factors as the main drivers of decision-making in firms, instead of environmental or external issues. 99

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6. Conclusions After analysing the traditional approaches of location theory, we conclude that internal factors influencing MNEs' decision-making processes have a highly relevant role in explaining international production relocation processes. Particularly, the operational flexibility of MNEs for transferring resources internationally becomes a key factor in such processes. The model proposed addresses a new level of analysis, the production plant. This allows for more precise identification of the internal barriers to relocation within a MNE's production network. From a theoretical perspective, the results obtained propose a transition from an economic geography perspective to a managerial perspective for explaining the international relocation of production. Reinforcement of the behavioural approach for predicting international production relocation conflicts with previous works that sustain the high explanatory power of the neoclassical and institutional approaches (Brouwer et al., 2004; Knoben, 2011). The internal resources and capabilities (core constructs in the Resource Based View) that characterise MNEs become key in relocation decisions in the firms analysed. This theoretical view could also help explain recent trends in production relocation, especially the “backshoring” phenomenon, that is, the (re)relocation of activities from foreign locations back to the initial domestic location (Kinkel & Maloca, 2009). The literature points out some strong arguments for (re)-integrating production capacities in the initial location via backshoring, namely the quality of processes, flexibility or ability to deliver on time, coordination and monitoring costs and availability of qualified personnel (Broedner, Kinkel, & Lay, 2009; Kinkel, 2012). The factors identified in processes of backshoring are related to firms' internal factors. This thus reinforces our findings and suggests that a MNE that is not interested in backshoring should prioritize internal factors in its decision on production relocation; otherwise, the likelihood of backshoring will be higher. Our results have implications for both business managers and public policy decision-makers. Managers with responsibilities in MNEs' international restructuring should include plant-level factors in their decision-making processes. The benefits of relocation – e.g. economies of scale and scope – may be lost if the relocation costs of each plant are not accurately evaluated and addressed. Managers should focus on the global optimum of the whole production network instead of local optima at different locations. Additionally, the results partially support previous findings that point to the limited importance of public policies for explaining plant relocation decisions and their patterns (Lee, 2008; Sleuwaegen & Pennings, 2006; Van Dijk & Pellenbarg, 2000). Regional and national governments have a decreasing influence on MNEs' relocation decisions. Large firms' decisions are mostly motivated by corporate criteria, so public aid to either favour or avoid plant relocations should be re-evaluated, given its limited effectiveness. This paper has some limitations that could be mitigated in future research. Although the model proposed has a good predictive capacity to analyse the international relocation of production, future research could include other internal factors in order to improve its predictive capacity. The model could also be applied in other manufacturing industries to assess its appropriateness and validity and also to study the current backshoring processes. References Allen, L., & Pantzalis, C. (1996). Valuation of the operating flexibility of multinational operations. Journal of International Business Studies, 27(4), 633–653. http://dx.doi. org/10.1057/palgrave.jibs.8490147. Amin, A. (1999). An institutional perspective on regional development. International Journal of Urban and Regional Research, 23, 365–378. http://dx.doi.org/10.1111/ 1468-2427.00201. Antras, P., & Helpman, E. (2004). Global sourcing. Journal of Political Economy, 112, 552–580. http://dx.doi.org/10.1086/383099. Arlbjørn, J. S., & Mikkelsen, O. S. (2014). Backshoring manufacturing: Notes on an important but under-researched theme. Journal of Supply Chain Management, 20(1),

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Jesús F. Lampón is an industrial engineer, PhD in Business Administration and a professor of Business Organization at the University of Vigo. He has been leader researcher in more than fifty research projects with different institutions and firms. His research is focused on automotive industry and has been published in several journals such as International Journal of Production Research, Papers in Regional Science, Production Planning and Control, Regional Studies or Journal of Business & Industrial Marketing. He was a manager of the Program to Promote Business Research and Innovation in the Economy and Industry Council of the Galicia Government (Spain). Pablo Cabanelas, PhD in Business Administration, is an assistant professor in Marketing and Director of the Master in International Commerce at the University of Vigo (Spain). His current research interests are networks, competitiveness and regional development, and industrial marketing. He has participated in several research projects with different regional and national institutions and with firms. His research has been published in several books and journals such as Industrial Marketing Management, Regional Studies, Journal of Business & Industrial Marketing, Production Planning and Control, Total Quality Management & Business Excellence or Papers in Regional Science. Francisco Carballo-Cruz is professor in the Department of Economy and researcher in the Núcleo de Investigação em Políticas Económicas (NIPE) from University of Minho. His interests are Public Economy, Regional Economics and Global Economy. He directed the master in International Business in the University of Minho and have also participated in several projects about public policies and internationalization. He belongs to the direction board of different organizations, Tecminho and Eures G-NP and Adrave, and associations, European Regional Science Association (ERSA) and Regional Science Association International (RSAI).

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