Understanding the manufacturing reshoring decision-making content through the lens of the Eclectic Paradigm: a systematic literature review

Abstract

Purpose

This study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective.

Design/methodology/approach

Data were collected through a six-step systematic literature review on factors influencing manufacturing reshoring decision-making. The review is based on 100 peer-reviewed journal papers discussing reshoring decision-making contents published from 2009 to 2022.

Findings

In total, 80 decision factors were extracted and then categorized into resource-seeking (8%), market-seeking (11%), efficiency-seeking (41%) and strategic asset-seeking (16%) advantages. Additionally, 24% of these were identified as hybrid, which means that they were classified into multiple categories. Some decision factors were further identified as reshoring influencing factors (i.e. drivers, enablers and barriers).

Research limitations/implications

Scholars need to consider what other theories can be used or developed to identify and evaluate the decision factors (determinants) of manufacturing reshoring as well as how currently adopted theory can be further advanced to create clearer and comprehensive theoretical frameworks.

Practical implications

This research underscores the importance of developing clearer and more comprehensive theoretical frameworks. For practitioners, understanding the multifaceted nature of decision factors could enhance strategic decision-making regarding reshoring initiatives.

Originality/value

To the best of the authors’ knowledge, this is the first study to investigate the value and practicality of the Eclectic Paradigm in categorizing factors in manufacturing reshoring decision-making content and presents in-depth theoretical classifications. In addition, it bridges the gap between decision factors and influencing factors in the decision-making content research realm.

Citation

Li, H., Hilletofth, P., Eriksson, D. and Tate, W. (2024), "Understanding the manufacturing reshoring decision-making content through the lens of the Eclectic Paradigm: a systematic literature review", European Business Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EBR-10-2023-0300

1. Introduction

Two dominant management philosophies in the past two decades have been to focus on key processes in the company’s value creation, while moving other processes to external partners (outsourcing) and moving processes to foreign low-cost countries (offshoring). More lately, challenges in global sourcing as well as market developments have encouraged discussions related to the drawbacks of this global approach. Hence, a shift in management philosophy towards reshoring has occurred, where firms locate their manufacturing activities closer to the final market (Boffelli and Johansson, 2020; Wiesmann et al., 2017). This change in mindset has occurred in many industries for many different reasons, including increased requirements on customization, responsiveness and sustainability (Gillani et al., 2023; Hilletofth et al., 2019; Johansson et al., 2019). The new mindset has changed how companies view manufacturing location decisions. Going from a position where offshoring and outsourcing have been seen as universal solutions to improve competitiveness, reshoring and insourcing are gaining ground (Barbieri et al., 2018; Engström et al., 2018B; Wiesmann et al., 2017). This development has been further strengthened by the COVID-19 pandemic which has highlighted the vulnerability of global supply chains and in many ways undermined the global approach (Barbieri et al., 2020; Phillips et al., 2022).

Manufacturing reshoring has received a great deal of attention in recent years from scholars, practitioners and policymakers (Di Mauro et al., 2018; Dachs et al., 2019a, 2019b). This research topic is in its infancy but is growing very rapidly (Boffelli et al., 2021; Pegoraro et al., 2022). The available literature includes three main research streams: reshoring decision-making (e.g. Engström et al., 2018A, 2018B; Sequeira et al., 2021), reshoring implementation (e.g. Boffelli et al., 2018; Lund and Steen, 2020; Zhao and Huchzermeier, 2017) and reshoring outcomes (e.g. Johansson et al., 2019; Uluskan et al., 2017; Stentoft et al., 2018). The decision-making stream has received the most attention up till now. Most of the research in this stream has focused on identifying and understanding the factors that are considered in decision-making (i.e. decision factors) (Johansson and Olhager, 2018; Mohiuddin et al., 2019; Presley et al., 2016) and the factors that are triggering, facilitating or impeding (i.e. influencing factors) manufacturing reshoring (Johansson et al., 2019; Lund and Steen, 2020; Sirilertsuwan et al., 2019).

Understanding the decision-making content is critical to make well-informed decisions (Engström et al., 2018A; Stentoft et al., 2018). To create robust knowledge, it is important to adopt a theory-based view (Boffelli et al., 2020; McIvor and Bals, 2021; Moradlou et al., 2021), as a theoretical lens helps shape data analysis and facilitates deeper finding interpretation. The current research on manufacturing reshoring decision-making content is, in most cases, lacking a clear theoretical lens. The Eclectic Paradigm (Dunning, 1973, 1988, 1998) is commonly used for analysing the determinants of international manufacturing. It classifies decision factors into efficiency-seeking, resource-seeking, market-seeking and strategic asset-seeking. So far, only four manufacturing reshoring studies have used this lens to identify and understand decision factors (Ancarani et al., 2015; Barbieri et al., 2019; Ellram, 2013; Moradlou et al., 2021). Given the lack of theoretical foundation and the potential benefit of the Eclectic Paradigm, there is a need to investigate decision factors using this lens and discuss the theory’s value and how it applies to manufacturing reshoring decision-making. Even if it has been noted that a decision factor also may constitute a driver, barrier or enabler of manufacturing reshoring (Engström et al., 2018A; Sirilertsuwan et al., 2019), current research tends to investigate decision factors and influencing factors separately, thus missing the opportunity to develop a deeper understanding which is important for this complex decision-making. Several scholars argue that this topic requires further investigation (e.g. Barbieri et al., 2018; Moretto et al., 2020).

To address these research gaps and provide insightful knowledge, this study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective. Three research questions are formulated:

RQ1.

What are the main decision factors in manufacturing reshoring decision-making?

RQ2.

Which decision factors constitute a driver, barrier or enabler of manufacturing reshoring?

RQ3.

How applicable is the Eclectic Paradigm to manufacturing reshoring decision-making?

The study adopted a theory refinement systematic literature review on 100 peer-reviewed journal papers published from 2009 to 2022. The synthesis was carried out in three phases. In the first phase, decision factors were extracted and categorized into resource-seeking, market-seeking, efficiency-seeking and strategic asset-seeking based on how they were addressed in their original article. In the second phase, influencing factors were extracted and categorized according to the identified main decision factors. In the final phase, the applicability of the Eclectic Paradigm in manufacturing reshoring decision-making was investigated by looking at the clearness (i.e. factors are easily categorized into one group) and comprehensiveness (i.e. all relevant factors are included) of the theory.

The remainder of the paper is organized as follows. To begin with, a theoretical background and the conceptual framework framing the deductive systematic literature review are presented in Section 2. Then, the research methodology is outlined in Section 3. Thereafter, the research findings are presented in Section 4, followed by an in-depth concluding discussion of the findings, contributions, implications and future research in Section 5.

2. The eclectic paradigm – framework development

Dunning proposed the ownership, location and internalization (OLI) parameters in 1976. This initial version of the Eclectic Paradigm was used to uncover the underlying motives that influence manufacturers’ production decisions. To test the validity of the OLI parameters, Dunning conducted empirical studies in 1980. Later, the ownership advantage was further divided into the asset (Oa) and transaction (Ot) advantages of international companies in 1983. Dunning then proposed three main forms of international manufacturing: market-seeking, resource-seeking and efficiency-seeking in 1988. With the dynamic global market situation, the next version of the Dunning (1998) highlights the growing importance of intangible assets (i.e. knowledge-intensive assets) in the wealth-creating process. In this version, the Eclectic Paradigm emphasized the changing role of company-specific location-bound assets and added “strategic asset-seeking” as an important motive of international manufacturing. Although this version was developed to explain the expansion of international companies, as Fratocchi (2018) pointed out, it has also been applied to the manufacturer’s global reconfiguration (e.g. offshoring and reshoring). Most recently, Dunning compared the Eclectic Paradigm with other international manufacturing theories and answered some criticisms of the theory in 2000 and 2001. The development of the Eclectic Paradigm is summarized in Table 1.

2.1 Decision factors in the Eclectic Paradigm

According to the Eclectic Paradigm, the OLI parameters determine international manufacturing activities. Based on the OLI parameters, companies conduct international reconfiguration to seek four types of advantages (Table 2):

1. Resource-seeking: access resources that are not available in the current location or that are available at a lower cost in a particular location.

2. Market-seeking: exploit market opportunities in a particular location.

3. Efficiency-seeking: explore cost-efficient and productivity-enhancing manufacturing in a particular location.

4. Strategic asset-seeking: access strategic/knowledge-related assets that are not available in the current location or that are enhanced in a particular location.

The Eclectic Paradigm is a framework for analysing the determinants of international manufacturing, but only four manufacturing reshoring studies have used this lens to identify and understand decision factors (Table 3). Ellram (2013) was the first researcher to explore the manufacturing reshoring decision using the Eclectic Paradigm. Eight main decision factors were generated and categorized into resource-seeking (product input), market-seeking (cost, labour, logistics), efficiency-seeking (supply chain interruption risk, strategic access, country risk) and strategic asset-seeking (government trade policies). Ancarani et al. (2015) conducted research in a similar vein also adding more factors: resource-seeking (automation, skilled human resource), market-seeking (coordination and monitoring cost), efficiency-seeking (know-how and IPs, host country legislation). Moradlou et al. (2021) investigated manufacturing reshoring decision-making in the context of Brexit. Finally, McIvor and Bals (2021) applied a multi-theory framework to understand manufacturing reshoring decision-making, including the Eclectic Paradigm.

2.2 Hybrid decision factors in the Eclectic Paradigm

It can be argued that the factor classification of the Eclectic Paradigm is not clear since several factors are hybrid, that is they have been placed in multiple categories in Dunning’s decision factor examples. Based on the framework developed in 1998, we extracted six hybrid decision factors (Table 4). For example, the cost of resources has been classified as resource-seeking (i.e. to access low-cost resources), but also as market-seeking (i.e. to enable market opportunities through a reasonable price) and efficiency-seeking (i.e. to enable cost-efficient production through low-cost resources). Similarly, local infrastructure has been classified as resource-seeking (i.e. to access high-quality infrastructure) but can also be regarded as market-seeking (i.e. to facilitate market opportunities by connecting business partners) and efficiency-seeking (i.e. to enable cost-efficient production through better communication and cooperation with partners). Factors that enrich the companies’ intellectual assets, for example, knowledge-related assets are usually considered as resource-seeking (i.e. to access skills and knowledge of local partners), but sometimes also as strategic asset-seeking (i.e. to harness knowledge-related assets from local partners).

The issue of clearness is also apparent in the previous manufacturing reshoring studies using the Eclectic Paradigm. In Moradlou et al. (2021), seven decision factors were categorized differently from the previous studies (Table 5). In addition, no factor was categorized as efficiency-seeking in their study. This indicates the difficulty of applying the Eclectic Paradigm in classifying decision factors of manufacturing reshoring activities.

2.3 Conceptual framework

Figure 1 presents the conceptual framework for this study. Through the theoretical lens of the Eclectic Paradigm, manufacturing reshoring decision-making content is investigated, namely, decision factors (i.e. factors that are considered in manufacturing reshoring decision-making) and influencing factors (i.e. factors that are triggering, facilitating and/or impeding the manufacturing reshoring). The decision factors are categorized as resource-seeking, efficiency-seeking, market-seeking and strategic asset-seeking. In addition, some are classified as hybrid when one decision factor belongs to multiple seeking categories. The influencing factors are drivers, enablers and barriers. The decision factors and the influencing factors are linked by a double-headed arrow to show that a certain factor may function as both a decision factor and an influencing factor. This conceptual framework will be used to investigate manufacturing reshoring decision-making content through a deductive, systematic literature review approach.

3. Methodology

This research uses a theory refinement systematic literature review methodology based on the Eclectic Paradigm (Dunning, 1998). The approach allowed us to comprehensively capture reshoring decision factors and discover the linkage to influencing factors. The systematic nature of the examination made it possible to examine reshoring decision-making literature and interpret the findings through the Eclectic Paradigm. The search and review process was inspired by Gimenez and Tachizawa (2012), Miemczyk et al. (2012) and Seuring and Gold (2012). It includes defining research questions, determining characteristics of primary studies, retrieving baseline samples, selecting pertinent literature, synthesis literature and reporting the results (Table 6). In the first review step, research questions were defined (Table 6). Firstly, “What are the main decision factors in manufacturing reshoring decision-making?” Secondly, “Which decision factors constitute a driver, barrier or enabler of manufacturing reshoring?” and last “How applicable is the Eclectic Paradigm to manufacturing reshoring decision-making?”. In the second review step, the inclusion and exclusion criteria of the literature review were determined. Firstly, the review sample only includes English and peer-reviewed articles published in scientific journals. Secondly, the articles should not be a literature review itself. Thirdly, it was an inclusion criterion that the articles should focus on manufacturing reshoring decision-making (i.e. decision factors or influencing factors).

In the third review step, the search string was developed. Search keywords were developed based on six main categories, for which alternate terms and synonyms were identified. In the third review step, a comprehensive search string was constructed to ensure a thorough exploration of the subject matter. This involved the identification and categorization of six main themes “reshoring”, “backshoring”, “onshoring”, “rightshoring”, “nearshoring”, “inshoring” and “redistributed manufacturing”. Each category was enriched with a robust compilation of alternate terms and synonyms, combining relevant search terms in a structured manner using Boolean logic to capture a wide spectrum of relevant literature (see Table 6 for full search string). For instance, terms related to reshoring were expanded, including “reshoring”, “re-shoring”, “reshore”, “re-shore”, “reshored”, “re-shored. Specifically, terms “onshore”, “on-shore”, “nearhore”, “near-shore”, “inshore” and “in-shore” were excluded from the search string. The decision to exclude these terms was rooted in the observation that the search results retrieved from these terms pertained to domains such as oil mining or related industries. By excluding these terms, the precision and relevance of the search results were enhanced. The initial literature search was conducted in March 2017 in two databases, Scopus and Web of Science. These databases were selected for broad coverage in the target research field, with 475 articles and 374 articles identified in Scopus and Web of Science separately. Moreover, 303 duplicates were found and excluded. The database search alerts were set in March 2017 to ensure new potentially relevant articles were included. Until December 2022, the authors continuously review and add relevant literature to the review sample with the same reviewing process.

In the fourth review step, the baseline sample underwent two rounds of screening by two researchers who applied the chosen inclusion and exclusion criteria. In the first-round screening, abstracts were examined to determine if the paper discussed manufacturing reshoring decision-making content (i.e. decision and influencing factors). Sources addressing decision-making content were selected for the full paper review. In the second-round screening, the complete paper was reviewed. Sources where the research focused on decision-making content were kept in the final sample. The initial search and review in March 2017 generated the initial sample with 55 relevant papers. Furthermore, the review sample generated by the database search alert from 2017 to December 2022 has been selected and screened with the same two-round screening strategy. This generated final sample included 100 papers (see Appendix).

In the fifth review step, the synthesis was carried out in three phases. In the first phase, quotes concerning decision factors were extracted and merged into decision factors. The selection of quotes ensured that the final sample contained the correct information, and created a chain of evidence between data, sample and methodology. Then, these factors were further categorized into resource-seeking, market-seeking, efficiency-seeking and strategic asset-seeking based on how they were addressed in their original article (Figure 2). In the second phase, quotes concerning influencing factors were extracted and merged into drivers, barriers or enablers and further categorized into main decision factors (Figure 3). Contingency factors (e.g. product, firm and/or industry characteristics) were excluded in both phases. In the final phase, the applicability of the Eclectic Paradigm in manufacturing reshoring decision-making was investigated by looking at the clearness and comprehensiveness of the theory. This was carried out through the identification and investigation of hybrid factors. The synthesis drew inspiration from previous research constructing similar frameworks (e.g. Ancarani et al., 2015; Benstead et al., 2017; Ellram, 2013; Joubioux and Vanpoucke, 2016; Moradlou et al., 2021) and involved two of the authors in all phases.

Finally, in the sixth step, the findings of the literature review were reported. To ensure validity and reliability, as well as to reduce bias, the authors have carried out the review process jointly. To improve the validity, from the definition of the research questions to the selection of the literature sample, two of the authors have read all the documents and classified the content based on the research questions. This was first done individually by the two authors and later compared and adjusted to increase the correctness of data processing and to avoid bias. To improve reliability, the description, classification and consistency of decision factors have been developed through protocol development and interpretation guidelines. The findings are presented in the following section.

4. Research findings

The findings are presented in two subsections, the first focusing on decision factors of each seeking category and the second focusing specifically on hybrid decision factors. In total, 80 reshoring decision factors were extracted and categorized into resource-seeking, market-seeking, efficiency-seeking, strategic assets-seeking and hybrid-seeking factors (Tables 7–11). Some decision factors were further identified as influencing factors (i.e. drivers, enablers and barriers) of manufacturing reshoring.

4.1 Reshoring decision factors

Resource-seeking includes factors that enable the company to access and harness critical resources (tangible and intangible). Six out of 80 (8%) of the discovered decision factors were categorized into this group. For example, access to natural resources (e.g. raw materials) that are available or better in quality and/or price in one location (Baldassarre and Campo, 2015; Barbieri et al., 2019; Martínez-Mora and Merino, 2014). Another important influencing factor in this category is the availability and stability of labour resources (Di Mauro et al., 2018; Kinkel and Maloca, 2009; Moretto et al., 2020). Other important decision factors in this category include high-quality infrastructure, facilities and equipment (Stentoft et al., 2016A; Stentoft et al., 2016B; Lund and Steen, 2020).

Market-seeking includes factors that enable the company to ease access to sales opportunities, resource markets and logistics performance. Nine out of 80 (11%) of the discovered decision factors were categorized into this group. For example, access to domestic or adjacent regional markets is an important decision factor in this category (Barbieri et al., 2019; Fel and Griette, 2017; Moradlou et al., 2021). Within this category, other pivotal decision factors encompass cost considerations like labour, logistics and manufacturing costs. These elements empower companies to competitively manufacture final products while managing costs effectively (Fel and Griette, 2017; Ocicka, 2016; Srai and Ané, 2016). Other decision factors highlighted by many scholars in this group include customer service, service facilities and logistics performance that enable companies to quickly meet customer requirements (Pal et al., 2018; Rainnie, 2021; Wan et al., 2019).

Efficiency-seeking includes factors that enable the company to manufacture cost-efficiently. In total, 33 out of 80 (41%) of the discovered decision factors were categorized into this group. For example, labour productivity, as high labour productivity mitigates the pressure of wage growth (Panova and Hilletofth, 2017; Pal et al., 2018; Theyel and Hofmann, 2021). In this category, the manufacturing automation level merges as a crucial factor, proven to enhance productivity and lower production costs (Boffelli et al., 2021; Faber, 2020; Mcivor and Bals, 2021). Additionally, scholars emphasize governmental incentives such as subsidies, tax reductions and grants that help companies release their financial burden (Ancarani et al., 2015; Mcivor and Bals, 2021; Rasel et al., 2020).

Strategic asset-seeking includes factors that enable the company to access and harness strategic knowledge-related assets (or resources) and contains 13 out of 80 (16%) of the discovered decision factors. Scholars highlighted IP rights as an important decision factor in this category (Benstead et al., 2017; Fratocchi and Di Stefano, 2019; Joubioux and Vanpoucke, 2016; Leisner and Nielsen, 2019). Equally significant decision factor is access to advanced technology and R&D investment to enhance innovation capability (Ancarani et al., 2019; Młody and Stępień, 2020; Moradlou et al., 2021; Rasel et al., 2020). Additionally, scholars emphasize brand image through advertising product quality or competitiveness, such as strategy related to sustainability (Boffelli et al., 2018; Grappi et al., 2020; Moretto et al., 2020).

4.2 Hybrid decision factors

While categorizing the discovered decision factors based on the Eclectic Paradigm it could be noted that the same decision factors appeared in multiple categories. As such, the factors are considered as hybrid decision factors. In total, 19 hybrid decision factors (24%) were identified (Table 11).

Some decision factors serve multiple purposes based on various reshoring situations (Table 12). For example, know-how and IP serves as both a critical resource-seeking (i.e. to access local know-how and IP) and a strategic asset-seeking (i.e. to better invest and integrate advanced knowledge and technology in production) advantage (Mcivor and Bals, 2021). Likewise, knowledge and technology is recognized as resource-seeking (i.e. to access local know-how and IP), efficiency-seeking (i.e. to better protect sensitive IP and knowledge) and strategic asset-seeking (i.e. to develop new IP to retain skills and know-how in the market) advantage (Dachs et al., 2019a, 2019b; Mcivor and Bals, 2021; Rainnie, 2021). Similarly, R&D plays an important role in developing companies’ innovation capability. It is both an efficiency-seeking (i.e. to increase manufacturing efficiency through proximity to the R&D department) and a strategic asset-seeking (i.e. to develop or accelerate innovative products as well as advanced product development) advantage (Johansson and Olhager, 2018; Młody and Stępień, 2020; Rasel et al., 2020).

Many companies are reshoring to better serve the target market. Some important market-related factors are considered a hybrid. For example, the market opportunity is obviously a market-seeking advantage (i.e. to access new sales and market-opening opportunities) but also, according to many studies (Vanchan et al., 2018; Barbieri et al., 2019), a strategic asset-seeking advantage (i.e. to better understand and exploit the potential market and consumer patterns). Customization is known as an important strategic asset-seeking (i.e. to exploit firms’ capability and functionality of customization strategy) and market-seeking (i.e. to enhance the capabilities to meet the customized-drive market) advantage (Moradlou et al., 2017; Rainnie, 2021). It is also considered an efficiency-seeking advantage (i.e. to satisfy customers’ changing needs while reducing waste with a customization strategy). Firms can expect cost efficiency and production flexibility (Dachs et al., 2019a; Hartman et al., 2017; Mcivor and Bals, 2021).

Scholars have emphasized the importance of building sustainable and/or socially responsible supply chains when reshoring. These two factors are known as important strategic seeking advantages. They contribute to enhancing companies’ competitive advantages by building a sustainable supply chain and production (Pal et al., 2018; Sirilertsuwan et al., 2019), building a responsible firm image and avoiding human rights violations (Ashby, 2016; Joubioux and Vanpoucke, 2016). Also, they are both efficiency-seeking advantages, as they aim to reduce waste and cost through building a sustainable supply chain (Stentoft et al., 2016A; Sirilertsuwan et al., 2019) and to easier reach suppliers and setting up direct sourcing agreements (Baldassarre and Campo, 2015; Engström et al., 2018A; 2018B). With the growing consumer awareness of responsible and sustainable production, these two factors are also considered market-seeking advantages, since they aid in creating an environmentally friendly brand image that attracts specific customer groups (Leisner and Nielsen, 2019; Martínez-Mora and Merino, 2020); and reaches customers that have consumer awareness of socially responsible products and production (Ashby, 2016; Ocicka, 2016; Pal et al., 2018).

5. Concluding discussion

Three research questions were sought to be answered in this study:

RQ1.

What are the main decision factors in manufacturing reshoring decision-making?

RQ2.

Which decision factors constitute a driver, barrier or enabler of manufacturing reshoring?

RQ3.

How applicable is the Eclectic Paradigm to manufacturing reshoring decision-making?

5.1 Answering research questions

This research proposes a classification of 80 decision factors into resource-seeking (8%), market-seeking (11%), efficiency-seeking (41%), strategic asset-seeking (16%) and hybrid (24%) (Table 13). The research has shown how decision factors can be hybrid and classified into multiple categories of the Eclectic Paradigm. The multiplicity of factors can be seen as an argument against the Eclectic Paradigm, if the factors are considered from a closed-systems perspective. This confusion is not new, as previous studies show different classifications of the same factor. For example, in Ellram (2013) as well as Ancarani et al. (2015) the proximity to customers is an efficiency-seeking advantage (i.e. to enable firms to quickly respond to market change with a lower cost), while in Moradlou et al. (2021), the proximity to customers is a market-seeking advantage (i.e. to access market opportunities). With these findings, it is important to realize that there is a context dependence (Sayer, 1992) which is difficult to control for in these types of studies. It is, therefore, important to adopt a position on knowledge which accounts for the importance of context. This is supported by the growing number of researchers arguing for the adoption of a critical-realist perspective (Aastrup and Halldórsson, 2008; Bille and Hendriksen, 2023; Eriksson, 2015; Eriksson and Engström, 2021; Rotaru et al., 2014). The Eclectic Paradigm requires understanding the decision factors and the context in which they influence the manufacturing reshoring decision, which is a central part of critical realism.

The use of hybrid decision factors is a consequence of researchers trying to describe how factors have influenced a manufacturing reshoring decision. The use of hybrid factors can also be used to explain why a factor influenced a manufacturing reshoring decision in different ways. That a factor can be seen differently depending on context is in line with how theory is viewed in supply chain research. Svensson (2013, p. 468) states that “theory [in sequential order] seeks to describe, explain and predict reality”. Theory needs to organize facts so that the model has both practical and theoretical value, which in this case is dependent on an accurate representation of how factors function in practice. The use of hybrid factors makes it possible to both describe and explain reality. It needs to be acknowledged that decision factors can be hybrid factors and the effect of the specific factor is contingent on context, such as geography, time and corporate priorities. Considering context in this way is in line with the role of theory in social science. Notably, Sayer (1992) suggested a model where an object has the power to produce an event, but the outcome is contingent on the context in which the event is triggered. Such a position need not be impossible in supply chain research, but the appropriate epistemology and ontology need to be used.

This research has furthermore classified decision factors as influencing factors, thus merging two research streams of manufacturing reshoring decision-making content. The merger of research streams allows building a holistic understanding of the main decision factors and which decision factors constitute a driver, barrier or enabler of manufacturing reshoring. Turning to the influencing factors more specifically, this research has, in accordance with previous research (Foerstl and Kirchooff, 2016; Wiesmann et al., 2017; Engström et al., 2018A), classified the influencing factors as drivers, barriers and enablers (Tables 7–11). Whether a factor constitutes a driver, barrier or enabler is context-dependent. A factor can be considered a driver in one situation, but a barrier in another. For example, skilled and qualified labour is a driver when companies reshoring for seeking trained, qualified and motivated personnel, but a barrier when companies consider relocating manufacturing to a place that lacks motivated, experienced and stable labour (Boffelli et al., 2018; Huq et al., 2021; Rainnie, 2021). In addition, skilled and qualified labour is an enabler when companies establish teams composed of employees with relevant knowledge and experience of similar products to ensure the manufacturing reshoring process proceeds smoothly (Nujen et al., 2019). The context-dependent view of the decision factors themselves has not been met with criticism. This gives credence to the notion that the view here adopted that the hybrid perspective is a suitable way to describe and explain reality. Decision factors are thus both hybrid and ambiguous. Hybrid in terms of what advantage they influence, ambiguous in terms of how they contribute to or detract from, said advantage.

This research has not only covered the clearness but also the comprehensiveness (i.e. coverage) of the Eclectic Paradigm. We found that most decision factors from the literature can be categorized into resource-seeking, efficient-seeking, marketing-seeking or strategy assets-seeking. The review found that the manufacturing reshoring literature largely overlooks one category of factors, namely, contingency factors (e.g. firm-level determinants, industry-specific characteristics and product features). While contingency factors such as the companies’ business strategy and the manager’s personal preferences are essential or even fundamental for decision-making, only a few studies adopt a contingency-based perspective (Benstead et al., 2017; Fratocchi and Di Stefano, 2019; Rasel et al., 2020; Moradlou et al., 2021). This study contributes to the literature by pointing out the development needed for building a more holistic Eclectic Paradigm in discovering the decision factors of manufacturing reshoring decisions.

5.2 Theoretical contributions

This research develops a better understanding of the decision factors in manufacturing reshoring. The research does so through a literature review and a categorization based on the Eclectic Paradigm. The categorization encapsulates multiple iterations and implementations of the Eclectic Paradigm. At the core of the categorization is the hybrid nature of decision factors, where a single factor can have influence in multiple ways at the same time. This research is able to present in-depth theoretical classifications as well as an overview of how each factor can be analysed by practitioners. The categorization thus contributes to both theory development of the Eclectic Paradigm, while also holding the potential to lead practitioners in future decisions.

Through the integration of decision factors and influencing factors, this research captures two research streams within the same area, which have previously been separate. Depending on what is to be analysed, one view, the other or a combination of both might be suitable. This position harmonizes with critical realism and once again stresses the fragmented and eclectic state of the research field. By shedding light on the ambiguous nature of decision factors and the Eclectic Paradigm this research contributes by showing how the Eclectic Paradigm can be understood, and the research thus encourages increased use of the theory.

Decision factors have been successfully categorized into resource, market, efficiency and strategic asset-seeking advantages. The research also highlighted that contingency factors are lacking research. Highlighting this lack of research and organizing current research are important parts of the contribution this paper makes by organizing and systemizing current knowledge. The research presents a strong argument that the research field is myopic. Combining previously independent research streams and highlighting missing decision criteria are steps to a more holistic use of the Eclectic Paradigm.

5.3 Managerial implications

While this study set out to understand a mainly theoretical issue, the findings are of great importance for practitioners. Given the field of research, the managerial implications are aimed at managers working with supply chain, manufacturing and marketing. Hybrid decision factors stress that there might be a decision factor that is enabling relocation, but that the factor can also be a barrier perhaps affecting a different decision category. As such, to effectively evaluate decision factors, each factor needs to be understood from two dimensions, namely, decision and influence Table 14.

There are a few issues that still need to be understood in terms of how a manager should evaluate a manufacturing reshoring decision. First off, there are 80 factors to evaluate, and each factor should be evaluated in 12 aspects. Consequently, there are 960 evaluations to make. Yet, challenges exist in evaluating decisions. It might also be necessary to add weights to each factor and each evaluation aspect. Given that the decisions are not taken in a vacuum, it is possible that the evaluation needs to be remade for each decision.

There are ways to make informed decisions. One way is to make sure that central factors are fully evaluated. Through brainstorming and cross-functional approaches, it might also be possible to identify the most critical factors. Much research in this area has been done with fuzzy logic (e.g. Hilletofth et al., 2021) since it is difficult to capture fully quantifiable and reliable data. Due to difficulties with specific data and the complexities of the field, the identified decision criteria and how to evaluate them hold much value, but more work is needed for implementation.

5.4 Limitations and future research

This paper has investigated manufacturing reshoring decision factors and how these should be categorized. By using a literature review it is possible to capture a wide range of decision factors published in previous research. This is not to say that all factors have been captured, and that there might be additional information about categorization available. It is possible that more knowledge could have been captured if snowballing technique had been used to gather a larger literature sample. It is also possible to learn more by using other sources of data, such as surveys and case studies. Contingency factors were largely overlooked in the sample, and perhaps a different methodology could have strengthened the inclusion of contingency factors into the theory development presented in this paper.

To address the limitations of this research, further research on contingency factors should be conducted. The primary suggestion on how to do so is to use a different research methodology, but it is also possible that a modified literature review can uncover contingency factors. The research has presented an example of how individual decision factors can be evaluated based on both decision factors and influencing factors. From a practical perspective, this holds much value, but there is a need to further develop how this knowledge should be used in practice. It is possible to use the list of factors and their dimensions as a basis for discussion, but it is interesting to see to what extent decision-support tools or modelling can be implemented. In addition, the importance of each factor may differ from industry to industry, it will also be interesting to see research investigating it.

If authors seek to continue contributing to manufacturing reshoring decision-making it might be possible to widen the theoretical scope and try to organize decision factors from a different perspective. Perhaps decision factors can be understood in terms of what competitive advantage a company seeks to develop, or through the lens of institutional theory. Authors who seek to better understand the Eclectic Paradigm can widen the empirical scope and try to understand decision factors from other relocations or manufacturing locations in general.

Figures

Figure 1.

Conceptual framework eclectic paradigm with decision and influencing factors

Figure 2.

Data coding scheme Phase 1

Figure 3.

Data coding scheme Phase 2

Table 1.

Development of the Eclectic Paradigm over time

Year	Contribution	Development of the Eclectic Paradigm
Dunning (1980)	Proposed the OLI parameters Conducted empirical studies to test the eclectic paradigm: and evaluated O and L parameters	O: ownership advantages L: location advantages I: internalisation advantages
Dunning (1983)	Distinguished between the asset (Oa) and transaction (Ot) advantages of international companies	O: ownership advantages Oa: asset advantages Ot: transaction advantages
Dunning (1988)	Specified the eclectic paradigm in explaining three main forms of international manufacturing	The Eclectic Paradigm: MS: market-seeking production RS: resource-seeking production ES: efficiency-seeking production
Dunning (1998)	Enriched the eclectic paradigm by adding the strategic asset-seeking into variables that influence the production decision	The Eclectic Paradigm: MS: market-seeking FDI RS: resource-seeking FDI ES: efficiency-seeking FDI SAS: strategic asset-seeking FDI
Dunning (2000)	Compare eclectic paradigm with other international manufacturing theories
Dunning (2001)	Present the development of the eclectic paradigm, answered three main criticisms

Source: Authors’ own work

Table 2.

Examples of decision factors in the Eclectic Paradigm (Dunning, 1998)

Type of advantages	Decision factor examples
Resource-seeking	• The availability of local resources (Natural resources/infrastructures/investment incentives) • The availability of local partners • Local opportunities for upgrading the quality of resources/logistics
Market-seeking	• Accessibility of domestic/adjacent regional markets • Cost of the resources market (Real wage costs/material costs/transportation costs) • Factors contribute to better business environments (Tariff barriers/privileged access to import licences) • Availability and price of skilled labour • Competitiveness of related firms • Quality of infrastructure • Industrial agglomeration • Macroeconomic and macro-organizational policies • Promotional activities
Efficiency-seeking	• Production-related costs (Labour costs/material costs/machinery costs) • Presence of agglomerative economies • Investment incentives (Tax breaks/accelerated depreciation/grant/subsidized land) • Knowledge-intensive and integrated activities (Research and development (R&D)/office functions) • Government regulations (Removing obstacles and facilitating HR upgrading) • Availability of specialized spatial clusters (Science parks/industrial parks/service support systems)
Strategic asset-seeking	• Price and availability of knowledge-related assets (Know-how/intellectual property (IP)/synergistic assets) • Acquisition and harnessing of such assets with business partners • Opportunities to exchange localized tacit knowledge • Different cultures, institutions and systems • Different customer demands and preferences

Source: Authors’ own work

Table 3.

Manufacturing reshoring research using the Eclectic Paradigm

Reference	Title	Focus	Journal
Ellram (2013)	Offshoring and reshoring, An update on the manufacturing location decision	Manufacturing location decision	Journal of Supply Chain Management
Ancarani et al. (2015)	Prior to reshoring: A duration analysis of foreign manufacturing ventures	Foreign manufacturing duration analysis	International Journal of Production Economics
Moradlou et al. (2021)	Geopolitical disruptions and the manufacturing location decision in multinational company supply chains, a Delphi study on Brexit	Manufacturing location decision (Brexit)	International Journal of Operations and Production Management
Mcivor and Bals (2021)	A multi-theory framework for understanding the reshoring decision.	Reshoring decision (multi-theory framework)	International Business Review

Source: Authors’ own work

Table 4.

Hybrid decision factors generated from dunning’s Eclectic Paradigm (Dunning, 1998)

Factor	Resource-seeking	Market-seeking	Efficiency-seeking	Strategic asset-seeking
Cost of resources	The price of natural resources	Real wage costs; material costs	Production cost-related factors (labour, materials and machinery)
Local infrastructures	Infrastructure to enable resources to be exploited	Quality of national and local infrastructure	Availability and quality of local infrastructure
Investment incentives	Investment incentives, such as tax and holiday		Investment incentives, such as tax breaks and grants
Legislation and regulations	Government restrictions on FDI or on capital/dividend remissions	Government restrictions on FDI and market access	Freedom to engage in trade in one location Government’s role in removing obstacles to restructuring economic activity
Knowledge-related assets	Availability of local partners to jointly promote knowledge			Availability of knowledge-related assets and harnessing such assets from foreign locations
Agglomerative spatial economies		Increased role of agglomerative spatial economies	Availability of specialized spatial clusters (science parks, service support systems)

Source: Authors’ own work

Table 5.

Decision factor categorization in different papers using the Eclectic Paradigm

Factor	Moradlou et al. (2021)	Ellram (2013), Ancarani et al. (2015)
Proximity to customers	Market-seeking	Efficiency-seeking
Customer service agreement	Market-seeking	Efficiency-seeking
Accessibility of skilled labour	Market-seeking	Resource-seeking
Uncertain regulation	Efficiency-seeking	Market-seeking
Favourable currency condition	Efficiency-seeking	Resource-seeking
Taxation regulations	Efficiency-seeking	Strategic asset-seeking
Government incentives	Efficiency-seeking	Strategic asset-seeking

Source: Authors’ own work

Table 6.

Systematic literature review completion details

Literature review process	Completion details
Step 1 Define research questions	RQ1: What are the main decision factors in manufacturing reshoring decision-making? RQ2: Which decision factors constitute a driver, barrier or enabler of manufacturing reshoring? RQ3: How applicable is the Eclectic Paradigm to manufacturing reshoring decision-making?
Step 2 Determine characteristics of primary studies	Time: 2009–2022 (Trial searches before 2009 yielded hits on, e.g. services and oil platforms. The first manufacturing paper was identified to be published in 2009.) Search areas: Title, Abstract, Keywords Source type: Peer-reviewed journal article Language: English Databases: Scopus and Web of Science (selection criteria: broad coverage in supply chain and operations management research, ensuring access to a comprehensive and pertinent array of literature on reshoring and the eclectic paradigm)
Step 3 Retrieve baseline sample of articles	Search string “reshoring” OR “re-shoring” OR “reshore” OR “re-shore” OR “reshored” OR “re-shored” OR “backshoring” OR “back-shoring” OR “backshore” OR “back-shore” OR “backshored” OR “back-shored” OR “onshoring” OR “on-shoring” OR “onshored” OR “on-shored” OR “rightshoring” OR “right-shoring” OR “rightshore” OR “right-shore” OR “rightshored” OR “right-shored” OR “nearshoring” OR “near-shoring” OR “nearshored” OR “near-shored” OR “inshoring” OR “in-shoring” OR “inshored” OR “in-shored” OR “redistributed manufacturing” OR “re-distributed manufacturing”
	Baseline sample: 546 (Scopus: 475, Web of Science: 374, Duplicates: 303)
Step 4 Select pertinent literature	Sample after abstract and paper screening Initial sample after initial search (March 2017) 55 articles Final sample after alert review (December 2022) 100 articles
Step 5 Synthesise literature	Phase 1: Decision factors were extracted and categorized based on Eclectic Paradigm. Phase 2: Influencing factors were extracted and categorized based on main decision factors. Phase 3: Hybrid factors were identified and further investigated
Step 6 Report the results	Decision factors are presented in sub-Section 4.1 Influencing factors are presented in sub-Section 4.1 Hybrid factors are presented in sub-Section 4.2

Source: Authors’ own work

Table 7.

Decision factors in resource-seeking category, highlighted as drivers (D), barriers (B) and enablers (E) where applicable

Decision factor	D	B	E	References
Business partners	X	X	X	[5], [8], [14], [25], [31], [47], [51], [55], [56], [61], [64], [67], [70], [77], [82], [86], [88]
Currency exchange rate	X	X		[1], [7], [11], [12], [18], [25], [28], [31], [36], [38], [39], [45], [58], [59], [63], [65], [71], [77], [82], [84], [88], [93], [100]
Facilities and equipment	X	X	X	[3], [14], [18], [26], [27], [39], [45], [47], [56], [61], [83], [84], [85], [86]
Labour resources	X	X	X	[1], [7], [11], [14], [15], [21], [22], [24], [26], [27], [28], [31], [33], [38], [39], [42], [43], [45], [49], [49], [50], [51], [52], [56], [59], [62], [63], [64], [65], [66], [67], [68], [70], [76], [77], [78], [80], [82], [84], [87], [88], [96]
Natural resources	X	X		[5], [7], [10], [17], [25], [26], [27], [28], [38], [39], [43], [44], [45], [47], [58], [59], [61], [62], [69], [77], [82], [84]
Production foundation			X	[28], [86]

Source: Authors’ own work

Table 8.

Decision factors in market-seeking category, highlighted as drivers (D), barriers (B) and enablers (E) where applicable

Decision factor	D	B	E	References
Customer service	X			[1], [12], [62], [69], [81], [82]
Energy cost		X		[3], [26], [33], [59], [69], [72], [77], [84], [87], [88]
Labour cost	X	X		[1], [3], [7], [11], [18], [19], [21], [22], [23], [24], [25], [38], [39], [44], [46], [49], [50], [51], [56], [58], [65], [71], [77], [80], [81], [82], [84], [86], [88], [92], [93], [94], [95], [96], [99]
Logistics cost	X	X	X	[1], [2], [7], [8], [11], [15], [21], [22], [24], [25], [38], [39], [44], [45], [46], [62], [64], [69], [81], [82], [88], [93]
Logistics performance	X	X		[1], [7], [15], [18], [26], [27], [28], [31], [39], [46], [47], [54], [57], [60], [63], [69], [88], [93], [94], [95], [96]
Macroeconomics	X			[7], [8], [26], [27], [30], [38], [47], [71], [80]
Patriotism	X			[13], [18], [24], [26], [27], [32], [34], [35], [73], [85], [92], [94]
Raw material access and cost	X			[3], [7], [8], [19], [30], [38], [45], [58], [59], [61], [64], [77], [91], [92], [96]
Service facilities				[54]

Source: Authors’ own work

Table 9.

Decision factors in efficiency-seeking category, highlighted as drivers (D), barriers (B) and enablers (E) where applicable

Decision factor	D	B	E	References
Controllability of production	X	X	X	[2], [3], [17], [32], [43], [60], [70], [77]
Coordination cost	X		X	[1], [7], [8], [11], [12], [15], [17], [21], [22], [24], [32], [47], [50], [51], [56], [58], [64], [77], [92], [96]
Customer proximity	X		X	[1], [2], [3], [7], [8], [18], [25], [31], [32], [38], [39], [44], [45], [50], [51], [56], [59], [60], [62], [63], [70], [77], [84], [88], [90]
Delivery lead time	X		X	[15], [26], [27], [40], [41], [64], [89], [94], [97]
External communication	X		X	[26], [27], [43], [56], [59], [62], [64], [69], [82], [99]
Function synchronization	X			[12], [72]
Geographical and cultural distance	X		X	[1], [8], [11], [12], [23], [26], [27], [42], [43], [47], [58], [64], [69], [70], [77], [81], [82], [84], [99]
Global economic conditions				[1], [11], [25], [26], [27], [28], [56], [58]
Information technology (IT)				[84]
Information transfer		X	X	[20], [31], [47], [76], [99]
Inventory level and cost	X		X	[2], [7], [8], [14], [24], [26], [27], [32], [42], [43], [47], [59], [63], [63], [64], [69], [70], [77], [82], [88], [92], [99]
Labour market flexibility	X			[33], [44], [5], [53], [69], [74]
Labour productivity	X		X	[1], [8], [10], [11], [12], [24], [58], [64], [70], [71], [72], [86], [90], [88], [92]
Manufacturing capacity	X	X	X	[4], [8], [11], [12], [15], [18], [21], [22], [24], [26], [27], [47], [51], [56], [58], [61], [64], [68], [70], [77], [81], [28], [92]
Manufacturing cost	X	X	X	[1], [37], [38], [58], [60], [61], [63], [66], [89], [92], [93]
Manufacturing risks	X	X		[26], [27], [28], [38], [39], [42], [43], [44], [45], [58], [67], [84]
Natural disasters	X			[8], [25], [42], [47], [60], [84]
Overhead cost		X		[36], [39], [59], [93]
Political stability	X	X		[25], [42], [43], [47], [63], [69], [75], [82], [84], [98], [73]
Production flexibility	X	X	X	[2], [3], [9], [11], [13], [15], [18], [38], [39], [40], [41], [44], [45], [46], [50], [51], [58], [60], [61], [64], [65], [70], [79], [84], [86], [90]
Replenishment lead time	X		X	[8], [14], [42], [44], [57], [58], [59], [62], [69], [81], [82], [84], [88], [92], [97], [99]
Social/ethical concerns				[25], [34]
Societal disruptions				[42], [77], [84]
Strategic flexibility				[93]
Subsidies	X			[8], [25], [32], [33], [39], [45], [50], [51], [84], [92], [94]
Supply chain agility				[62], [90], [98]
Supply chain flexibility	X	X	X	[1], [3], [4], [11], [15], [18], [21], [2], [22], [24], [38], [39], [43], [44], [45], [46], [50], [51], [56], [58], [60], [61], [65], [69], [70], [77], [79], [84], [86], [92]
Supply chain resilience	X			[8], [11], [25], [78], [82], [85]
Supply chain risks	X		X	[1], [8], [25], [26], [27], [38], [39], [42], [43], [58], [67], [70], [86], [92], [96]
Supply chain visibility	X		X	[70], [82], [88]
Time to market	X		X	[2], [3], [8], [24], [26], [27], [32], [38], [39], [40], [41], [42], [43], [44], [45], [47], [56], [60], [62], [63], [64], [65], [67], [69], [70], [79], [81], [82], [84], [86], [90], [92], [93], [99]
Total cost	X	X	X	[1], [2], [3], [5], [7], [10], [14], [24], [30], [32], [43], [47], [69], [70], [77], [79], [82], [84], [90], [93], [94], [95], [99]
Trade and payment terms				[64], [69], [82], [96]

Source: Authors’ own work

Table 10.

Decision factors in strategic asset-seeking category, highlighted as drivers (D), barriers (B) and enablers (E) where applicable

Decision factor	D	B	E	References
Brand image and reputation	X			[3], [5], [12], [14], [16], [23], [25], [26], [27], [59], [60], [65], [77], [92], [93]
Competitive pressure	X	X		[4], [7], [11], [25], [38], [63], [78]
Competitive priorities	X		X	[3], [7], [11], [50], [51], [67], [93]
Core competencies	X	X		[26], [27], [34], [38], [44], [45], [67], [78], [84], [84], [86]
Countertrade requirements				[25], [47]
Innovation ability	X			[2], [3], [7], [11], [24], [32], [40], [41], [43], [58], [59], [60], [64], [70], [79], [92]
Knowledge transfer		X	X	[47], [51], [67]
Ownership related issues				[26], [27], [70]
Product quality	X		X	[1], [2], [3], [5], [7], [8], [11], [12], [15], [21], [22], [24], [26], [27], [30], [32], [34], [38], [39], [40], [41], [42], [43], [45], [46], [47], [51], [54], [56], [58], [59], [60], [62], [66], [69], [70], [77], [79], [82], [84], [89], [90], [92], [95], [96]
Production and process quality	X		X	[15], [37], [39], [64]
Servitization strategy				[6]
Strategy shift	X		X	[8], [24], [30], [32], [56], [61], [64], [67], [70], [78], [82], [96]
Tax advantages	X		X	[7], [8], [11], [19], [45], [50], [51], [58], [63], [65], [84], [88]

Source: Authors’ own work

Table 11.

Hybrid decision factors, highlighted as drivers (D), barriers (B) and enablers (E) where applicable

Decision factor	D	B	E	References
Customer demand			X	[12], [13], [14], [28], [34], [36], [38], [39], [42], [64], [69], [70], [76], [86], [88], [93]
Customization strategy				[32], [62], [70], [76], [77], [89], [90]
Government incentives	X		X	[1], [7], [8], [24], [26], [27], [28], [49], [61], [63], [72], [77], [82], [84], [94], [95], [99]
Industrial agglomeration	X		X	[56], [77], [92]
Information access				[28], [33], [42], [43], [63], [84]
Infrastructure	X	X	X	[1], [7], [18], [21], [22], [25], [42], [43], [47], [51], [58], [59], [61], [67], [81], [87], [92]
Know-how and IP	X		X	[1], [2], [3], [7], [8], [9], [11], [12], [15], [21], [22], [24], [28], [33], [43], [47], [54], [58], [64], [67], [69], [70], [76], [77], [82], [84], [88], [92]
Knowledge and technology	X		X	[3], [10], [16], [23], [32], [38], [39], [44], [44], [50], [56], [64], [67], [70], [75], [76], [78], [80], [82], [84], [85]
Legislation and regulations	X	X		[1], [3], [7], [8], [11], [14], [25], [26], [27], [28], [38], [39], [43], [45], [63], [69], [70], [77], [78], [81], [84], [86], [96]
Made-in effect	X	X		[1], [2], [3], [5], [7], [11], [12], [14], [18], [24], [26], [27], [28], [32], [33], [34], [49], [58], [60], [64], [65], [77], [80], [82], [94], [95]
Management performance	X			[18], [67], [69], [82], [84]
Manufacturing automation level	X		X	[1], [7], [10], [12], [14], [21], [22], [26], [27], [29], [59], [60], [67], [70], [72], [82], [84], [85], [86], [87], [88], [92]
Market opportunities	X	X		[3], [4], [7], [8], [10], [18], [24], [25], [26], [27], [45], [47], [50], [51], [57], [60], [61], [63], [72], [75], [82], [88], [92], [93]
R&D	X			[8], [9], [15], [24], [26], [27], [32], [38], [44], [51], [56], [59], [60], [63], [65], [75], [77], [82], [84], [86], [89]
Responsible supply chain				[5], [7], [26], [27], [31], [47], [58], [64], [69], [70], [77], [82], [84], [88]
Responsiveness to market	X		X	[1], [11], [30], [42], [43], [44], [48], [62], [64], [70], [88], [92], [97]
Supply networks		X	X	[5], [9], [11], [13], [14], [19], [26], [27], [37], [47], [58], [61], [62], [63], [67], [67], [70], [77], [80], [81], [90]
Sustainable supply chain	X	X		[5], [7], [26], [27], [31], [40], [41], [47], [54], [58], [60], [64], [66], [70], [81], [82], [84], [88]
Technology agglomeration	X		X	[5], [7], [50], [51], [59], [60], [67], [77], [78]

Source: Authors’ own work

Table 12.

Hybrid decision factors with category descriptions

Decision factor	Description
Customer demand	MS: To access market opportunities by exploring the preferences, wishes and demands of customers ES: To enable cost-efficiency production by accurate customer demand forecast SAS: To harness the capability to satisfy different customer demands and preferences
Customization strategy	MS: To enhance the capabilities to meet the customized-drive market. ES: To enable satisfying customers’ changing needs with lower waste through a customization strategy SAS: To exploit firms’ capability and functionality of customization strategy
Government incentives	RS: To access incentives offered by governments ES: To exploit incentives offered by governments to release financial burden (e.g. tax reduction, grants) SAS: To harness strategic assets by leveraging government-provided incentives
Industrial agglomeration	MS: To enable a better understanding and serve local markets through joining industrial agglomeration ES: To increase production stability and responsiveness by locating close to agglomerative economies
Information access	RS: To access transparent and comprehensive information related to production ES: To easily collect comprehensive information before decision-making
Infrastructure	RS: To access high-quality infrastructure that enables resources to be exploited MS: To facilitate market opportunities through developed infrastructures ES: To better communicate and cooperate with business partners.
Know-how and IP	RS: To access local know-how and IP ES: To better protect sensitive IPs and knowledge SAS: To develop new IPs to retain skills and know-how in the market.
Knowledge and technology	RS: To access advanced knowledge, technologies and skill resources. SAS: To better invest and integrate advanced knowledge and technology in production
Legislation and regulations	MS: To access the market based on trading regulations, e.g. tariff and non-tariff trade barriers ES: To avoid tariff costs and heavy punishments when locating production with fewer restrictions SAS: To enhance the brand image (e.g. product quality and sustainability) through the fulfilment of strict regulation/requirements
Made-in effect	MS: To promote the “Made-in-Sweden” story, and to attract specific customer groups SAS: To gain a better brand image from the “Made-in-Sweden” tag.
Management performance	RS: To access available qualified managers and/or management resources ES: To enable low waste production by enhancing management performance with lower costs
Manufacturing automation level	ES: To enable increasing productivity while decreasing production costs SAS: To increase production flexibility and capacity
Market opportunities	MS: To access new sales/market-opening opportunities SAS: To better understand and exploit the potential market and consumer patterns
R&D	ES: To increase manufacturing efficiency through proximity to the R&D department SAS: To develop or accelerate innovative products as well as advanced product development
Responsible supply chain	MS: To access customers that have consumer awareness of socially responsible products and production ES: To enable directly set up direct sourcing agreements with suppliers SAS: To build a responsible firm image and avoid human rights violations
Responsiveness to market	ES: To enable a quick response to changing demands SAS: To develop the ability to compete on speed
Supply networks	RS: To better cooperate with regional suppliers and/or build new supply networks MS: To jointly promote local competitiveness with suppliers ES: To better collaborate with proximity suppliers SAS: To harness the skills, resources and infrastructure of the supply networks
Sustainable supply chain	MS: To attract specific customer groups through building an environmentally friendly supply chain image ES: To enable cost-efficiency production by reducing waste and cost. SAS: To enhance the company’s competitive advantages by building sustainable supply chains
Technology agglomeration	ES: To jointly promote production innovation by locating proximity to technology clusters SAS: To access and acquire advanced knowledge through joining technology clusters such as industrial parks

Source: Authors’ own work

Table 13.

Manufacturing reshoring decision factor classification

Decision factors	No.	% of total
Resource-seeking	6	8
Market-seeking	9	11
Efficiency-seeking	33	41
Strategic asset-seeking	13	16
Hybrid Total	19 80	24 100

Source: Authors’ own work

Table 14.

Matrix shows how decision factors need to be understood

[Decision factor]	Driver	Barrier	Enabler
Resource-seeking	+/−/0	+/−/0	+/−/0
Market-seeking	+/−/0	+/−/0	+/−/0
Efficiency-seeking	+/−/0	+/−/0	+/−/0
Strategic asset seeking	+/−/0	+/−/0	+/−/0

Source: Authors’ own work

Appendix. Literature review sample

[1] Ancarani A, Di Mauro C, Fratocchi L, Orzes G, Sartor M (2015), “Prior to reshoring: A durationanalysis of foreign manufacturing ventures”, International Journal of Production Economics, Vol. 169, pp. 141-155, doi: 10.1016/j.jwb.2019.04.003.

[2] Ancarani A, Di Mauro C, Mascali, F (2019), “Backshoring strategy and the adoption of Industry4.0: Evidence from Europe”, Journal of World Business, Vol. 54 No.4, pp. 360-371, doi: 10.1016/j.jwb.2019.04.003.

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