1 Introduction

The international business (IB) landscape is being transformed by a convergence of digitalization and globalization, creating new possibilities for businesses worldwide (Banalieva and Dhanaraj 2019; Bhatti et al. 2022). It is argued that organizations that adopt emerging advanced technologies are able to quickly expand internationally (Ahi et al. 2022; Messner 2022). Such technological advancements have enabled firms to digitize their business processes and organize them in geographically dispersed locations spanning organizational, spatial, temporal, and cultural boundaries (Castagnoli et al. 2021; Laplume et al. 2016; McWilliam, Kim, Mudambi, & Nielsen, 2020). This has also allowed companies to integrate their businesses along global value chains, forge alliances with new partners, and acquire new customers in international locations (De Beule et al. 2022; Oliveira et al. 2021; Mukherjee et al. 2019; Pananond et al. 2020). In short, the rise of newer and novel technologies centering around the Industry 4.0 revolution have minimized global trade barriers and enabled businesses to slice value chain activities into granular processes that can be performed at multiple geographic locations and leverage externalization benefits (Bouncken and Barwinski 2021; Kedia and Mukherjee 2009; Laplume et al. 2016; Luo and Zahra 2023).

The aforementioned developments have led to significant scholarly interest that delves into the dynamics of internationalization via emerging and advanced technologies (Bosma and Witteloostuijn 2024; Lee et al. 2023). Often referred to as “Industry 4.0” it emphasizes the critical role of intelligent machines and smart automation of business activities, advancing a vision of a workplace that values interconnectivity, smart automation, machine learning, and real-time data (Luo and Zahra 2023, p. 404). Indeed Luo and Zahra (2023) commented that such advancements are transforming IB operations. Yet, the literature on this crucial phenomenon remains somewhat fragmented and dispersed. We fill this void by conducting a comprehensive bibliometric analysis of this burgeoning literature stream and synthesizing the findings in a meaningful way (Donthu et al. 2021a, b; Mukherjee et al. 2022). This will help in updating scholars on the status of the discourse and advance the ongoing dialogue in the field (e.g., Kraus, Mahto & Walsh 2023).

Our investigation revealed a number of previous review papers that examine the intersection of technology and IB. For example, Laplume et al. (2016) and Hannibal and Knight (2018) investigated the application of additive manufacturing and three-dimensional (3-D) printing to bolster the multinational production capabilities of cross-border corporations. Additionally, several studies have explored the impact of technologies falling under the umbrella of “Industry 4.0” on global value chains and the IB landscape (Strange et al. 2022; Strange and Zuchella, 2017).

Although these published reviews primarily adopt a narrative approach, providing insights and suggestions for future research endeavors involving the integration of new technologies into IB practices, it is noteworthy that the aforementioned studies did not encompass emerging and advanced technologies. Notably absent are innovations such as big data analytics, blockchain, simulation, cybersecurity, augmented reality, and cloud computing, all of which are categorized as Industry 4.0 technologies (Sahoo 2021). Thus, our paper aims to bridge this significant gap in the literature, offering a fresh perspective to propel the field forward.

Acknowledging that a prior similar study conducted by Ahi et al. (2022) also undertook a comprehensive review on a related topic, albeit with a broader scope encompassing articles from 60 journals spanning different content areas such as information systems (18.33%), operations research (30%), general management (35%), and IB (16.67%) from 2011 until 2020, and considering the increasing scholarly interest in the topic specifically within the IB area, our point of departure was the obersevation that a more substantial volume of relevant research has emerged in the meantime. As such, our current review is positioned to offer a more nuanced as well as comprehensive understanding of how various emerging technologies within the Industry 4.0 realm can enhance IB research methodologies, drawing insights from the forefront of IB scholarship.

Thus, we propose to answer the research questions (RQ) listed below to address the concerns stated in the preceding discussion::

RQ

What are the primary themes of Industry 4.0 emerging and advanced technologies in the IB literature?

To offer avenues for interested scholars to advance the technological perspective of IB With a focus on this analytical perspective, this study aims to achieve several objectives: Firstly, it seeks to enumerate the diverse methodological trends utilized by scholars in their examinations of emerging technologies for IB. Secondly, it aims to identify the primary application areas of Industry 4.0 technologies for international commerce and trade based on literature sourced from top-rated IB journals. Lastly, it endeavors to provide avenues for interested scholars to further advance the technological perspective within IB research in the future.

The remainder of the paper is structured as follows. Section 2 provides readers with a background on disruptive technologies within Industry 4.0. Section 3 outlines the methodology used for the study, while Sect. 4 summarizes the findings. Section 5 proposes future research avenues before ending the article in Sect. 6.

2 Foundational elements of industry 4.0 technologies

The modern economic order has been shaped by the three successive industrial revolutions. Mechanization, steam power, and water power were the key driver of the first industrial revolution, while electricity powered mass production and assembly lines was critical to the second revolution (Autio et al. 2021). The third industrial revolution was driven by electronics, information technology systems, and automation, paving the way for the fourth industrial revolution, also known as Industry 4.0, which is linked to cyber-physical systems.

Industry 4.0 refers to the increasing tendency toward business automation, predominantly linked with the manufacturing sector (Bhatti et al. 2022), but it may also exends to other sectors like supply chain (Soni et al. 2022), finance (Wang et al. 2021), and infrastructure (Chauhan et al. 2021). It envisions a future where technological connectedness is expanded and integrated into complex business processes, digital platforms and ecosystems, and digitally connected marketplaces(Barbieri et al. 2022; Jankowska et al. 2021; Luo and Zahra 2023). This fosters an accelerated physical experience of conducting business, encompassing multi-entity conversation, multi-location manufacturing, end-to-end distribution, and remote supervision, in what is known as the physical-to-digital-to-physical loop (Ahi et al. 2022; Sahoo 2021).

The successful deployment of Industry 4.0 depends on the technical interoperability of its nine fundamental elements. These nine elements, often referred to as the Nine Pillars of Industry 4.0 (Leong et al. 2021), are: big data analytics, the Internet of things, additive manufacturing, system integration, cloud computing, autonomous robotics, simulation, augmented reality, and cyber security. A seamless interaction between physical assets and digital infrastructure through these nine pillars is critical for real-time operations and realizing the benefits of Industry 4.0.

Emerging technologies under the umbrella of Industry 4.0 offer organizations the opportunity to leverage them across their entire value chains (Castagnoli et al. 2021; Oliveira et al. 2021; Pananond et al. 2020), driving operational excellence and business expansion in multiple areas from products and services to global supply chains. This allows organizations to better serve and satisfy key stakeholders including employees, partners, and customers. The following section provides a brief description of the nine pillars to enhance the understanding of the readers.

  • Big Data Analytics: Analyzing massive datasets (i.e., big data) is crucial in Industry 4.0. This data is generated through various sources like sensors, social media, videos, and business records(Lin et al. 2022; Lee et al. 2023). Businesses can gain valuable insights by leveraging big data analytics to optimize their operations and gain competitive advantage.

  • Internet of Things (IoT): The IoT is the network of interconnected devices, gadgets, appliances, machinery, equipment, and other intelligent entities that collect and transmit real-time data about the physical world(Kumar et al. 2022). This data is critical for intelligent decision-making and automation within Industry 4.0.

  • Additive Manufacturing (3D Printing): This technology allows the creation of objects with precise geometric measures using computer-aided design (CAD) and 3D scanners (De Beule et al. 2022; Laplume et al. 2016). Additive manufacturing may allow businesses rapid prototyping and on-demand production.

  • Systems Integration: This pillar encompasses methods ofdigitalization that connect different functionalities within a business, either horizontally across departmentsor vertically throughout the supply chain (Yevu et al. 2021). Horizontal integration allow organizations to focus on core competencies and collaborate with partners (Chiarini and Kumar 2021), while vertical integration fosters effective management of the entire value chain in-house (Devi et al. 2021; Narula et al. 2022).

  • Cloud Computing: This pillar stores data and programs on Internet servers servers rather than local hard drives (Duman and Akdemir 2021). Cloud computing platforms faciliate communication and coordination across cyber-physical systems, forming the backbone for many Industry 4.0 applications(Farahani et al. 2022).

  • Autonomous Robots: These intelligent machines, powered by the IoT, are capable of performing repetitive or hazardous taks within smart factories (Autio et al. 2021; Bibby and Dehe 2018). Such robots can sense their surroundings and operate independently for an extended periods thereby increasing efficiency and safety.

  • Simulation: This pillar is critical for developing models to improve decision-making, design, and management of complicated systems within Industry 4.0 (Duman and Akdemir 2021). Simulations allow businesses to evaluate different scenarios using artificial intelligence and machine learning to improve decisions and operations (Ferreira et al. 2022; Kumari and Kulkarni 2022).

  • Augmented Reality (AR): This technology overlays digital information such as text, images, and sound on to the physical realm(Ahi et al. 2022; Choi et al. 2022). The addition of computer-generated graphics and information creates an interactive environment, which has potential to enhance efficiency and effectiveness of operations and workforce with in businesses within Industry 4.0 (Sharma et al. 2022).

  • Cybersecurity: Given the increased automation and data exchange, robust cybersecutity measres are essential in Industry 4.0 (Narula et al. 2022). A comprehensive cybersecurity plan that addresses vulnerabilities and protects sensitive data of businesses is critical for the acceptance and adoption of Industry 4.0. Blockchain technology shows promise in ehnacing cybersecurity risk management within Industry 4.0 (Ahi et al. 2022; Rabby et al. 2022; Strange and Zucchella 2017).

Multinational corporations can achieve significant advancements in manufacturing, exploring new markets, and expanding their IB activities by leveraing the nine pillars of Industry 4.0.

3 Review methodology

3.1 Planning the literature review

Considering our RQ and the background on the nine pillars of Industry 4.0 in the second section, the first set of keywords relevant to them was determined for bibliometric analysis. This review method premised upon bibliometric analysis utilizes various quantitative methodologies to examine patterns, relationships, and trends found in a collection of publications (Donthu et al. 2021a, b). As shown in Fig. 1 and 18 keywords linked to different technologies under the banner of Industry 4.0 were entered into the digital database as a query string using Boolean operator “OR”. This search strategy was refined further by integrating the Boolean operator “AND” with eight leading journals in the IB domain, which have been inspired by the work of Gaur and Kumar (2018). To further support our decision to include these top-ranked journals in the search query string, we consulted the Academic Journal Guide (AJG) endorsed by the British Chartered Association of Business Schools (CABS), where these eight journals were found to be rated 3 or higher. Mukherjee et al. (2021) also supports this core IB journal list. They are as follows: The Asia Pacific Journal of Management, Global Strategy Journal, International Business Review, Journal of International Business Studies, Journal of International Management, Journal of World Business, Management International Review, and Management and Organization Review.

Fig. 1
figure 1

Review methodology. Note APJM– Asia Pacific Journal of Management, GSJ– Global Strategy Journal, IBR– International Business Review, JIBS– Journal of International Business Studies, JIM– Journal of International Management, JWB– Journal of World Business, MIR– Management International Review; MOR– Management and Organization Review; All publications from one specific year are grouped together by a similar colour; The number next to the journal’s short name represents the number of publications in a particular year

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Scopus was the digital database of choice for this study since it has been utilized by several researchers in the field (Barbieri et al. 2018; Klarin et al. 2021; Kumar et al. 2021; Rialp et al. 2019). When searched on the SCOPUS database on March 30, 2023, the combination of search keywords used in conjunction with the predefined source titles yielded 179 documents, which included 165 articles, 3 notes, 3 editorials, and 8 reviews. Following cues from prior studies (Khandelwal et al. 2022; Pandey et al. 2022; Sahoo et al. 2022). We confined our inclusion criteria to just the (academic) “articles” category published after 2010 to ensure relevance to the scope of investigation proposed in the current review. There were 108 articles in the review corpus after a comprehensive evaluation of abstracts and full-text of the publications. However, as seen in Fig. 1, the review corpus comprises articles from 8 journals, with the maximum being 23 articles from International Business Review, followed by 22 articles from the Journal of International Management and 18 articles from the Journal of International Business Studies, 8 in Management International Review and the rest in other IB journals. To ensure that we did not overlook any potential research that may be included in the review corpus, we visited each journal’s website. We performed abstract screening on all articles published since 2010. We confined our review corpus to 108 articles for further analysis after completing this laborious screening process.

3.2 Analyzsing the review corpus

Considering the primary objectives proposed in the introduction section, we align our analysis strategy accordingly. The primary objective was to determine the key thematic structure of the review corpus that explicitly or implicitly reflects on the application areas of Industry 4.0 technologies for IB. Although there are numerous scientometrics methods for determining the thematic structure of any review corpus (Donthu et al. 2021a, b; Perianes-Rodriguez et al. 2016), we choose the “coupling by document” method (tool available in “Biblioshiny-R” software package) to achieve the aforementioned objective. There are numerous reasons for using this method (Aria and Cuccurullo 2017), including the fact that it provides a cluster of keywords bounded by the degree of coupling strength to represent a unique theme, as well as reflects on the articles associated with every theme.

Following that, we intend to use a content analysis approach to review the articles in each labelled cluster due to the coupling by document method to interpret the common crux of each thematic cluster. Drawing inspiration from a prior study (Gaur and Kumar 2018), we use coding categories to define the research design, method of data collection, kind of data collected, type of sample/respondents, analysis methodology, and type of interpretation, the explanation of which is summarized in Table 1.

Table 1 Coding scheme for content analysis
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3.3 Reporting the findings

108 papers published in eight leading journals in the field of IB attempted to represent, either overtly or covertly, the application areas of emerging technologies under the canopy of Industry 4.0. Figure 2 depicts a tree map that summarizes a descriptive summary of the publishing trend in these journals from 2010 to first quarter-2023. As seen in Fig. 2, the topic of concern exhibited a single-digit trend until 2020, with a peak in 2019, and then a double-digit trend after that. The findings of the scientometrics assessment and the content analysis are outlined in the following subsections.

Fig. 2
figure 2

A tree map of publishing trends on emerging technologies for international business

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4 Findings of methodological trend analysis

A concise summary and interpretation of the findings of the content analysis is presented in Table 2. Starting with research trends, it appears that the majority of studies in the review corpus (34.26%) used a cross-sectional design, while 29.63% used a longitudinal design design. While there was no explicit or tacit explanation of the research design used in 21.30% of the studies in the review corpus. Second, when it comes to data collection methods, archives (36.11%) are the most preferred data source, followed by surveys (25.93%) and interviews (22.22%). Researchers have been interested in understanding the impact of developing technologies on internationalization from a macroeconomic standpoint, as a consequence of which economic variables (19.44%) seem to be the most chosen unit of data analysis. Data gathered in the form of closed-ended (17.59%) and open-ended responses (16.67%), that are congruent with their respective principal method of data collection (i.e., surveys and interviews), also dominate the coding category linked to data type. Next, when it comes to the demographic characteristics of the sample or respondents selected for unit of analysis, firms (44.44%) appear to be the most preferred, followed by employees (18.52%) and countries (10.19%). Among the various principal analysis methods utilized by researchers in the review corpus, regression seems to be the most commonly used (26.85%), followed by structural equation modeling (16.67%), mathematical modeling (12.04%), descriptive analysis (11.11%), and content analysis (7.41%). Lastly, quantitative interpretation (47.22%) outnumbers qualitative interpretation (28.70%) in the coding category “interpretation type”.

Table 2 Summary of coded categories observed in the review corpus
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5 Findings of bibliographic coupling analysis

To comprehend how IB activities are being handled by technological advancements, one of the primary objectives of this investigation was to determine the thematic structure on which researchers in the IB domain have been focusing. To achieve this, we based our analytical effort on the “coupling by document” approach, which is in fact a bibliographic coupling technique (Aria and Cuccurullo 2017; Kaur 2022; Saini et al. 2022). Bibliographic coupling is based on a similarity metric described by “coupling strength”, which is stronger the more citations to other documents they share (Abhishek and Srivastava 2021; Donthu et al. 2021b). It indicates that the two publications are about the same subject matter, recognizing the common themes (Aria and Cuccurullo 2017). In simpler words, “Coupling by document” in bibliometric analysis refers to the connection or relationship between different research articles based on their shared references or citations. The use of this method in the Biblioshiny software package, using the author’s keyword and global citation score as standard units of measurement led to the identification of nine distinct thematic clusters, as depicted in Fig. 3. The generated visual map, as shown in Fig. 3, is a four-quadrant map with “impact” parameter on the x-axis and the “centrality” parameter on the y-axis. The “Centrality” values highlight the most relevant nodes (i.e., articles) within a review corpus, showing that the higher the score, the stronger the coupling strength between articles in the cluster (Aria and Cuccurullo 2017; Singh and Ravi 2022). While the “impact” metrics reflect on the cluster’s normalized aggregated citation performance, the higher the score, the more articles in the cluster are referred to by other academics (Aria and Cuccurullo 2017; Donthu et al. 2021b; Perianes-Rodriguez et al. 2016). The observations depicted in Fig. 3 are supplemented by the information in Table 3, which shows the number of articles on a similar theme (frequency), centrality score, impact score, and article within each cluster. The coupling map was generated using 60 of the 108 articles and generated by the software to identify the most substantial establishing themes based on content commonality. Making use of content analysis, we have been able to pinpoint the overarching themes of each cluster, which has been briefly summarized as follows.

Fig. 3
figure 3

Visual results of “document by coupling” analysis

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Table 3 A descriptive summary of results of “documents by coupling” analysis
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5.1 Cluster 1: internationalization of R&D activities

As seen in Table 3, cluster 1 contains the most articles (17 in total). The prominent author’s keywords covered in these articles include “simulation”, “internationalization”, and “innovation”. Furthermore, this cluster has the maximum centrality score of 1.644, indicating that a significant number of nodes (articles) from other clusters are incident upon nodes in this cluster. The cluster is also positioned third on the impact metrics, with a score of 2.021, suggesting that it comprises some of the review corpus’s most impactful articles. These articles vary in context and essentially reflect on two main topics: globalization of R&D activities on the one hand, and scenario simulation on the other. We make an effort to describe the background of each topic independently.

In today’s dynamic business landscape, aggressive competitiveness has compelled a new dimension of knowledge acquisition activities for managing product/process innovation to suit the local context, for which multinational firms are now constantly pressured to expand their R&D efforts outside of their home regions and work collaboratively with diverse entities in expansion markets (Hsu et al. 2015; Mukherjee et al. 2019, 2023). Technological advancements that offer cost savings and allow for more regionalization of technology transfer (Mithani 2023), as well as better access to global research capabilities, all contribute to internationalizing R&D (Chandra and Wilkinson 2017; Zahra 2021). The cluster has also considered how foreign direct investments in innovation-focussed activities, cross-border mergers and business acquisitions can help countries strengthen their innovation systems (Flores et al. 2013; Santos-Arteaga et al. 2017; Sarwar et al. 2023; Zahra 2021), allowing multinationals to perform more knowledge-intensive functions, handle more advanced equipments, and manufacture more complex products. Cloud technology provides the possibility to improve knowledge exchange and collaboration in R&D internationalization activities (Bouncken and Barwinski 2021). The term “simulation” appears to be a common node connecting all articles discussing a range of topics relating to international entrepreneurship (Chandra and Wilkinson 2017; Vuorio and Torkkeli 2023; Zahra 2021), innovation (Brouthers et al. 2022; Cano-Kollmann et al. 2018; Liu 2012), or FDI (Eapen 2013; Flores et al. 2013; Wang et al. 2021). Simulation is a popular approach of analysis in this cluster for modelling the outcomes of real-life events in international trade and commerce (Stahl et al. 2012). While simulation is not explicitly defined as a technological implementation of Industry 4.0, we advocate it as a viable application area owing to the nature of recreating real-world environment and deriving possible outcomes.

5.2 Cluster 2: international ambidexterity

This cluster comprising of 8 articles has a centrality score of 1.137 and an impact score of 1.265 (as seen in Table 3), is dominated by three important keywords: big data, ambidexterity, and country-risk. The capacity of an organization to effectively manage current operations while simultaneously being agile enough to respond to the unpredictable demands of the future is what experts call “ambidexterity” (Shamim et al. 2020; Shams et al. 2021). There is a growing need for businesses to do real-time analysis of semi-structured and unstructured data as a way of confronting the challenges of internationalization and reducing the uncertainty (risk) associated with entering new markets or new country (Banalieva and Dhanaraj 2019; Brown et al. 2015). As a result, it can be inferred that big data analytics can play a crucial role in promoting global development (Aguinis et al. 2020; Shamim et al. 2020). Articles in this cluster appear to have linked the opportunistic and experimental activities of businesses with an increased reliance on the technological application of big data analytics, machine learning, and artificial intelligence (Autio et al. 2021; Grant and Phene 2022; Grimpe et al. 2022). These advancements would result in better knowledge management and higher process standardization (Grant and Phene 2022), which would help businesses expand and perform better in a variety of fields, including robotics (Autio et al. 2021), cross-cultural understanding (Field et al. 2021), managing human capital in the subsidiary network (Grimpe et al. 2022), and reducing market entry risk (Wu et al. 2019), among other domains.

5.3 Cluster 3: IoT-enabled global value chains

As illustrated in Table 3, this cluster comprising of 8 articles is predicated on the author’s keywords—global value chains, digitalization, and the Internet of Things (IoT) — and ranks fourth in centrality score (1.341) and fourth in impact score (1.931). A product offering, which may be characterized as moronic, already is dominating the traditional global value chain of several business entities, but thanks to the inclusion of advanced IoT technologies, the product has been elevated to the status of an intelligent participant in its own value chain (Chauhan et al. 2021; Kumar et al. 2022). The IOT is revolutionizing business practices and playing a key role in the transformation of business interactions, distribution networks, consumer engagement, and product pricing, and its continual expansion is changing the fundamental structure of how we understand the product value chain (Ahi et al. 2022; Strange et al. 2022). Because of the interconnected nature of the sensors and machines that make up the IoT ecosystem, new digital platforms and infrastructures are being developed to facilitate communication, innovation, real-time monitoring, and the sharing of information among various stakeholders (Gooderham et al. 2022; Sahoo 2021). This has aided in the explosive growth of the corpus of information accessible to key individual decision-makers and commercial entities, as well as in their ability to make fast decisions and build confidence in the autonomous systems that involve economic transactions (Gowtham and Pramod 2022; Sinkovics et al. 2011). Such progressing digital revolution in global value chains has witnessed transformation dynamics in the socio-technical framework of network firms in the product value chain (Oliveira et al. 2021), spurring research into action areas such as uniformization of digital infrastructure (Bhandari et al. 2023; da Fonseca et al. 2023; Gooderham et al. 2022), power structures among partnering businesses (Pananond et al. 2020), and subsidiary network administration (Paul and Gupta 2014).

5.4 Cluster 4: offhsoring, reshoring and location choice

The fourth cluster consists of 6 articles, focusing on Industry 4.0, backshoring, and offshoring as its main keywords. The cluster is ranked number eight for centrality and number two for impact. The cluster focuses primarily on articulating businesses’ strategic reshoring decisions to externalized manufacturing and service activities (i.e., offshoring) back to their home country (i.e., backshoring/reshoring). Such businesses often lack the requisite industrial infrastructure to keep an eye on operations in real-time and are instead relying on various sensors for aging machineries to gather data that can be used for decision-making (Ancarani et al. 2019; Dachs et al. 2019). It has been recognized that data gathering for operational activities is still a painstaking system, which is time-consuming, error-prone, and potentially hinders the effectiveness of strategic reshoring (Bhatti et al. 2022; Brown et al. 2015). Businesses seeking facility relocation or expansion are increasingly demanding real-time data acquisition, as they are required to deal with large volumes of data produced by sensors deployed in industrial systems, necessitating the use of intelligent analytics and connected technologies (Ancarani et al. 2019; Mukherjee et al. 2023). Reshoring automation is made possible by Industry 4.0 technologies, which enable companies to better manage their distributed production facilities and supply chain networks (Barbieri et al. 2022). As new connected technologies emerge, several experts have examined how much businesses may benefit, taking into account the differing economics of transaction costs (Bhatti et al. 2022; Dahlgrün and Bausch 2019; Jankowska et al. 2021; Makarius et al. 2020). Prior scholarly evidence suggests that such expansion strategies are associated with the adoption of Industry 4.0 when the firm’s priorities include manufacturing high-quality products and reducing expenses associated with non-conformance (Ancarani et al. 2019; Barbieri et al. 2022; Dachs et al. 2019). There is a common thread running through the articles in this cluster: a desire to comprehend more about the factors that influence the location decisions of multinational enterprises and about the parameters that seem to offer the most advantageous configuration when thinking about where these businesses should be located and how their operations should be distributed to maximize the value created in global value chains. As a consequence, some studies have emphasized on innovation as well as R&D initiatives (Barbieri et al. 2022; Jankowska et al. 2021), while others have focused on developing an adaptable culture for all subsidiaries networks for fruitful reshoring projects (Dahlgrün and Bausch 2019; Field et al. 2021). The articles herein draw a clear connection between reshoring initiatives and multinational firms’ substantial investment in Industry 4.0 technologies.

5.5 Cluster 5: digitized governance

The fifth cluster, comprising 8 articles, ranks second in terms of centrality score (1.462) and ranks first in terms of impact score (2.323). Information technology, digital economy, and governance are the three important keywords that comprise the cluster. Undoubtedly, technology can be seen as a crucial facilitator for governance and providing services to citizens and organizations in an economy. Because of this, the notion of a “digital economy”, defined as an economy dependent on computing infrastructure and digital technologies, has emerged (Banalieva and Dhanaraj 2019; Tatarinov et al. 2022). Governing the transformation to a digital economy demands for policy initiatives that may pave the way for the widespread use of digital technologies by individuals, governments, and businesses (Ma and Hu 2021; Thompson et al. 2021). As a result, a majority of articles in this cluster have discussed governance from both a macroeconomic and microeconomic perspective, either in the form of policy suggestions or critiques (Tatarinov et al. 2022). Institutionally speaking, a main impediment for most large businesses is the need to monitor and control subsidiary firms in their global value chain network without complete faith in those firms’ management. The cluster has also witnessed the potential of combining various information and communication technologies (for e.g., blockchains, the internet of things, big data analytics etc.) to develop a governance mechanism for a focal firm that ensures compliance with social sustainability standards, performance monitoring, and the facilitation of financial transactions with its network firms (Ahi et al. 2023; Banalieva and Dhanaraj 2019; Chen and Kamal 2016; Jean et al. 2010).

5.6 Cluster 6: global smart factory

The sixth cluster, which consists of 5 articles, is centered on the keywords additive manufacturing, 3D printing, and global factory, and is placed third in terms of centrality (1.381) and lowest in terms of impact ratings (0.477). This implies that the thematic underpinning of the cluster is substantially pertinent to the area of investigation while having had little impact so far, perhaps because of the newness of the topic. Manufacturers confront difficulties when trying to digitally expand internationally since both their institutional and technological infrastructures weren’t built to scale in such a manner (Celo et al. 2018; Celo and Lehrer 2022). Empirical evidence has demonstrated that a firm’s ability to make globalization (or localization) decisions increases in direct proportion to how fast it recognizes the potential of digital technologies (Lee et al. 2022). Researchers investigated the structural and geographical quirks of international commerce, uncovering various typological architectures of global manufacturing and value-creating technologies, leading them to postulate the benefits of smart factories as a paradigm for addressing various prevalent operational issues (Bouncken and Barwinski 2021; Hannibal and Knight 2018). The focus on digitalizing manufacturing operations has resulted in industrial facility innovations (3-D printing, additive manufacturing, automation, adaptive computer numerical control machines, and the internet of vehicles) that are establishing a sophisticated artificial intelligence-fuelled analytics and management system driven across the global value chain (Bouncken and Barwinski 2021; De Beule et al. 2022; Sahoo 2021). The advent of the “smart factory” aims to do away with the necessity for proactive manufacturing and replace them with a more robust and responsive approach to manufacturing management. As little is known about this concept from the perspective of internationalization of production (Bouncken and Barwinski 2021; Celo et al. 2018), articles in this cluster have promoted additive manufacturing (also known as 3D printing) as a disruptive technology that changes the way production is structured across geographical and organizational barriers (De Beule et al. 2022; Hannibal and Knight 2018).

5.7 Cluster 7: global project management

The seventh cluster is one of the three smallest clusters, consisting of 3 articles, and has a centrality score of 1.341 and an impact score of 1.847. Prominent keywords in this cluster include “global virtual teams”, “cultural intelligence”, and “communication accommodation”. When many business entities socially engage informally or without formal agreements to pursue shared strategic goals, an alliance network is established (Kang et al. 2014). This occurs when organizations do not make their own products but instead rely upon a complicated network of business entities that provide them with specialized products or commodities (Shi et al. 2014). Collaboration is frequently associated with the manufacturing operations, but it may also be associated with R&D activities or the exploring of expansion into new markets (Ahi et al. 2022; Celo and Lehrer 2022; Chadee et al. 2017). For effective collaboration, businesses may need to limit their own absorptive capacity or raise the openness of their operations in order to facilitate learning with alliance partners (Sahoo et al. 2022). Empirical studies have shown that knowledge sharing or social exchange enabled by emerging information and communication technologies has resulted in a variety of strategic benefits for alliance network organizations, including new market penetration, technological innovation and high-performance work systems, among others (Bartol et al. 2009; Kim et al. 2018; Malik et al. 2021; Xiao and Björkman 2006). For example, using artificial intelligence technologies, multinational enterprises may reproduce or reassemble their various expertise in order to assure integration of knowledge in the alliance network (Malik et al. 2021; Messner 2022). In such cases, it is critical for businesses to develop their cultural intelligence or cultural quotient, which is their ability to communicate and operate well across cultures when working with workforce or subsidiary organizations from other nations (Presbitero 2020, 2021). Both articles in this cluster are predicated on communication accommodation theory, which is a form of social identity theory and is concerned with individuals learning to communicate by reducing social disparities. These studies revealed that people have the capability to mould themselves in every scenario in order to minimize social inequalities and have proposed strategies to strengthen it.

5.8 Cluster 8: global human resource management

The eighth cluster, which includes 3 articles and is grounded on artificial intelligence, machine learning, and cultural heterogeneity (as prominent keywords. The cluster has a centrality value of 0.796, ranking lowest, and an impact score of 1.228, ranking seventh among all clusters. Building on the discussion in Cluster 8, this cluster is a bit distinct since it asserts two articles that endeavour to discuss how artificial intelligence and machine learning are streamlining human resource functions across the entire employee lifecycle or across the network value chain of businesses (Malik et al. 2021; Messner 2022). Indeed, machine learning and artificial intelligence may be used to churn through enormous amounts of cognitive or psychological data to uncover behavioural, cultural, or cross-cultural patterns and thereafter anticipate future occurrences, which can then be utilized to improve global workforce or organizational efficiency (Sadler-Smith et al. 2022). Articles in the cluster emphasized how digitalized platforms are being built to be effectively positioned to give businesses with access to a global talent pool of highly qualified workers. From the articles in this cluster (Malik et al. 2021; Messner 2022), it can be deduced that multinationals are now changing their human capital management strategies to take into consideration the ethos of a more culturally diverse workforce.

5.9 Cluster 9: international relations and cybersecurity

The ninth cluster consists of 2 articles, with the keywords cybersecurity, cybercrime, and security policy being the most prominent buzzwords. Table 3 shows that the cluster has a centrality score of 1.211 and an impact score of 1.886. The convergence of information technology and operational technology is at the core of the fourth industrial revolution, ushering in the age of the industrial internet of things, which is now becoming a critical infrastructure of a digital economy (Ahi et al. 2022; Sahoo 2021; Stallkamp and Schotter 2021). With the exponential growth of internet communications, social media platforms, digital commerce, and virtual negotiations occurring around the globe, the relevance of cybersecurity regarding international relations is a topic that can no longer be ignored (Brandão and Camisão 2021). Conventionally, the aforementioned technological infrastructures and systems are unsupervised since they lack inbuilt defences, do not generate transaction records, and do not allow for the installation of safeguarding agents (Carrapico and Barrinha 2017; Carrapico and Farrand 2021). Financial fraud, information theft or misuse, the malevolent motivation to render computer systems inoperable, and the intention to disrupt essential infrastructure and critical business services are just a few of the many motivations behind cyberattacks that have led to the rise of cybercrime in every country or even affecting multiple countries (Rodgers et al. 2019). Furthermore, as major economic powers in the world become more aggressive in certain scenarios (for example, the Russia-Ukraine War), critical infrastructures are becoming the focus of military confrontations, increasing the likelihood of ongoing threats of cyberattacks targeting electrical grids, transportation systems, or water facilities representing a major vulnerability in the future (Rosch-Grace and Straub 2022). As the number of risks posed by ransomware and malware rises (Ramesh and Menen 2020), businesses throughout the world must reevaluate their strategies for ensuring their cyber security (Weismann 2010), as well as the protection of their customers and the integrity of the global economy (Carrapico and Farrand 2021; Rodgers et al. 2019). The two articles make an effort to summarize these debates by offering policy recommendations.

6 Avenues for future research

A content analysis methodology was employed to identify potential areas for further investigation. The future research suggestions of articles in each cluster were meticulously extracted and summarized in an integrative manner to ensure coherence. Drawing on various fragmented traces of evidence left behind by the aforementioned nine clusters, we discovered that the broad application areas of use of Industry 4.0 technologies for global commerce can be divided into four broad categories: sociotechnical factors, technological factors, governance factors, and performance outcomes. Our suggestions for future research avenues are anchored on understanding the interplay of four categorical variables utilizing a comprehensive method of content analysis for recognizing generic cues following the chain of events occurring. To substantiate our argument, we rely on the socio-technical system theory, a zeitgeist for discussing elaborate organizational designs that centre on the interplay between human stakeholders and cutting-edge technology (Pasmore et al. 1982; Walker et al. 2008). The theory advocates for the simultaneous optimization of both humanistic and technological factors in order to produce sociotechnical adjustments that sets the stage for technological transformation, which may also be regulated in certain situations through the interaction of diverse stakeholders (Lee et al. 2022; Parent-Rocheleau and Parker 2022). Assuming this understanding, internationalization of research and development activities (cluster 1), international ambidexterity (cluster 2), global project management (cluster 8), and global human resource management (cluster 9) indicate a sociotechnical perspective, whereas IOT-enabled global value chain (cluster 3), reshoring and location choice (cluster 4), and global smart factory (cluster 7) indicate a technical perspective. Whereas digitalized governance (cluster 5) and international relations and cybersecurity (cluster 6) have a regulatory emphasis. There are also a wide range of performance indicators discussed in context of IB, including financial (Bhatti et al. 2022; Dahlgrün and Bausch 2019), social sustainability (Pananond et al. 2020), environmental (Wu et al. 2019), innovation (Barbieri et al. 2022), and market success (Jean et al. 2015). To facilitate further exploration of the relationships between the aforementioned categories of variables, we present a research framework, shown in Fig. 4 that scholars may use for generalization in the IB domain.

Fig. 4
figure 4

Proposed framework for future research

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Researchers need a theoretical grounding in sociotechnical systems in order to discover the predictors and probable moderators or mediators that might explain the practical deployment of Industry 4.0 technologies in IB. Prior research has argued that top-down as well as bottom-up institutional change are main facilitators of technological change in an organization, and that both are necessary for the success of the infant initiatives that may lead to widespread network-wide acceptance (Brouthers et al. 2022; Jean et al. 2015; Shamim et al. 2020). In addition, it is necessary to evaluate and generalize the findings based on the possibility of changes in context, such as age of firms, digital infrastructure, labour availability, geography and industry (Grimpe et al. 2022; Malik et al. 2021). As we propose the technical implementation of Industry 4.0 for doing business internationally, it is essential to have a clear understanding on the competitive needs of a company and the rationale behind any investments made to enhance specific performance aspects (Pereira et al. 2021; Pruthi and Tasavori 2022). We propose sociotechnical factors such as R&D management, organizational ambidexterity, project management, and human resource management as precursors to technological factors such as adoption choice, speed of implementation, global factory networkization, and reshoring options, as it is anticipated that successful organization of sociotechnical factors will lead to successful introduction of technological factors (Hsu et al. 2015; Shamim et al. 2020; Shams et al. 2021). This may imply that technological factors may serve as a mediator in the interplay between sociotechnical factors and performance factors. It is expected that the better the governing factors, the better the interaction between sociotechnical factors, technological factors, and performance outcomes. This is why policy interventions and regulatory factors may have a moderating influence on this dynamic. From a qualitative understanding, future research should look at how Industry 4.0 technology might affect the velocity at which business enterprises internationalize. This rapid speed of globalization raises the question of whether or not it comes at a price, which researchers should look into. As another area of concern, the ownership structure of companies that make investments in adopting cutting-edge technology as part of a pivotal firm-subsidiary network is where academics may put their efforts to find an explanation (Benito et al. 2022; Strange et al. 2022). Based on this discourse, scholars may use a case study methodology to discuss how different types of funding used in mergers and acquisitions influence the success of a business aspiring for global expansion.

7 Conclusion

In this article, we take a look at how emerging technologies falling under the umbrella of Industry 4.0 are radically changing the face of world commerce. This review inquiry analysed 108 articles based on a narrow emphasis of choice of journals confining to eight leading journals in the international business arena to hunt for either implicit or explicit clues to identify probable application areas of emerging technologies within the “Industry 4.0” bracket. Thematic analysis yielded nine clusters representing studies centred on sociotechnical, technological factors, and governing factors. We propose a research framework based on the theory of sociotechnical systems to assist future researchers working in the field of IB in conducting empirical analysis for the generalization of results across diverse geographic regions. This is accomplished by having drawn on a rigorous content analysis and following the chain of events articulated in articles of review corpus. From a practical perspective, we advocate for the early adoption of Industry 4.0 technologies by businesses so that they can more rapidly expand internationally, make more informed decisions, gain remote access to information, create a more cohesive global value chain, and better manage their stakeholders. This is not a simple task for businesses since it requires many governmental interventions at both the macroeconomic and microeconomic levels. In order to advance their nation to “digital economy” status, policymakers and regulatory authorities should consider their pivotal role and rely on the insights offered in this article to formulate policy for expansion of Industry 4.0 technical infrastructure. Lastly, the review is qualitatively analytical, but it is restricted in its breadth due to the small number of papers included in the study. Therefore, it is recommended that future researchers wait for a considerable period of time, e.g. five years, to conduct a similar investigation with larger sample to highlight upon the evolutionary nature of Industry 4.0 technology and contrast upon the various application areas that would exist at that time. Lastly, the current review investigation focused on comprehending the application of technologies for internationalization. However, future study might concentrate on how internationalization promotes the use of emerging technologies. Internationalization may be convincingly claimed to establish an atmosphere that encourages the development and acceptance of emerging technologies for promoting collaboration, diversity, resource sharing, standardization, and a supportive global market. One final limitation of our study lies in its narrow focus on selected leading journals within the international business arena, which may have resulted in the exclusion of relevant research published in general business and general management journals. However, this methodological choice is widely accepted and has been employed in numerous previous studies, as demonstrated by similar approaches utilized in similar research studies (e.g., Mukherjee et al. 2023; Gaur and Kumar 2018). Future research could broaden this scope.