Industry 4.0 implementation in the supply chain: a review on the evolution of buyer-supplier relationships

ABSTRACT

This paper analyses extant literature on how Industry 4.0 impacts Social Capital in Buyer-Supplier Relationships. We conduct a systematic literature review and identify 36 academic articles that are analysed in the research process. The study uncovers strategic changes Industry 4.0 implies for Social Capital in Buyer-Supplier Relationships. These include transformations in cognitive, structural and relational capital in terms of a shared vision, social interaction and trust. Therein, Social Capital in Buyer-Supplier Relationships is needed and further invested in aspects like common decision-making, information sharing and cross-company integration in Industry 4.0 contexts. We propose that Industry 4.0 implementation does require and foster Social Capital in Buyer-Supplier Relationships and that two diametrically opposed elementary forms of Buyer-Supplier Relationships co-exist in an Industry 4.0 context. The systematic literature review is the first to analyse the extant body of literature on Buyer-Supplier Relationships in Industry 4.0 to synthesise detailed transformations against the backdrop of Social Capital. It provides a comprehensive overview of the current state of research and develops several suggestions for future research and managerial practice, for example, concerning the role of humans in strategic tasks in Industry 4.0.

KEYWORDS:

• Industry 4.0
• internet of things
• social capital
• systematic literature review
• buyer-supplier relationships

1. Introduction

Based on the Internet of Things (IoT) and Cyber-Physical Systems (CPS), Industry 4.0 offers the potential to transform, reshape, and improve value creation processes of individual companies (Kagermann, Wahlster, and Helbig 2013). Given its digital and interconnecting character, however, the full potential of Industry 4.0 cannot be leveraged in company-individual solutions but will only unfold in its entirety in case of a seamless cross-company vertical interconnection (Ben-Daya, Hassini, and Bahroun 2019; Büyüközkan and Göçer 2018; Frederico et al. 2020; Ghadge et al. 2020). This means upstream and downstream supply chain (SC) stages, i.e. suppliers and buyers, are to be further integrated and interconnected (Garay-Rondero et al. 2020; Zekhnini et al. 2021). These changes result in a value-added network and in turn transform traditional SCs (Belhadi et al. 2021; Ghadimi et al. 2019; Hofmann et al. 2019; Yan et al. 2018).

Enabling the required cross-company vertical interconnection to transform SCs towards the requirements of Industry 4.0, changes in the interactions, coordination, and cooperation between buyers and suppliers are predicted (Joseph Jerome et al. 2022; Schiele and Torn 2020). Therefore, these developments affect interactions between buyers and suppliers in buyer-supplier relationships (BSRs), facing challenges of transformation (Burger, Kessler, and Arlinghaus 2021; Burger and Arlinghaus 2021; Patrucco et al. 2021; Veile et al. 2020).

These ongoing changes in SCs are further driven by current events, such as the Covid-19 pandemic, calling for more resilient and flexible SCs (Frederico 2021a, 2021b). Likewise, the seamless cross-company vertical interconnection allows several potentials for sustainable supply chains and enables the Circular Economy (Belhadi et al. 2021; Veile et al. 2020). Hence, more resilient SCs as well as more sustainable SCs require a transformation in BSRs, especially in conjunction with the requirements of Industry 4.0.

Whereas several articles and even literature reviews exist on Industry 4.0 and the nexus of Supply Chain Management (SCM), a comprehensive overview on how Industry 4.0 impacts BSRs from a theoretical perspective is still missing (Büyüközkan and Göçer 2018; Calatayud, Mangan, and Christopher 2018). Social Capital theory offers an expedient framework to analyse these developments since BSRs build upon the core elements of Social Capital – personal contact, trust and interaction between humans (Tsai and Ghoshal 1998; Villena, Revilla, and Choi 2011). In this context, Industry 4.0 is expected to transform and even replace traditional forms of interaction between buyers and suppliers (Nitsche et al. 2021). However, a high degree of coordination and trust between actors is required for close integration of value creation activities in Industry 4.0 (Ardito et al. 2018; Müller, Veile, and Voigt 2020).

Against this backdrop, it is largely unclear how Social Capital in BSRs is affected through Industry 4.0 representing the motivation for our study. Hence, this paper addresses the following research question:

How does Industry 4.0 impact Social Capital in Buyer-Supplier Relationships?

Conducting a systematic literature review (SLR), the aim of this study is to consolidate, synthesise, and structure the fragmented research on BSRs in Industry 4.0, and analyse it from a Social Capital perspective.

For this purpose, the remainder of the paper is structured as follows. Section 2 presents the theoretical background, whereas the methodology of the SLR is presented in Section 3. Then, we present our results in Section 4 and discuss them in Section 5. In Section 6, the study’s limitations are presented, future research directions are discussed and implications for practice are given.

2. Theoretical background

2.1. Industry 4.0 and BSRs

The vision of Industry 4.0 is that people, products, machines and systems communicate and interact with each other along the entire SC up to the end customer (Kagermann, Wahlster, and Helbig 2013). Hence, business processes manage and control themselves autonomously. Thereby various value creation processes, organisational departments, production facilities, and organisational entities within a company are closely interconnected and integrated (Belhadi et al. 2021; Hofmann and Rüsch 2017).

As technological enablers, CPS allow the collection and the IoT the integrated transmission of data, which thereupon can be analysed and exploited using Artificial Intelligence (AI) and Big Data Analytics (BDA) (Strandhagen et al. 2017; Winkelhaus and Grosse 2020). To grasp the full potential of Industry 4.0, the application of these technologies requires integration across companies and across the SC (Ardito et al. 2018; Frederico et al. 2020; Ghadge et al. 2020; Papert and Pflaum 2017).

As a result of the automation of processes across entire departments and across entire SCs, inter-organisational processes, cross-company collaboration, and BSRs are transformed (Strange and Zucchella 2017; Veile et al. 2020).

BSRs represent and describe the dyadic, long-term cooperation between buyers and suppliers. They can be improved by maintaining close cooperation, combining resources and capabilities, and aligning strategies (Vanpoucke, Vereecke, and Muylle 2017). Buyers and suppliers draw mutual benefits from close BSRs (Bienhaus and Haddud 2018; Büyüközkan and Göçer 2018; Haddud et al. 2017).

Due to this comprehensive interconnection and integration, Industry 4.0 takes the interactions between buyers and suppliers to a next level. First, this is as processes are integrated and data is shared across departments and across SC partners, complementing purchasing and logistics processes with, e.g. product information, financial or product data. Second, automated processes including the exchange of sensitive and cross-departmental data require firms to collaborate in a new way and develop their BSRs further (Belhadi et al. 2021; Calatayud, Mangan, and Christopher 2018; De Vass, Shee, and Miah 2018; Ghadge et al. 2020).

2.2. BSRs and social capital

To examine intra- and cross-company activities and BSRs, Social Capital theory (Bourdieu 1986) has been applied successfully in the past (Nahapiet and Ghoshal 1998; Villena, Revilla, and Choi 2011), inclusively in SC research (Autry and Griffis 2008). Close BSRs are generated through Social Capital between SC partners, but also lead to the generation of further Social Capital in the long term. A high level of Social Capital leads to sharing information and supporting other network members to improve and maintain existing relationships (Adler and Kwon 2002; Inkpen and Tsang 2005; Tsai and Ghoshal 1998).

Hence, we analyse BSRs in the context of Industry 4.0 using the perspective of Social Capital theory. Social Capital has been conceptualised by applying three constituting dimensions: a cognitive, a structural and a relational dimension (Nahapiet and Ghoshal 1998). These dimensions affect and promote each other, however, each one has different effects. In current literature the three dimensions of Social Capital are frequently housed under the following three terms:

Shared vision (cognitive dimension) relates to cooperation partners sharing common goals and objectives, similar perceptions and a mutual understanding. This strengthens relationships as similar targets are ought to be met and there is a general consensus on how to reach them (Inkpen and Tsang 2005; Li, Ye, and Sheu 2014; Yim and Leem 2013). In the context of Industry 4.0, a shared vision of how to implement the concept, including which purposes, and with benefit for whom across the SC is vital for it to unfold successfully (Hofmann and Rüsch 2017; Müller, Veile, and Voigt 2020).

Trust (relational dimension) relates to the relationships formed by mutual interactions (trustworthiness, dependability, and mutual respect) constituting the third dimension that positively influences relationships (Inkpen and Tsang 2005; Li, Ye, and Sheu 2014; Yim and Leem 2013). For sharing data and information in the first place, trust is required (Müller, Veile, and Voigt 2020), while the data transparency achieved thereupon offers the potential to further develop trust between partners (Bienhaus and Haddud 2018; Hofmann and Rüsch 2017; Strandhagen et al. 2017).

Social interaction (structural dimension) refers to the interaction between cooperation partners, forming a social structure within networks or network ties (communication frequency, form of communication, network members’ integration). If social interaction is frequent and of high quality, relationships are enhanced (Inkpen and Tsang 2005; Li, Ye, and Sheu 2014; Yim and Leem 2013).

2.3. Research gap

Whereas Industry 4.0 in general aims to decrease social interaction in operative processes, it allows to focus on social interaction regarding strategic tasks. Further, social interaction enables to implement autonomous data exchange across the SC in the first place, as it allows to generate shared vision and trust (Kagermann, Wahlster, and Helbig 2013; Li, Ye, and Sheu 2014; Veile et al. 2020).

While several SLRs have addressed the topic of Industry 4.0 and associated transformations of the SC, no SLR has comprehensively analysed Industry 4.0 and resulting effects on BSRs, especially not in relation to Social Capital. Table 1 highlights how this SLR is distinct and unique compared to extant SLRs in this area, thereby adding a nuanced view on BSRs and Social Capital to current research. In this regard, several existing SLRs mention aspects of BSRs but have a comparably broad focus that does not consider BSRs or Social Capital in depth.

Table 1. Differentiation from literature reviews on Industry 4.0 in a supply chain context.

Author(s) (year)	Time-frame	Focus/research object	Differentiation
Barata (2021)	2012–2020	Tertiary literature analysis of Industry 4.0 and SC implications	Broad focus, not specific on BSRs or Social Capital
Chauhan and Singh (2019)	2011–2019	Literature analysis of Industry 4.0 and SC implications	Not focused on BSRs or Social Capital
Daghar, Alinaghian, and Turner (2020)	1995–2020	SC relationships and SC risks from a Social Capital perspective	Not specific on Industry 4.0
Dias and Silva (2022)	2008–2019	Performance factors for SC sustainability from a Social Capital perspective	Overlaps on BSRs and Social Capital, but focused on SC sustainability and not specific on Industry 4.0
Frederico (2021b)	2015–2021	Synthesis of the concepts of Industry 4.0 and Industry 5.0 and their SC implications	Not focused on BSRs or Social Capital, social aspects are discussed as a target of Industry 5.0
Gebhardt et al. (2021)	2010–2020	Industry 4.0 technologies as enablers of SC collaboration in the Circular Economy	Overlap in the context of SC collaboration, but narrower focus on Circular Economy and no focus on Social Capital
Rejeb et al. (2021)	2016–2020	Impacts of Blockchain technology on SC collaboration	Different topical focus (exclusively on blockchain)
Shamsollahi et al. (2021)	1988–2019	Dynamics of BSRs over time	Not specific on Industry 4.0 or Social Capital
Sillanpää, Shahzad, and Sillanpää (2015)	n/a	Supplier development and BSR strategies	Not specific on Industry 4.0 or Social Capital
Tangpong et al. (2015)	n/a	BSR typologies	Not specific on Industry 4.0 or Social Capital
Tiwari (2020)	Until 2020	Supply chain integration in the context of Industry 4.0	Focuses on specific processes and forms of SC integration, but does not analyse aspects such as BSRs or Social Capital in depth
Zekhnini et al. (2021)	1994–2020	Literature analysis of Industry 4.0 and SC implications	Broad focus, not specific on BSRs or Social Capital

3. Methodology

To answer the research question, an SLR was carried out that identifies, consolidates, analyses, and discusses the current state of research on Industry 4.0’s effects on Social Capital in BSRs. Doing so helps to gain comprehensive insights, conduct novel interpretations, uncover patterns and themes, and accumulate existing and generate new knowledge. We followed the approach for SLRs defined by Tranfield, Denyer, and Smart (2003) as it is a prominent method used for SLRs in the field of operations management and SCM (e.g. Gebhardt et al. 2021). An SLR is transparent, unambiguous, and reproducible as it follows a well-defined and elaborated systematic procedure (Denyer and Tranfield 2009; Fink 2013; Tranfield, Denyer, and Smart 2003). We followed the approach used by Tranfield, Denyer, and Smart (2003) which comprises seven steps: (1) defining search terms, (2) determining time horizon, (3) selecting databases, (4) selecting publication types, (5) selecting articles, (6) classifying articles, and (7) analysing articles.

In the first step, the keywords for the systematic search are defined. Firstly, several keywords were derived from renowned literature on Industry 4.0. Besides, the Scopus (Elsevier) database was scanned for articles that appear to be relevant at first sight, unveiling further keywords used in these articles. This two-stage process led to valid and reliable keywords. Consolidating individual keywords, we generated a search string that was tested and adapted in an iterative process. The final search string comprises two groups of terms interlinked with Boolean logic:

The first group encompasses ‘Industry 4.0’ and ‘Internet of Things’. This is as the term ‘Industrial Internet of Things’ is regarded as an alternative concept to Industry 4.0 in some English-language publications (Kagermann, Wahlster, and Helbig 2013). Further, we refrained from adding further technologies housed under Industry 4.0, such as AI or BDA, as those search terms did not reveal additional results that were relevant for this study.

The second group of terms relates to BSRs and Social Capital, adding ‘Procurement’ and ‘Purchasing’ as search terms. The latter was included as several relevant papers could be identified that cover BSRs but do not refer to these directly in the abstract or title. We refrained from adding search terms like ‘Supply Chain Management’ or ‘Supply Chain’, as those did not reveal additional relevant results in comparison to ‘Procurement’ and ‘Purchasing’.

We iteratively checked for further search terms and combinations, such as ‘Smart Manufacturing’, but refrained from including them, as this procedure did not reveal additional relevant results. The search was restricted to titles, abstracts and keywords to discover relevant papers. Figure 1 visualises the final search string.

Figure 1. Search string.

(‘Industry 4.0’ OR ‘Internet of Things’) AND (‘Buyer’ OR ‘Supplier’ OR ‘Relationship’ OR ‘Social Capital’ OR ‘Procurement’ OR ‘Purchasing’).

The analysis’ time horizon was limited to publications released between 2011 and August 2021 as the concept of Industry 4.0 was first announced in 2011 (Kagermann, Wahlster, and Helbig 2013). This approach ensures all relevant papers published to date are included. The literature search was initially conducted in March 2021, and after the analysis, repeated in August 2021 to ensure the latest publications are included.

To ensure academic quality, we chose peer-reviewed and published academic articles in English from scientific journals as publication types (Denyer and Tranfield 2009). As we focus on peer-reviewed, scientific journals, we used the database Scopus (Elsevier) as it is the largest citation and abstract database, covering over 20,000 peer-reviewed journals and all important publishers. In doing so, we followed the example of several authors in the field and only considered Scopus when regarding academic journals since it provides exhaustive output (e.g. Fahimnia et al. 2015; Soosay and Hyland 2015). Other databases, e.g. Web of Science, mainly lead to redundant results or add a larger variety of article types like conference proceedings and trade journals, which were not regarded in this study.

To ensure sufficient quality and rigour, we did not include papers published in journals that appear on Beall's List of Potential Predatory Journals and Publishers and that were not indexed in the Journal Citation Reports (JCR) by Thomson Reuters and the SCImago Journal Rank, following recent SLRs published (e.g. Gebhardt et al. 2021). We did not add further quality-related exclusion criteria, such as a certain rating in the AJG ranking (Fatorachian and Kazemi 2021). This balances out our intention to add all relevant journal articles while still adhering to quality indicators of academic journals as pointed out in previous literature and being able to handle the number of articles to analyse.

Likewise, we applied further inclusion and exclusion criteria. We included articles from business and management science, but excluded articles that primarily focus on technology and information science, computer science, and mathematics. As it was not our focus, we refrained from including articles purely focusing on information technology or company-internal logistics and transportation. The articles were limited to those with an industrial business-to-business focus. Whereas many Industry 4.0 and SCM articles can be found in Scopus’ engineering-related publication areas, when focusing on Buyer-Supplier Relationships and Social Capital, this number is quite small. Further, except for conference proceedings, no relevant articles in engineering-related journals or other disciplines regarding BSRs and Social Capital could be found.

The articles were selected in a systematic, multi-stage, iterative selection process as shown in Figure 2 to ensure a systematic, replicable, and transparent analysis. First, we scanned the databases for articles that contained the keywords, which were published within the determined period, and that met the defined inclusion and exclusion criteria.

Figure 2. Data selection process.

An iterative process that highlights criteria in each stage for reducing 637 initial search results to 36 articles, the criteria for each stage are described in the text.

Applying this procedure initially reveals 637 articles, which is reduced to 272 articles when limiting the search to peer-reviewed journal articles in English. We skimmed through the articles’ titles checking for business and management science origin, industrial business-to-business and SCM focus, quality level, and relevance from a content perspective. Thereby we excluded articles that were not relevant to our purpose (e.g. purely technical and mathematical articles, non-industrial focus, business-to-consumer focus) and that were not of sufficient quality to ensure consistent focus and reduce bias. In so doing, we retained 97 articles relevant at first sight that went into the final filtering. Afterwards, we read the articles’ abstracts and conclusions in detail, and reapplying the above-listed criteria, kept 36 articles. Lastly, we read the remaining articles in their entirety and further excluded non-relevant articles according to the pre-defined criteria. This step yielded 24 articles.

Following common research practice, we scanned the remaining articles’ citations and bibliographies and ‘further readings’ sections (‘snowball method’) and checked for relevant articles, uncovering further 14 relevant articles. This mainly relates to other SLRs or theoretical papers with a broader focus that did not include the relevant keywords or stated the contents in the abstract. This is as in these papers BSRs or Social Capital were only mentioned as a smaller part of a mostly larger focus of the results. In total, the systematic selection process led us to a final list of 36 academic peer-reviewed articles.

The entire selection process was conducted by our research team, comparing, reviewing, and discussing individual results until agreement was reached, which ensures a high search process reliability and validity. Finally, we classified, compared, contrasted, critically evaluated, analysed, and discussed the final sample’s articles, including several iteration rounds in team. Following Durach, Kembro, and Wieland (2021), in our analysis we aim for a ‘literature review as a contextualized explanation’, using social capital theory as a theoretical framework to explain developments in BSRs with relation to Industry 4.0. This is, to some extent, comparable to a ‘theory refinement’ approach following Seuring et al. (2021), which does not intend introducing new constructs, but using existing theories to explain new phenomena in a given context.

The final literature sample comprises 36 articles published between 2017 and 2020. Whereas the concept of Industry 4.0 initially emerged in 2011 and was formulated in detail in 2013 (Kagermann, Wahlster, and Helbig 2013), the first article on the nexus of BSRs and Industry 4.0 included in the sample was only published in 2017. This can be explained by the fact that initially Industry 4.0 was predominately analysed from a company-internal perspective, and only recently analyses from an SC perspective followed. However, in the recent past, a clear increase in publications can be observed indicating a rising research interest in the field.

Table A1 in the Appendix gives an overview of all research articles reviewed, while Table A2 in Appendix summarises the main findings of the empirical articles among them.

4. Results

The analysis reveals that from a cross-company perspective Industry 4.0 entails several elements implying thorough transformations of Social Capital in BSRs as found and discussed in scientific literature, presented in the following subsection. These changes are of cognitive, structural and relational nature as originated in Social Capital Theory and therefore are clustered in the three associated dimensions shared vision, social interaction and trust (Nahapiet and Ghoshal 1998; Li, Ye, and Sheu 2014; Yim and Leem 2013). Given transformations of individual BSRs across all value creation stages of an SC, Industry 4.0 also leads to alterations of holistic SCs that are presented in the second subsection of the results.

Figure 3 illustrates the coding scheme and summarises accompanying potentials found in the analysis.

Figure 3. Industry 4.0 induced potentials for Social Capital in BSRs.

The coding scheme for article analysis is divided in the three dimensions of Social Capital, as well as holistic supply chain impacts.

4.1. Changes in buyer-supplier relationships caused by Industry 4.0 from a social capital perspective

4.1.1. Shared vision: forecasting, planning and SC transparency

SC partners are ought to generate a shared vision to exploit Industry 4.0 potentials like increased SC efficiency. To do so, processes like forecasting and planning must be aligned, calling for increased data-sharing and transparency and joint decision-making. From a Social Capital perspective, these aspects increase cognitive capital in BSRs.

First, shared analysis and forecasting abilities stand to the fore. In Industry 4.0, especially the intersection of AI and BDA allows comprehensively analysing data and substantially improving forecasting abilities throughout the SC (Calatayud, Mangan, and Christopher 2018; Stank et al. 2019). The analysis and forecasting possibilities help to predict demands, capacities, and risks, and in turn increase SC resilience (Haddud et al. 2017; Ivanov, Dolgui, and Sokolov 2018; Ralston and Blackhurst 2020). Shared real-time information helps to facilitate context- and situation-aware control and to conjointly plan while adapting to changing environmental conditions for all SC partners (Calatayud, Mangan, and Christopher 2018; Haddud et al. 2017).

Industry 4.0 enables an end-to-end SC visibility and a high level of information transparency with real-time access (Dolgui, Ivanov, and Sokolov 2020a; Zelbst et al. 2020). The IoT and digital platforms allow to share and manage data across the SC, which can be analysed and monitored using AI and BDA (Ben-Daya, Hassini, and Bahroun 2019; Calatayud, Mangan, and Christopher 2018). Based on the IoT and CPS, virtual objects serve to control and monitor physical goods and processes. Data is intended to be permanently available, visible to all actors, which increases transparency along the SC (Frederico et al. 2020; Zhang, Zhao, and Qian 2017).

Information transparency in SCs through IoT and comprehensive analyses via BDA and AI allow better and faster common decision making both on strategic and operational levels (Haddud et al. 2017; Osmonbekov and Johnston 2018). Former silo and standalone decisions might be replaced by decisions optimal from a holistic SC perspective (Stank et al. 2019).

4.1.2. Social interaction: negotiation, ordering and communication

Industry 4.0 paves the way for electronic and automated interactions in BSRs. Therefore, less social interaction is required in processes like negotiation, ordering and contracts, particularly when these activities are conducted via digital platforms. However, special emphasis of Social Capital in BSRs is laid increasingly on strategic tasks, where human engagement remains obligatory.

In Industry 4.0, negotiations between buyers and suppliers are automated and parameter-based, grounded on a pre-defined strategic co-creation of specifications (Gottge, Menzel, and Forslund 2020; Osmonbekov and Johnston 2018). For example, intertwining smart contracts and AI enables automatising initiation and negotiation activities (Handfield, Jeong, and Choi 2019). Thus, evaluation, proposal analysis, and pricing approvals become more data-driven and less judgment-driven. Due to higher data transparency, costing approaches change from product-centered to parameter-centered cost allocations, considering ‘hidden costs’ (Gottge, Menzel, and Forslund 2020).

Automated negotiation fosters a change towards data-based supplier selection and results in a supplier base development (Ghadimi et al. 2019). Industry 4.0 could change the structure of the supplier base and the choice of suppliers, eventually adding new suppliers to a network (Gottge, Menzel, and Forslund 2020; Osmonbekov and Johnston 2018). In this context, algorithm-based or supported decisions change the way of supplier selection. This helps overcoming shortfalls of traditional procedures, such as information asymmetries in negotiations (Gottge, Menzel, and Forslund 2020). In addition, an increasing cross-company interconnection and comprehensive information transparency could uncover discrepancies between the focal company’s perception and the actual state of the supplying company in existing BSRs. Besides, altered supplier requirements and demand on capacities (e.g. digital capabilities, digital infrastructure), could transform supplier evaluation criteria towards data and information sharing aspects (Müller, Veile, and Voigt 2020). Literature also discusses that Industry 4.0 enforces outsourcing activities that could lead to including further suppliers as well as insourcing activities making some suppliers obsolete (Strange and Zucchella 2017; Ralston and Blackhurst 2020). These aspects could lead to a consolidation of the supplier base for replaceable products and services as well as including new suppliers with specific capabilities at the same time (Veile et al. 2020).

Literature further discusses automatic machine-to-machine order processes with subsequent call-offs and rebuys. Contracts are expected to be parameter-centered within a given frame, enabling an automated ordering process (Da Silva, Kovaleski, and Pagani 2019; Gottge, Menzel, and Forslund 2020; Hofmann and Rüsch 2017; Osmonbekov and Johnston 2018).

Within parameter-centered ordering, smart contracts are deployed and executed including payment procedures and shipments basing on a defined set of rules that are constantly adapted according to changing conditions (Dolgui et al. 2020b; Ivanov, Dolgui, and Sokolov 2018).

Industry 4.0 further allows real-time integrated information exchange and communication and thus increases SC transparency, as discussed against the backdrop of a shared vision (Bienhaus and Haddud 2018; Stank et al. 2019; Winkelhaus and Grosse 2020). This exchange is of automated and autonomous nature, requiring little or no human interaction (Frederico et al. 2020; Schniederjans, Curado, and Khalajhedayati 2020; Zelbst et al. 2020). The IoT allows a secure identification, traceability and transaction documentation in BSRs. Decentralised data storages enable all buyers and suppliers to share, store, analyse, and act upon the same information (Calatayud, Mangan, and Christopher 2018; Dolgui et al. 2020b; Hofmann and Rüsch 2017).

Hence, humans’ roles in SCs change and humans are either replaced or enhanced by technology so that they can concentrate on strategic tasks (Hofmann and Rüsch 2017; Schniederjans, Curado, and Khalajhedayati 2020; Winkelhaus and Grosse 2020). Based on a comprehensive technological progress, several processes and tasks predominately on an operational level are automated requiring little or no human interaction (Calatayud, Mangan, and Christopher 2018; Ghadimi et al. 2019; Hofmann and Rüsch 2017; Osmonbekov and Johnston 2018). As a result, humans engage in strategic rather than operational tasks, also when interacting with other SC partners in BSRs (Müller, Veile, and Voigt 2020; Ralston and Blackhurst 2020; Veile et al. 2020). Hence, humans continue to play a key role in strategic decisions (e.g. negotiating contractual framework conditions) and in BSRs (Bienhaus and Haddud 2018; Gottge, Menzel, and Forslund 2020; Hofmann and Rüsch 2017).

From a strategic point of view, the application of digital platforms transforms collaboration and social interaction between buyers and suppliers (Hahn 2020; Hofmann and Rüsch 2017; Leminen et al. 2018; Menon, Kärkkäinen, and Wuest 2020). Platforms increase the integration of upstream and downstream value creation stages and cross-company collaboration in BSRs (Da Silva, Kovaleski, and Pagani 2019; Handfield, Jeong, and Choi 2019). Thereby, they enable the coordination of SCs through integrated interfaces and thus reduce transaction costs (Calatayud, Mangan, and Christopher 2018; Gottge, Menzel, and Forslund 2020; Osmonbekov and Johnston 2018; Stank et al. 2019). Holistically, digital platforms even could simplify SCs. Hereby, various actors are connected to one platform via standardised interfaces allowing for direct communication, smooth interaction and reciprocal exchange between buyers and suppliers of different SC stages. This could make intermediary SC stages obsolete, eventually leading to a shorter, more integrated SC (Büyüközkan and Göçer 2018; Hofmann and Rüsch 2017; Menon, Kärkkäinen, and Wuest 2020; Osmonbekov and Johnston 2018).

4.1.3. Trust: integration, partnerships and sharing

Improved traceability, transparency, and consistency of ordering and contracting across the SC increase buyer-supplier trust in Industry 4.0 environments. Especially enhanced integration, coordination, partnerships and sharing affect relational capital and might even lead to collaborative business models (Bienhaus and Haddud 2018; Müller, Veile, and Voigt 2020).

The transformation of BSRs in Industry 4.0 goes in hand with a further partner integration and a convergence of activities (Chen 2019; Falkenreck and Wagner 2017). Comprehensive information sharing increases transparency and traceability along the SC. This allows to make joint decisions, coordinating approaches, and synchronising communication, relating to Sections 4.1.1 and 4.1.2 (Bienhaus and Haddud 2018; Veile et al. 2020). Large-scale data sharing improves partnerships and SC integration enabling real-time adaptability, cross-company coordination and capacity sharing (Calatayud, Mangan, and Christopher 2018; Haddud et al. 2017; Hofmann and Rüsch 2017; Stank et al. 2019). In this context, knowledge, for example regarding technologies or formal and informal processes, becomes a key resource to establish such coordination and capacity sharing (Bienhaus and Haddud 2018; Büyüközkan and Göçer 2018; Frederico et al. 2020). Especially Small and Medium-Sized Enterprises (SMEs) face a dearth in knowledge and specific resources and, thus, need special treatment when being integrated, for instance, as a supplier. Trust can only be built up when the dichotomy between openness and closeness is balanced (Menon, Kärkkäinen, and Wuest 2020; Papert and Pflaum 2017) and all actors can profit equally from cross-company coordination and sharing, without facing the fear of losing competitive advantages or independence (Chen 2019; Müller, Veile, and Voigt 2020; Yan et al. 2018).

Industry 4.0 and underlying technologies hence are expected to develop strengthened and trustful partnerships and BSRs (Bienhaus and Haddud 2018; Chen 2019; Veile et al. 2020). A greater inter-company transparency based on the IoT reduces information asymmetries, impedes opportunistic behaviour, and builds trust between buyers and suppliers (Stank et al. 2019; Zhang, Zhao, and Qian 2017).

Further, Industry 4.0 allows comprehensive resource sharing across SCs (Benitez, Ayala, and Frank 2020; Da Silva, Kovaleski, and Pagani 2019; Strandhagen et al. 2017). Amongst others, especially platforms serve to integrate and share resources (Menon, Kärkkäinen, and Wuest 2020). Industry 4.0 can change BSRs and the actors’ roles rooted on the transformation from transaction-based to sharing and value co-creation approaches (Benitez, Ayala, and Frank 2020; Dolgui, Ivanov, and Sokolov 2020a).

With shared resources and co-creation initiatives, Industry 4.0 does not only enable changing individual business model elements, but it also paves the way for entirely new, highly collaborative business models to emerge (Bienhaus and Haddud 2018; Leminen et al. 2018).

Customised value offers combined with internet-based services increasingly include the suppliers’ data and capabilities (Ben-Daya, Hassini, and Bahroun 2019; Frederico et al. 2020; Hahn 2020; Strandhagen et al. 2017). Herein, buyers can get further involved in the value creation process, for instance, co-creating value offerings on digital platforms (Chen 2019). Cross-company collaboration, platforms, strategic networks, and ecosystems may represent a source of SC innovation and pave the way for new value offerings and business models elements (Bienhaus and Haddud 2018; Benitez, Ayala, and Frank 2020; Hahn 2020; Schmidt et al. 2020).

Against this backdrop, data-driven business models and platform-based business models, building on data from the entire SC network are expected (Bienhaus and Haddud 2018; Leminen et al. 2018; Strandhagen et al. 2017). For example, data without direct value for a supplier might be of value for the buyer to offer a better service to the common end customer (Müller, Veile, and Voigt 2020).

Given that individual BSRs can be found all along an SC between different companies and across all value creation stages, the results also entail holistic alterations Industry 4.0 brings for SCs.

4.2. Holistic SC transformations as consequence of BSR changes

When it comes to strategic cognitive aspects, Industry 4.0 is expected to reshape SC architecture and thinking holistically, transforming traditional BSRs and SCs towards networks and ecosystems (Gottge, Menzel, and Forslund 2020; Schmidt et al. 2020; Strange and Zucchella 2017). The overall vision includes a comprehensive SC orchestration, based on close and continuous thinking and acting as a collaborative and cooperative system. The full potentials of Industry 4.0 can only be achieved through comprehensive interconnection and integration, i.e. in networks and ecosystems (Benitez, Ayala, and Frank 2020; Papert and Pflaum 2017; Strange and Zucchella 2017).

As far as the SC organisation and shape is concerned, two diametrically opposed research streams exist: On the one hand, research expects that SCs transform into comprehensive networks implying a more complex shape and breaking up linear structures (Chen 2019; Dolgui, Ivanov, and Sokolov 2020a; Haddud et al. 2017). On the other hand, literature broaches the issue of Industry 4.0 simplifying complex SCs, representing shorter and less complex SCs, smoothly interconnecting actors in BSRs and leapfrogging SC stages (Zhang, Zhao, and Qian 2017). In either of both designs, new forms of collaboration and novel Business-to-Business-to-Consumer-Relationships might emerge (Falkenreck and Wagner 2017; Strange and Zucchella 2017).

Based on ecosystem thinking, further actors become relevant to focal firms, such as technology firms, platform providers, and universities for reasons such as knowledge exchange and technological developments (Gottge, Menzel, and Forslund 2020; Menon, Kärkkäinen, and Wuest 2020; Strandhagen et al. 2017). Not only thinking but also acting as an ecosystem becomes a key strategic aspect in the shared vision (Büyüközkan and Göçer 2018; Papert and Pflaum 2017; Schniederjans, Curado, and Khalajhedayati 2020). Against this backdrop, literature unveils that the communication, cooperation, interactions, and collaborative approaches are transformed across companies, across SC stages, and across further actors (Calatayud, Mangan, and Christopher 2018; Da Silva, Kovaleski, and Pagani 2019).

In Industry 4.0, it might not be the most competitive company but the most competitive network that will succeed, emphasising the need for a shared ecosystem vision (Schmidt et al. 2020; Strange and Zucchella 2017). Accordingly, literature discusses various forms such as competition of analytics algorithms behind SCs, competition between SC networks, and competition between ecosystems (Dolgui et al. 2020b). Hence, companies are expected to face an increasing market and competitive pressure to implement Industry 4.0 on a cross-company level. However, literature also discusses changing roles in Industry 4.0 ecosystems, as for example a supplier might turn into a competitor or vice versa (Dolgui, Ivanov, and Sokolov 2020a).

5. Discussion and theoretical contribution

5.1. Implications for BSRs

Traditional BSRs are expected to be transformed through Industry 4.0, especially by improving existing bonds and creating new ties between buyers and suppliers (Falkenreck and Wagner 2017; Joseph Jerome et al. 2022; Schiele and Torn 2020). As a result, this will transform BSRs (Burger, Kessler, and Arlinghaus 2021; Burger and Arlinghaus 2021; Patrucco et al. 2021; Veile et al. 2020).

Several authors discuss an improvement of BSRs in Industry 4.0, for example, intensifying coordination and further integration, and aligning and synchronising value creation activities (Haddud et al. 2017; Hofmann and Rüsch 2017). Industry 4.0 allows for comprehensive information sharing (Bienhaus and Haddud 2018; Müller, Veile, and Voigt 2020). This prevents information barriers and increases transparency and therefore trust (Ben-Daya, Hassini, and Bahroun 2019; Dolgui, Ivanov, and Sokolov 2020a; Stank et al. 2019; Zelbst et al. 2020). It further paves the way for a more comprehensive supplier integration (Calatayud, Mangan, and Christopher 2018; Haddud et al. 2017; Hofmann and Rüsch 2017), and enables a synchronisation and orchestration along the SC (Gottge, Menzel, and Forslund 2020; Osmonbekov and Johnston 2018).

Building on these elements, companies may develop further customised and customer-oriented value offerings (Ben-Daya, Hassini, and Bahroun 2019; Strandhagen et al. 2017) and bring cross-company innovations to market (Hahn 2020). For instance, Dolgui, Ivanov, and Sokolov (2020a) describe a value network based on co-creation and co-evolution. Literature discusses that Industry 4.0 enables new cross-company business models building on new forms of BSRs. These include data-driven business models and platform-based business models, involving numerous SC actors (Bienhaus and Haddud 2018; Leminen et al. 2018; Menon, Kärkkäinen, and Wuest 2020). Especially these novel SC business models require intense, trustful BSRs (Bienhaus and Haddud 2018; Strandhagen et al. 2017).

Partially opposed to former findings and argumentations, literature also argues that automated, electronic negotiation, ordering and information exchange results in looser BSRs (Gottge, Menzel, and Forslund 2020; Osmonbekov and Johnston 2018). Therefore, BSRs might also be affected negatively by higher levels of automation and digitisation in SCs (Burger and Arlinghaus 2021). Especially via platforms, companies may address a large, typically unknown supplier base. This implies less intensive, lower quality, and short-term oriented BSRs (Gottge, Menzel, and Forslund 2020; Hofmann and Rüsch 2017; Menon, Kärkkäinen, and Wuest 2020). Despite less intense individual and direct BSRs, the relationships and organisational ties to the platform are intense, given comprehensive technical interconnections.

Based on the theoretical findings, we postulate that there might be typically intense BSRs (at an ever-growing level) maintained to all strategic suppliers, going as far as novel business models based on buyer-supplier cooperation (Bienhaus and Haddud 2018; Hofmann and Rüsch 2017; Strandhagen et al. 2017). In addition, there might also be a high supplied volume handled in BSRs of a rather low intensity (at an ever-decreasing level), where digital technologies and platforms manage all contact points and automatically take over exchanges and interactions (Gottge, Menzel, and Forslund 2020; Hofmann and Rüsch 2017; Menon, Kärkkäinen, and Wuest 2020). Hence, it is to be assumed that both types of BSRs described may coexist in Industry 4.0.

5.2. Impact of Industry 4.0 on social capital in BSRs

Based on extant literature, a decrease in Social Capital in BSRs through automation of processes and decreasing personal contact could be expected (Kagermann, Wahlster, and Helbig 2013; Hofmann and Rüsch 2017). This paper, in contrast, reveals a more nuanced view on how Social Capital in BSRs develops. On the one hand, Social Capital might be required to implement automated processes in BSRs in the first place. This is, as implementing automated solutions requires social interaction, trust and shared vision so that all parties are willing to implement these solutions (Müller, Veile, and Voigt 2020; Yim and Leem 2013). On the other hand, Industry 4.0 even leads to an increase of Social Capital in BSRs, and the generation of Social Capital through Industry 4.0 unlocks several further potentials. Most notably, sharing information and data, enhanced transparency, improved and potentially fairer decisions extend trust and shared vision between buyers and suppliers (Bienhaus and Haddud 2018; Stank et al. 2019; Winkelhaus and Grosse 2020). Hence, automated solutions do not replace Social Capital in BSRs, but give proof that BSRs are actually functioning through data transparency and enhanced possibilities of information sharing. This, in turn, favours further integrating partners and expanding cross-company cooperation and coordination through shared vision and trust (Chen 2019; Falkenreck and Wagner 2017; Tortorella et al. 2021).

Further, this paper extends the findings of extant literature that Social Capital in BSRs positively impacts the willingness to share data or information (Müller, Veile, and Voigt 2020; Yim and Leem 2013). As described above, the technological potentials of Industry 4.0, especially relating to data transparency, need a substantial base of Social Capital in BSRs to be implemented in the first place, and can in turn increase trust and shared vision in BSRs. Hence, Social Capital in BSRs can be expected to take a double role in Industry 4.0: Social Capital serving both as an enabler of Industry 4.0 and being enhanced itself (Li, Ye, and Sheu 2014; Yim and Leem 2013).

Nonetheless, the increasing automation of processes that formerly involved human interaction could be expected to reduce social interaction and therefore Social Capital in BSRs (Gottge, Menzel, and Forslund 2020; Osmonbekov and Johnston 2018). However, a substantial amount of cross-company trust, cooperation and integration is needed to set up and rely on this automated design of processes. Whereas monotonous and repetitive tasks such as ordering might be automated, human interaction can now focus on establishing trust, social interaction regarding strategic alignment in BSRs, and sharing visions (Müller, Veile, and Voigt 2020). Hence, human interaction might be shifted towards tasks where Social Capital in BSRs is built (Veile et al. 2020). Therefore, operational processes might be automated, while strategic tasks, building trust, and sharing visions by interacting in person might be enhanced through Industry 4.0.

The double role of Social Capital in BSRs as enabler of the potentials of Industry 4.0 and beneficiary of Industry 4.0 can impact SC design in a broader sense. This refers to, e.g. closer cooperation and supply chain integration while consolidating supply chains (Schmidt et al. 2020; Strange and Zucchella 2017: Veile et al. 2020). These effects are only regarded secondarily in this manuscript and should be examined further in future research, as explained in Section 6.2.

Further, typical challenges for BSRs can occur during Industry 4.0 implementation, such as diverging potentials or suspected opportunistic behaviour between SC partners that hamper the implementation of Industry 4.0 across the SC (Benitez, Ayala, and Frank 2020; Müller, Veile, and Voigt 2020). Further, lacking trust of SC partners, especially SMEs that need to share data but do not see potentials for themselves yet, lead them to not share data (Müller, Veile, and Voigt 2020). Another challenge represents employees that, by lacking personal contact through automation of processes, lose the understanding of SC partners or see their own role as threatened (Bienhaus and Haddud 2018; Büyüközkan and Göçer 2018; Frederico et al. 2020). All above-named challenges can lead firms to not share data or not cooperate. However, for all challenges, Social Capital has been found to play an influential role to increase the willingness to share data or to collaborate (Müller, Veile, and Voigt 2020).

Figure 4 subsumes the postulated multiple role of Social Capital in BSRs, enabling potentials of Industry 4.0, but also being supported itself by Industry 4.0 implementation.

Figure 4. Multiple role of Social Capital in BSRs and challenges for Industry 4.0 implementation.

All three dimensions of Social Capital in BSRs play a role for (1) prerequisites of Industry 4.0 implementation, (2) challenges during implementation, and (3) potentials through implementation, which are described in detail in the text.

6. Conclusion

6.1. Managerial implications

From our results, several recommendations and implications can be extracted that could serve managers and practitioners.

First, given its relevance, managers should incorporate the understanding of Social Capital and BSRs in the company and SC strategy in detail. A cooperative culture that supports Industry 4.0 implementation is to be established and the organisational structure is to be adequately shaped for Industry 4.0 implementation across companies (Büyüközkan and Göçer 2018; Chen 2019). This must be aligned to the overall requirements of an SC which should be designed and coordinated from end to end to meet the requirements of Industry 4.0 (Frederico 2021a, 2021b).

Second, knowledge is a key resource and both internal and external knowledge may be acquired through recruiting skilled personnel and qualifying internal personnel (Bienhaus and Haddud 2018; Büyüközkan and Göçer 2018; Frederico et al. 2020). As humans continue to play a decisive role in strategic tasks, for example to build Social Capital in BSRs, managers are ought to focus on the employee side as well and refrain from primarily considering technological aspects of Industry 4.0.

Third, building up trust with suppliers, sharing visions and close cooperation, i.e. developing Social Capital, are prerequisites for successful long-term BSRs being the fundament of Industry 4.0 in SCs (Büyüközkan and Göçer 2018; Müller, Veile, and Voigt 2020).

Fourth, the study calls for a differentiated mind-set regarding suppliers, as they often belong to SMEs that may suffer from a lack of knowledge or resources to adopt Industry 4.0 technologies, impeding a comprehensive SC transformation. Hence, somewhat favourably situated companies are asked to provide support (Chen 2019). In order to set incentives for cooperation and to avoid conflicts, a fair distribution of benefits is obligatory so that all partners likewise profit from the interconnections, building trust between SMEs and their often larger customers (Müller, Veile, and Voigt 2020; Yan et al. 2018).

Fifth, trust in data exchange requires considering the dichotomy between openness and closeness, e.g. in terms of data exchange, data possession and usage rights, and data security (Menon, Kärkkäinen, and Wuest 2020; Müller, Veile, and Voigt 2020; Papert and Pflaum 2017). As described by Burger, Kessler, and Arlinghaus (2021), higher levels of digitisation can be a challenge for BSRs. Hence, how to develop those with regard to the above-named aspects remains important in managerial practice.

Sixth, the results unveil that Industry 4.0 can pave the way for new business models based on Social Capital in BSRs (Benitez, Ayala, and Frank 2020). Against this backdrop, managers are encouraged to evaluate possibilities to update existing business models for SCs or ecosystems.

Finally, current events such as increased SC resilience requirements due to Covid-19 or changes in SC design due to requirements for the Circular Economy drive changes in BSRs (Frederico 2021a, 2021b). Those must be better understood by managerial practice, especially in conjunction with the requirements of Industry 4.0.

6.2. Limitations

As with all SLRs several limitations must be discussed (Denyer and Tranfield 2009; Fink 2013; Tranfield, Denyer, and Smart 2003). Carefully choosing search terms, databases, and inclusion and exclusion criteria and conducting cross-referencing, ensures that – to the best of our knowledge – the paper includes all relevant publications in the field. Despite the exercised cautiousness, however, we must admit the selection of inclusion and exclusion criteria influence the final sample with a given probability, and thus, some relevant studies might have been omitted. Further, the final sample and its characteristics influence the findings and implications, which must be kept in mind. To avoid misinterpretations, the methodology section transparently documents the search process and all choices. As Industry 4.0 is still a nascent field being at an early stage – especially when it comes to BSRs and Social Capital (Ben-Daya, Hassini, and Bahroun 2019; Büyüközkan and Göçer 2018) – the paper at hand only claims to depict the current state of research. In addition, the recently growing number of publications suggests further publications are in the offing. As far as the results are concerned, our research lacks a differentiation regarding company characteristics (e.g. company size, position in SCs), industry sectors (De Vass, Shee, and Miah 2018), and geographical and cultural differences (Falkenreck and Wagner 2017; Strange and Zucchella 2017). In addition, the study does not differentiate between buyers’ and suppliers’ perspectives, however, given different determinants and characteristics, implications may vary depending on the perspective.

6.3. Research agenda

Our study presents several avenues for further research, which are described in the following.

First, preparing the transition towards a digitised and interconnected SC requires transformations on both the suppliers’ and buyers’ sides, for example when obtaining the technological basis, building up Social Capital and shaping BSRs. This becomes especially difficult when individual suppliers have to meet differing standards for several buyers, which may also apply the other way round. Research should therefore focus on how to set incentives for suppliers or buyers to take and reduce required efforts. This may include how to distribute benefits (e.g. distributing efficiency gains from real-time capacity information sharing) and how to balance investments, costs and risks between buyers and suppliers (Dolgui, Ivanov, and Sokolov 2020a; Müller, Veile, and Voigt 2020). As a large proportion of SCs are SMEs – with potentially lower resource and knowledge bases – it comes into question how somewhat favourably situated companies can aid SMEs, and thus build Social Capital, so that they could join a comprehensive network (Benitez, Ayala, and Frank 2020; Chen 2019; Da Silva, Kovaleski, and Pagani 2019).

Second, our study calls for future research that empirically underpins the findings regarding the antecedents for and effects of Social Capital and BSRs in the context of Industry 4.0. This includes measuring and analysing actual effect sizes of distinct technologies and demonstrating its practical applicability. In addition, multi-stage SCs and differences in value creation stages (e.g. first tier-suppliers vs. second tier-suppliers) call for more nuanced analyses. Research studies suggest that Industry 4.0 causes SC reorganisation (Strange and Zucchella 2017), demanding further analyses to understand underlying mechanisms. Some yet unsolved dilemmas also call for further research in the context of but also beyond Social Capital theory, such as managing the trade-off between efficiency and flexibility in Industry 4.0 SCs (Dolgui, Ivanov, and Sokolov 2020a) and the dichotomy between openness and closeness in terms of data exchange, data possession rights, and data security (Menon, Kärkkäinen, and Wuest 2020; Papert and Pflaum 2017). Further research could also shed light on actual quantitative outcomes of digital interconnection, for example, efficiency gains and innovation output improvements and potential effects on BSRs (Hahn 2020). The same applies to the effects on SC design following changes in BSRs, that must be understood more in detail (Schmidt et al. 2020; Strange and Zucchella 2017).

Third, the ongoing transformation of SCs is further driven by current events, such as the Covid-19 pandemic, calling for more resilient and flexible SCs (Frederico 2021a, 2021b). In response, the requirements of BSRs must be understood from these perspectives, along with those posed by Industry 4.0. Adjacent aspects like for example new forms of SC collaboration (Gebhardt et al. 2021) must be contextualised with current requirements while improving overall SC performance and sustainability, and therefore form a third avenue for future research. Notably, SC collaboration and BSRs towards establishing the requirements of a Circular Economy could be of particular interest in this regard.

Fourth, the opportunities of Industry 4.0 to generate value across the entire SC must be understood better (Hofmann et al. 2019). In this context, several authors expect new forms of SCs to emerge following Industry 4.0 (Garay-Rondero et al. 2020; Zekhnini et al. 2021). This includes concepts such as ecosystems and digital platforms that are expected to transform SC design significantly (Schmidt et al. 2020; Strange and Zucchella 2017). These new forms of SCs, that might better suit current requirements, such as shorter, more centralised and orchestrated, or platform-based forms of SCs, represent a fourth avenue for future research. In this context, integrating the perspective of multi-tier SCM that considers entire supply chains can be highlighted, integrating for instance the requirements of SMEs (Chen 2019).

Finally, while this SLR can be regarded as a ‘contextualized explanation’, using Social Capital theory as a theoretical framework to explain developments in BSRs with relation to Industry 4.0, we suggest future studies to extend or combine extant theories in order to develop theory-building approaches for SC research with respect to Industry 4.0 (Durach, Kembro, and Wieland 2021; Hofmann et al. 2019; Seuring et al. 2021).

Acknowledgements

We thank the anonymous reviewers for their helpful comments when revising this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article as no new data was created or analysed in this study.

Additional information

Notes on contributors

Marie-Christin Schmidt

Marie-Christin Schmidt, Dr., holds a Ph.D. degree from Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) and is research and teaching associate at the Chair of Industrial Management, School of Business, Economics and Society at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany). She holds a master’s degree in International Business Studies from Friedrich-Alexander-Universität and a master’s degree in Change Management from the University of Alcalá (Spain). Her research interests include Industry 4.0, digital platforms and ecosystems, and digital value creation.

Johannes W. Veile

Johannes W. Veile, Dr., holds a Ph.D. from Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany). Having studied in Nürnberg (Germany) and São Paulo (Brazil), he obtained a master’s degree in Management from Friedrich-Alexander-Universität Erlangen-Nürnberg. Before that, he worked for Voith Group in Heidenheim while studying at the Baden-Wuerttemberg Cooperative State University (Germany). His research interests include strategic cross-company cooperation and Supply Chain Management in the context of Industry 4.0, digital transformation and digital value creation.

Julian M. Müller

Julian M. Müller, Professor Dr., is Full Professor of Digital Business at Seeburg Castle University, Austria. Further, he is Private Lecturer (Privatdozent) at Friedrich-Alexander-Universität Erlangen-Nürnberg, where he also received his venia legendia (Habilitation) and Ph.D. degree. His research interests include Industry 4.0 and Digital Transformation in the context of Supply Chain Management, Technology and Innovation Management, and Sustainability. Since 2022, he is board member of the European Operations Management Association (EurOMA).

Kai-Ingo Voigt

Kai-Ingo Voigt, Professor Dr., holds the Chair of Industrial Management at the School of Business, Economics and Society at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany). He is visiting professor at Tongji University Shanghai (China), Universidad de Alcalá (Spain), Babson College (U.S.A.), Sofia University (Bulgaria) and University of International Business and Economics, Beijing (China). His research interests include industrial value creation, especially in the context of Industry 4.0, business model innovation, and technology and innovation management.

Appendix.

Overview data sample, methods and main empirical results

Table A1. List of reviewed literature (n = 36).

		Social Capital
Articles	Method	Cognitive Capital	Structural Capital	Relational Capital
Ben-Daya, Hassini, and Bahroun (2019)	Literature Review	x		x
Benitez, Ayala, and Frank (2020)	Mixed Method	x		x
Bienhaus and Haddud (2018)	Quantitative-empirical		x	x
Büyüközkan and Göçer (2018)	Literature Review	x	x	x
Calatayud, Mangan, and Christopher (2018)	Literature Review	x	x	x
Chen (2019)	Qualitative-empirical	x		x
Da Silva, Kovaleski, and Pagani (2019)	Literature Review	x	x	x
De Vass, Shee, and Miah (2018)	Quantitative-empirical
Dolgui, Ivanov, and Sokolov (2020a)	Conceptual/Modelling and Simulation	x		x
Dolgui et al. (2020b)	Conceptual/Modelling and Simulation	x	x
Falkenreck and Wagner (2017)	Mixed Method	x		x
Frederico et al. (2020)	Literature Review	x	x	x
Ghadimi et al. (2019)	Conceptual/Modelling and Simulation		x
Gottge, Menzel, and Forslund (2020)	Qualitative-empirical	x	x
Haddud et al. (2017)	Quantitative-empirical	x		x
Hahn (2020)	Quantitative-empirical		x	x
Handfield, Jeong, and Choi (2019)	Qualitative-empirical		x
Hofmann and Rüsch (2017)	Theoretical/Conceptual		x	x
Ivanov, Dolgui, and Sokolov (2018)	Literature Review	x	x
Leminen et al. (2018)	Mixed Method		x	x
Menon, Kärkkäinen, and Wuest (2020)	Qualitative-empirical	x	x	x
Müller, Veile, and Voigt (2020)	Quantitative-empirical		x	x
Osmonbekov and Johnston (2018)	Theoretical/Conceptual	x	x
Papert and Pflaum (2017)	Qualitative-empirical	x		x
Ralston and Blackhurst (2020)	Qualitative-empirical	x	x
Schmidt et al. (2020)	Qualitative-empirical	x		x
Schniederjans, Curado, and Khalajhedayati (2020)	Literature Review	x	x
Stank et al. (2019)	Theoretical/Conceptual	x	x	x
Strandhagen et al. (2017)	Theoretical/Conceptual	x		x
Strange and Zucchella (2017)	Theoretical/Conceptual	x	x
Tortorella et al. (2021)	Quantitative-empirical			x
Veile et al. (2020)	Qualitative-empirical		x	x
Winkelhaus and Grosse (2020)	Literature Review		x
Yan et al. (2018)	Theoretical/Conceptual			x
Zelbst et al. (2020)	Quantitative-empirical	x	x
Zhang, Zhao, and Qian (2017)	Conceptual/Modelling and Simulation	x		x

Table A2. Main results of empirical papers reviewed.

Articles	Main empirical results
Benitez, Ayala, and Frank (2020)	Innovation ecosystems are described as an alternative development to ‘traditional’ supply chain in the context of Industry 4.0 where technology providers gain new roles in digital transformation of entire ecosystems
Bienhaus and Haddud (2018)	Repetitive tasks will be replaced, and interaction will become more focused on strategic decisions and activities, leading to a more strategic role of procurement in organisations, supporting the creation of new business models, products, and services
Chen (2019)	SMEs’ inclusion in global value chains in the context of Industry 4.0 leading to higher efficiency, better bargaining skills and market access, leading to new value offers
De Vass, Shee, and Miah (2018)	The IoT as a base technology for Industry 4.0 leads to several potentials in SC integration and SC collaboration for all participating organisations
Falkenreck and Wagner (2017)	Trust in manufacturer data safety and transfer is a key enabler for data exchange between buyers and suppliers. Significant differences between regions, e.g. for China
Gottge, Menzel, and Forslund (2020)	Collaborative platforms and new roles for purchasing enabled by new forms of automation in SC processes change, e.g. negotiation processes or collaboration in SCs
Haddud et al. (2017)	IoT integration into SCs and large-scale data sharing improves partnerships and SC integration, enabling real-time adaptability, cross-company coordination and capacity sharing, breaking up linear SCs to a multi-tier of even platform nature of collaboration
Hahn (2020)	Industry 4.0 allows to develop procurement and SC activities into innovation and strategic tasks. Focus of existing organisations on higher productivity while neglecting potentials for humans and new, platform-based forms of value creation
Handfield, Jeong, and Choi (2019)	New forms of cross-company collaboration in SCs by for instance intertwining smart contracts and AI that enables automatising initiation and negotiation activities
Leminen et al. (2018)	Collaboration and platforms among four types of new forms of SC-based business models enabled by Industry 4.0 technologies. Roles of ecosystems for new forms of value creation
Menon, Kärkkäinen, and Wuest (2020)	Determines the level of openness of IoT-based platforms and implications of SC collaboration between platform providers and platform users
Müller, Veile, and Voigt (2020)	All three dimensions of Social Capital are relevant to drive SC collaboration as well as data and information exchange, but different importance of Social Capital dimensions regarding regions worldwide and the type of data shared
Papert and Pflaum (2017)	Develops and differentiates different forms of cooperation for SC collaboration leading to IoT-based service offers in the SC and platform-based business models
Ralston and Blackhurst (2020)	New roles for humans and new forms of BSRs through Industry 4.0 technologies, leading to a more strategic form of personal BSR interactions
Schmidt et al. (2020)	Industry 4.0 influences SC design towards more centralised SC ecosystems as well as developing more decentralised forms of SC collaboration. Both lead to new forms of SC collaboration, BSRs and potentials for new forms of business models
Tortorella et al. (2021)	New forms of SC collaboration lead to smart SCs with increased SC transparency that enable SC resilience
Veile et al. (2020)	Industry 4.0 drives BSRs developments, such as closer collaboration with specific SC partners. At the same time, looser relationships could develop into consolidation of the supplier base based on automation of personal contact and human’s roles
Zelbst et al. (2020)	RFID, IoT and blockchain integration into SCs leads to process automation that prevents information barriers and increases transparency and therefore trust