Introduction

There has recently been an increase in cooperate and academic interest in sustainability due to an increase in socio-ecological changes (Khan et al. 2021a, b, c), including global warming (Menon and Ravi 2021), growing environmental regulations (Mangla et al. 2020), rapidly exhausting resources (Luthra and Mangla 2018), increasing carbon emissions (Yousefi and Tosarkani, 2022), customer awareness about sustainability (Kouhizadeh et al. 2021; Paul et al. 2021; Kshetri 2021), carbon tax (Manupati et al. 2020), various disease caused by pollution (Khan et al. 2021a, b, c), climate change (Ahuja et al. 2019), corporate social responsibility (Ahi and Searcy 2013), United Nations sustainable development goals (Park and Li 2021; Yousefi and Tosarkani, 2022), and net zero-emission targets. United Nations Brundtland Commission’s definition of sustainability “making use of available resources to meet the need of the present population without compromising with the future generation’s needs” is the best way available in the literature to define sustainability.

Over the years, supply chains have played a prominent role in achieving a globally sustainable economy (Park and Li 2021). Supply chain management (SCM) is defined as managing a network of interconnected processes involved in providing the required product/service to the final customer (Wittstruck and Teuteberg 2012). By integrating ecological, economical, and social dimensions into this conventional definition of SCM, we get the notion of sustainable supply chain management (SSCM). SSCM is the capability to reduce continuing risks related to depleting resources, waste management, energy shortage, global warming, and pollution (Wittstruck and Teuteberg 2012). Supply chain sustainability adopts an integrated approach from an environmental, social, and economic perspective, to enable reliable management of inbound and outward operations as well as logistics within a supply chain (Njualem 2022). Companies that incorporate social and environmental concerns into their business strategy now prioritize sustainability as a key issue (Ghahremani-Nahr et al. 2022). Because of the market and ecological uncertainties, it is challenging to achieve SSC (Ghosh et al. 2020).

Blockchain technology (BCT) has been deployed by various firms recently to sustain their supply chains (Rejeb et al. 2021). BCT has been acknowledged as a digital technology that can give supply chains a sustainable component (Trollman et al. 2022). In the supply chain, various members are interacting with each other with their own pool of information, but proper communication between them is lacking. The reason mentioned for this lack of commination by Dujak and Sajter (2019) is a lack of trust in exchanging information. Clauson et al., (2018) suggested blockchain as a way to transfer information securely while maintaining data privacy. Scholars and professionals are intrigued by BCT as a potential remedy for social, environmental, and economic sustainability problems (Zhu et al. 2022).

Blockchain is a digital, decentralized network that operates in a shared and coordinated atmosphere where users validate the information (Wamba et al. 2020), which is contained inside the blocks (Tandon et al. 2021). Special software systems are used to manage these blocks, allowing data to be transmitted, processed, stored, and retrieved by humans (Niknejad et al. 2021). Every block is connected to the blocks that come before and after it through hash (Sharma et al. 2021). A hash can be compared to a digital signature that secures data within the blockchain (Wang et al. 2019). These blocks become immutable once they are linked together in a chain because only the majority of actors can add or remove them through a consensus mechanism. Blocks of the network continue to expand as more exchanges (records and data) are introduced (Kamble et al. 2020a). Accessibility, customer happiness, data management, safety, decentralization, and documentation are identified as essential success elements by (Sunmola 2021) for blockchain in SSC.

Since the inception of BCT, the promise of SSC has been raised; however, the promise has not yet been realized despite its successful and widespread adoption (Zhu et al. 2022). There are few papers in the literature on BCT from an SSCM perspective. For example, Esmaeilian et al., (2020) provided a series of recommendations on how technology may contribute to a sustainable future. Yadav and Singh (2020) justified the implementation of BCT in the supply chain over the conventional method by applying a fuzzy-analytic network process. Authors have proposed BCT as a solution to global price war and competition and as a way to reduce transaction cost and time. Saurabh and Dey (2021) identified drivers of BCT implementation in the grape wine supply chain and explored the relationship between them by employing conjoint analysis. Mangla et al., (2021) mapped the milk supply chain in Turkey to investigate information flow between diverse stakeholders for enhanced traceability and investigated the societal impact of BCT in the milk supply chain to shape social sustainability. Kouhizadeh et al., (2021) employed the technology-organization-environment model to find the obstacles to BCT adoption for SSC and then used the decision-making trial and evaluation laboratory (DEMATEL) to examine the results. Sahebi et al. (2022) recognized and implemented the relationship between BCT’s enablers in renewable energy supply chains by using fuzzy interpretive structural modeling (FISM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) methodology. Trollman et al., (2022) described how BCT could help the coffee supply chain’s ecological embedding. Sahoo et al., (2022) conducted a comprehensive summary of the best blockchain research for the SSCM. It has been found that previous studies only examined a specific category of BCT-based applications domain in SSC. However, there are not many studies that map out BCT applications in SSC, which spurs researchers to conduct research outlining the uses and difficulties of BCT implementation in SSCM.

Previous study offers unique insights into this subject, but thorough bibliometric, citation, and co-citation analysis of this literature can reveal additional, previously unrecognized insights. Therefore, there is a clear need to close this gap. As a result, the following research goals are what motivate this study.

  1. (1)

    To identify the most recent research trends on the relationships between SSC and BCT.

  2. (2)

    To provide bibliometric and network analysis results of BCT applications in SSC.

  3. (3)

    To make suggestions about the theoretical, managerial, and practical implications of using BCT technologies for SSC.

  4. (4)

    What are the potential future directions for this field of study?

Following the introduction, the organization of the paper is as: research methodology and data statistics in “Research methodology”, bibliometric analysis findings in “Bibliometric analysis findings,” network analysis findings in “Discussions and recommendations,” discussion on the results and recommendations in “Conceptual framework and implications of the study,” and lastly entire work is concluded in Sect. 6 with the limitations of the present study.

Research methodology

The next subsections present data collection and the descriptive results derived based on the statistical analysis of the chosen literature. This study’s research methodology entails screening, organizing, and ultimately drafting outcomes. The process flowchart for research is displayed in Fig. 1.

Fig. 1
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Process flowchart for research

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The first step in the bibliographic analysis is choosing appropriate keywords, then data is collected from the Web of Science with the help of chosen keywords. Then bibliometric analysis was done to identify influential individuals, journals, and organizations in this field and then a network analysis was performed to identify influential co-author, and keywords, and for page rank, and cluster analysis. In addition, it can be explained in the following three subsections.

Keywords selection and data collection

The data was gathered from the Web of Science, which enables the exportation of articles’ metadata, including references, abstracts, and journal information, as well as the ability to locate articles using search keywords. Bibliometric studies frequently use the Web of Science as their data source (Ante et al. 2021). The selection of suitable keywords plays a significant role in the bibliometric analysis. Various keywords used for this analysis are “Blockchain Technology (BCT),” “Sustainable Supply Chain (SSC),” “Supply Chain Sustainability (SCS),” “Distributed Ledger Technique (DLT),” and “Supply Chain (SC).” Selected keywords link supply chain operations and strategic processes to BCT. Selected keywords were combined as (a) BCT AND SSC (b) BCT AND SCS (c) DLT AND SC and searched on the web of science database.

This collection includes publications published between 2018 and 2022. Articles obtained from the selected keywords are refined by using the following inclusion–exclusion criterion:

  1. i.

    Articles published as books, meetings, and editorial materials are excluded because they do not generally have a rigorous review procedure.

  2. ii.

    Only articles published in the research area of Business Economics, Environmental Sciences Ecology, Engineering, and Food Science Technology are included.

  3. iii.

    Articles published only in the English language are included.

  4. iv.

    Repeated publications, publications with missing information, and publications that off topic are eliminated.

The number of initially searched articles and searched articles after refinement are listed in Table 1.

Table 1 Search results initially and after refinement
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After reading the title and abstract 297 articles were shortlisted for bibliometric analysis.

Descriptive data evaluation

This study used two analytical techniques bibliometric and network analyses to look at the development and organization of the research field.

Bibliometric analysis

Bibliometric analysis is a statistical method for analyzing published articles (Moosavi et al. 2021) and gaining a complete grasp of a research area and identifying prominent scholars and fresh areas for future research. A bibliometric analysis aids in the analysis of statistics for publications published in a specific field (Agrawal et al. 2022a, b). A bibliometric technique, according to scholars, is a cross-disciplinary way for effectively mapping the directions and issues addressed during the growth of a field of study (Tandon et al. 2021). Bibliometric reviews, in contrast to conventional reviews, are a systematic analytical technique that aids scholars in identifying the most significant writers, their associations, the keywords they chose, and the connections among their works. This kind of analysis employs a systematic analysis technique that aids in the identification of the most well-known academics, the most popular keywords, affiliations, and related academic publications (Naz et al. 2022). The bibliometric analysis was carried out using the VOS viewer 1.6.16.0 software. In order to statistically chart the bibliometric data of research papers in BCT within the SSC context, the various bibliometric parameters, including country-wise contribution, journal-wise contribution, and institution-wise contribution, were presented in this study.

Network analysis

The network analysis was carried out using the VOS viewer 1.6.16.0 software. VOS viewer can display a map in a variety of ways by emphasizing its unique qualities (Agrawal et al. 2022b). The network analysis was carried out to examine the network and research partnerships. In keeping with the studies by (Yadav et al. 2022), a network analysis was also conducted in this study as an additional qualitative layer to offer more in-depth explanations for the investigation’s quantitative findings. Our network analysis is divided into three sections: cluster-based keyword statistics, cluster-based co-citation analysis of related writers, and cluster-based citation analysis in this area.

Bibliometric analysis findings

The main goal of this research work was accomplished by using a bibliometric analysis to evaluate the composition of the body of existing knowledge about BCT in SSC. Using bibliometric analysis, a researcher can conduct a systematic, comprehensive, and reliable literature review (Naz et al. 2022). In this section, the bibliometric analysis results are discussed.

Country-wise contribution

Countries worldwide have realized in recent years that their economic development and growth depend on research-based initiatives. Participation in research is one of the most important measures of development of any country on the planet. Research is believed as the cornerstone of a country’s development. Furthermore, technology-based applications are becoming more prevalent, compelling countries to participate in this field.

Figure 2 depicts country contributions based on clusters, whereas the size of a node denotes the number of publications produced by a country. A minimum of 5 documents are considered for a nation. Only 26 of the 63 nations fit the criteria.

Fig. 2
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Country contributions based on clusters

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Table 2 shows the top ten countries with the greatest number of publications.

Table 2 Contribution of the top ten countries with respect to number of publications
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Table 2 and Table 3 show the top 10 countries in the world with respect to number of publications and the number of citations respectively. From Table 2, it can be depicted that China has topped in terms of the number of publications, with a total of 66 publications, followed by the US (49) publications. India is in third position with 48 publications, which shows that there has been an increase in research in the field of technology in India. Following India in the rankings are the following countries: England, Italy, Canada, France, Australia, Germany, and Pakistan. Figure 3 also displays the top 10 nations by contribution with respect to number of publications.

Table 3 Contribution of the top ten countries with respect to number of citations
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Fig. 3
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Contribution of the top ten countries with respect to number of publications

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Figure 4 displays a world map for country-wise contributions with respect to number of publications.

Fig. 4
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World map for country-wise contributions with respect to number of publications

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Table 3 shows the top ten countries with the greatest number of citations.

From Table 3, it can be depicted that the USA has topped in terms of numbers of citations, with a total of 2261 citations, followed by China (1398) citations, followed by England (1121) citations. India is ranked number 4 with a total of 1121 citations. Other countries following India in the ranking are Italy, Finland, France, Canada, South Korea, and Spain. The total link strength (TLS) measures a country’s connection to other countries around the world. Figure 5 also displays the top 10 nations by contribution with respect to number of citations.

Fig. 5
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Contribution of the top ten countries with respect to number of citations

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Figure 6 displays a world map for country-wise contributions with respect to number of citations.

Fig. 6
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World map for country-wise contributions with respect to number of citations

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Journal-wise contribution

Journal clusters in the domain as shown in Fig. 7.

Fig. 7
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Journal clusters

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Journal-wise contribution is given by the record of the top 20 journals, which has published the articles in the domain as shown in Table 4.

Table 4 Journal-wise contribution
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Figure 8 also graphically displays the journal-wise contribution.

Fig. 8
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Journal-wise contribution

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Institution-wise contribution

Researchers and academicians are increasingly motivated by statistics that are based on affiliation or institutional acknowledgment of a certain topic. In Fig. 9, institution-based clusters are displayed.

Fig. 9
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Institution-based clusters

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Table 5, as shown below, represents the top ten institutions that have contributed most in the field of BCT-based SSC.

Table 5 Top ten institutions contribution
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Based on the Table 5, it may be observed that Worcester Polytechnic Institute of USA has contributed most (14 documents and 1394 citations) which are followed by other reputed institutions like National Institute of Industrial Engineering, California State University, Bakersfield and Hong Kong Polytechnic University.

Network analysis

The network analysis was carried out using the VOS viewer 1.6.16.0 software. VOS viewer can display a map in a variety of ways by emphasizing its unique qualities (Agrawal et al. 2022a). The network analysis was carried out to examine the network and research partnerships.

Keyword statistics

The keyword statistics were developed to examine the most popular keywords in the headings of articles and the keyword section to comprehend the key conceptual trend established by current research (Agrawal et al. 2022a, b). The cluster-based network of keywords developed using VOS viewer is shown in Fig. 10.

Fig. 10
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Cluster-based network of keywords

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Table 6 displays the cluster-based network of keywords based on the VOS viewer.

Table 6 Cluster-based keywords
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Based on the Table 6, it may be observed that cluster 1 has most occurrence keywords followed by other three clusters. Furthermore, the trending keywords are BCT, sustainability, supply chain, and technology.

Co-author analysis

Co-author cluster has been shown in Fig. 11.

Fig. 11
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Co-author cluster

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The co-author analysis identifies six significant collaborative clusters and quantifies the number of collaborative publications between researchers, all of which contribute to the field’s knowledge advancement. Total link strength assesses the strength of a researcher’s or article’s existing ties to other researchers and papers (Tandon et al. 2021). For co-author analysis, selected authors must have at least 3 documents. Out of 942 authors, 32 have met the requirement.

Page rank analysis

Prestige of an article is not indicated by its number of citations but by the number of citations by other highly cited articles. Page rank analysis reveals the level of prestige an article enjoys, whereas citations look at how closely related different articles (or network nodes) are to one another (Tandon et al. 2021). The higher a paper’s page rank value, the more frequently it is cited by important papers. Page rank analysis is a method for determining how frequently someone visits a certain web page (Yadav et al. 2022). The initial aim of the page rank was to describe connections between webpages that can be used to investigate interactions among networks, such as a network of publications in a citation study (Muessigmann et al. 2020). The page rank analysis of an article A, if article A is cited as T1, T2, …. Tn by other authors in their articles is calculated by using Eq. 1.

$$\mathrm{PR}\left(A\right)=\frac{1-d}{N}+d (\frac{\mathrm{PR}\left({T}_{1}\right)}{C\left({T}_{1}\right)}+\dots + \frac{\mathrm{PR}\left({T}_{n}\right)}{C\left({T}_{n}\right)} )$$
(1)

where d = damping factor (generally ranging from 0 and 1)

C (Ti) = number of times Ti has referenced other articles.

PR (Ti) = page rank of article Ti.

It is essential to mention that citations to other frequently cited publications have an impact on page rank. By their very nature, later publications cannot reference earlier ones. Page rank will probably deliver a better image of the prestige of articles in the future as the field expands in terms of publications and productivity. Note that the tools “GEPHI” and “VOS viewer” were used to compute PRA in this article. Table 7 lists the top 10 writers’ page rankings after being calculated.

Table 7 Top 10 authors’ page rankings
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Analysis of clusters

In a bibliometric investigation, cluster analysis is crucial for examining the network of researchers, articles, and co-citations (Agrawal et al. 2022a). Data clustering seeks to group articles from the same academic field together into a single cluster while dividing articles from different fields into separate clusters (Yadav et al. 2022). The top ten clusters have been taken into consideration in the current analysis. Cluster of dominating articles is illustrated by Fig. 12.

Fig. 12
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Cluster of dominating articles

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The primary articles under each cluster are shown in Table 8, along with their link strengths and total citations.

Table 8 Cluster-based article’s citation and key findings
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Discussions and recommendations

Here, the study summarizes the preliminary conclusions from the done investigation into a number of broad and detailed research propositions. In the past 10 years, the subject of supply chain sustainability has gained tremendous significance, garnering the attention of industry, academics, and society (Kouhizadeh et al. 2021). A conventional supply chain system does not, however, offer the capability of real-time tracking of the product. Consequently, using BCT will enable digital supply chains. Major characteristics of BCT, which makes it a suitable tool for SSC are transparency (Kusi-Sarpong et al. 2022), traceability (Mangla et al. 2021), immutability (Ghode et al. 2020), audibility (Yadav and Singh 2020), disintermediation (Kamble et al. 2020a, b), and reliability (Sahebi et al. 2022). This paper’s bibliometric mapping summarizes the pertinent data gathered from a thorough literature research in order to boost the acceptance of BCT in various supply chain operations. Cluster analysis has been done to propose the specific propositions (Ps) of various areas that various clusters have contributed to.

Cluster 1: BCT and green supply chains

Cluster 1–based documents support the contribution of BCT to the green supply chains. BCT’s features such as smart contracts with partners and transparent information sharing boost supply chain visibility dramatically (Khan et al. 2021c). In cluster 1, the top article was authored by Cole et al. (2019) with total citations of 151. The authors explain the use of BCT in supply chain management and operations. The authors also demonstrate the operation of a blockchain from the perspective of the supply chain. Kouhizadeh and Sarkis (2018) have a total citation of 141. They give information on how BCT might be used to support supply chains that use green approaches. Bai and Sarkis (2020) have the total citation of 113 and authors designed a blockchain supply chain transparency evaluation methodology to reduce sustainability risks and boost global supply chain competitiveness. The study by Tijan et al. (2019) has total citation of 80 and investigates the use of BCT in logistics procedures, its effects on supply chain transparency, and the reasons it is crucial to integrate BCT into every step of the supply chain. The article by Manupati et al. (2020) has a total citation of 75 and create a blockchain-based distributed database solution for tracking supply chain performance and synchronizing the optimization of emission levels and operating costs. Kim and Shin (2019) have the total citation of 42 and examines how the application of BCT in supply chain activities may affect (raise or decrease) the effectiveness and expansion of supply chain partnerships, thus affecting supply chain performance outcomes. Khan et al. (2021a, b, c) has total citation of 38 and investigate how BCT is used in circular economy practices and how that affects sustainable and environment performance, which affects organizational effectiveness.

Based on the concise summaries of the articles that are offered in the preceding paragraphs, we suggest the following proposition (P1) for further investigation into this determined theme:

  • Proposition 1 (P1): BCT enables the precise tracking of a product’s carbon footprint, aiding the government in determining the amount of carbon tax to be levied against each business and helps businesses to comply with environmental standards. Smart contracts initiate payments for returned goods automatically based on their condition.

Cluster 2: blockchain-based life cycle assessment

Cluster 2–based documents focuses on the development of the theoretical model of a blockchain-based life cycle assessment (LCA) method for controlling supply chains’ environmental sustainability. LCA is the measure of a good’s environmental effects throughout the course of its whole lifecycle, which includes the mining of raw materials, manufacturing, transportation, use and consumption of the goods, and eventually good’s disposal (Farooque et al. (2020). Traditional LCA techniques, however, have a number of limitations (Farooque et al. (2020). Because of its unique technical property of data security, the newly emerging BCT presents a perfect alternative to address the data reliability challenges for conducting LCA (Zhang et al. 2020). Kouhizadeh et al. (2020) in cluster 2, with highest citations of 99, investigates how the adoption of the circular economy is going to change and advance thanks to BCT. Esmaeilian et al. (2020) with total citations of 88 give a brief review of Industry 4.0 and BCT to help supply chains move toward sustainability. Yadav and Singh (2020), with total citations of 85, determined factors that would justify BCT-based SSC vs. the conventional approach and created a cause–effect relationship between the major factors to find the causes. Zhang et al. (2020), with total citations of 71, suggest a blockchain-based LCA model in order to include BCT into several LCA phases and improve the operation’s effectiveness and efficiency. Farooque et al. (2020), with total citations of 39, enumerate the main obstacles to using BCT for LCA.

As a result, in order to conduct further research on this subject, we suggest the following propositions (P2):

  • Proposition 2 (P2): BCT can reduce data uncertainty, enhance data authenticity, dependability, and transparency, and improve data inputs and outcomes for LCA tools.

Cluster 3: BCT and social sustainability

Cluster 3 explores the potential of BCT to reinforce social responsibility in the supply chain by preventing child labor, and corruption through its traceability and transparency features, protecting human rights. BCT offers enormous prospects for achieving responsible economic growth while protecting the environment and upholding social responsibility. Various contribution of BCT in promoting social sustainability found in literature are fraud prevention (Upadhyay et al. 2021; Kshetri 2021), assurance of labor and human rights (Venkatesh et al. 2020), ensuring the quality of the product (Saurabh and Dey 2021), to support sustainability certificates ( Kouhizadeh et al. 2021; Paul et al. 2021;Kshetri 2021), to provide safe and good quality milk (Mangla et al. 2021), to fight against corruption (Kshetri 2021), (Khanfar et al. 2021), to monitor the origin of raw materials (Park and Li 2021), to trace the product waste (Park and Li 2021), to certify the authenticity of the product (Yousefi and Tosarkani, 2022). In cluster 3, Saberi et al. (2019), with highest citations of 648, critically reviewed the use of smart contracts and BCT in supply chain management. This study also summarizes the barriers to implementation of BCT for businesses. The study by Venkatesh et al. (2020), with total citations of 86, create a system architecture that incorporates Internet of Things, big data analytics, and BCT for supply chain traceability and social sustainability. Upadhyay et al. (2021), with total citations of 46, examine BCT’s present and potential effects on the circular economy from a sustainability and social responsibility perspective. The article by Ivanov et al. (2019a, b) has a total citation of 45, outlines a framework for supply chain risk analytics and describes the idea of digital supply chain twins. A digital twin is created by the integration of modeling, optimization, and database management. Astarita et al. (2019) give a survey of the available research on the use of BCT-based solutions in the transportation sector through bibliometric review.

Fraud and counterfeiting might be overcome with a BCT-enabled system that makes traceability visible at the unit level (Rogerson and Parry 2020). Thus, we may attain sustainability and social responsibility goals through cooperation and data exchange in the BCT (Upadhyay et al. 2021).

Therefore, we suggest proposition (P3) for more study on the highlighted theme:

  • Proposition 3 (P3): BCT possess significant potential to enhance the social sustainability of the supply chain like guaranteeing product quality, supports sustainability certificates, protects human rights, prevents frauds, child labor, and corruption.

Cluster 4: BCT and supply chain visibility

Cluster 4 analyses the relationship between operations management, sustainability concerns, and BCT within the supply chain management and suggested blockchain as a tool to increase supply chain visibility. Supply chain visibility enhances the long-term supply chain performance. The visibility offered by BCT systems facilitates decision-making by allowing stakeholders to view fast, precise, and trustworthy information while minimizing the amount of data sets that lead to decision-making (Rogerson and Parry 2020). In the past, trust was traditionally built through mutual supply chain investments or through the development of long-term relationships with partners. Because trust is already there in BCT systems, due to supply chain visibility, businesses do not need to “trust” their partners to the same extent when using blockchains (Wang et al. 2019). BCT eliminates intermediaries between organizations, and trust is generated via interconnected nodes (Wong et al. 2020). In cluster 4, the study by Wang et al. (2019) has highest citations of 177 and explores how upcoming BCT may change supply chains using the sense making theory by consulting with 14 supply chain professionals. Wong et al. (2020), with total citations of 111, examine into the effects of BCT adoption for supply chain and operations management among Malaysian small-medium enterprises. Di Vaio and Varriale (2020), with total citations of 74, analyze the relationship between operations management, sustainability concerns, and BCT within supply chain management. Rogerson and Parry (2020) has a total citation of 42 and explore the implementation of BCT to enhance supply chain transparency and trust. Saurabh and Dey (2021) have total citations of 51 and identify drivers of BCT implementation in grape wine supply chain and explored relationship between them by employing conjoint analysis.

For the supply chain to compete better while addressing the issues of product’s damage, demand supply changes, safety, and sustainability, information collection and sharing are hugely beneficial and advantageous (Kamble et al. 2020b). Various benefits of BCT imply that blockchain can offer improved visibility through stronger links with digital supply chains (Rogerson and Parry 2020).

Therefore, we suggest the following proposition (P4) for more study on the highlighted theme:

Cluster 5: BCT and sustainable agriculture supply chain

Cluster 5 focuses on developing the model for the sustainable agriculture supply chain by implementing Industry 4.0, BCT, and machine learning applications. BCT may lower risks and enhance the effectiveness of the agriculture supply chain by removing mediators and ensuring transparency and accountability in the agriculture supply chain (Kamble et al. 2020a). In cluster 5, Kamble et al. (2020b), with highest citations of 168, and carried out a literature review to comprehend the extent to which supply chain objectives for sustainable agriculture, have been met and suggested a conceptual model for those working in the agri-food supply chain. Kamble et al. (2020a), with a total citation of 162, determine and linkages among the facilitators of BCT implementation in agriculture supply chain and their connections by using integrated interpretive structural modeling and decision-making trial and evaluation laboratory approach. The research by Lezoche et al. (2020), with citations of 116, reviews literature on Industry 4.0 techniques and agriculture supply chain to comprehend the future research implications in the agriculture domain. Sharma et al. (2020), with citations of 83, propose a framework on the use of machine learning for creating sustainable agriculture supply chain by reviewing the literature. Kumar et al. (2021), with citations of 35, highlight Industry 4.0 and circular economy adoption obstacles in agriculture supply chain and identified the contextual connections between them in order to rank them in relation to one another.

As a result, in order to conduct further research, we suggest propositions (P5):

  • Proposition 5 (P5): Utilizing BCT allows smart agriculture to achieve crucial farming goals including water conservation, soil preservation, carbon emission reduction, cutting back on storage facilities, and productivity growth by using less resources.

Cluster 6: BCT and supply chain traceability

Cluster 6 presents details on BCT capability for supply chain traceability. BCT is found to increase the sustainability of the supply chain, which operates more efficiently and targets product recalls. The supply chain’s significant effects, such as transparency and accountability, traceability and fraud prevention, security and authentication, and cybersecurity and protection, are recognized by BCT-based traceability. Product’s traceability will be greatly improved because to BCT’s expertise in product originality, traceability, and real-time transactions. This will have a favorable effect on product quality, safety, and sustainability (Kamble et al. 2020a). In cluster 6, Hastig and Sodhi (2020), with highest citations of 122, determine the business needs and the elements essential to an effective deployment to direct operations management study on the adoption of supply chain traceability operations. The study by Antonucci et al. (2019) have total citations of 64 and reviews literature on adoption of BCT in agriculture supply chain by using network analysis. Duan et al. (2020), with citations of 54, apply a literature evaluation based on content analysis to the implementation of BCT in the agriculture supply chain. Kim and Laskowski (2018), with citations of 54, create an ontology‐based BCT for supply chain traceability Ethereum blockchain platform. Gao et al. (2018), with citations of 35, suggest a unique supply chain network based on BCT to create a uniform platform for the many parties and participants involved in the supply chain network to do business and share information.

As a result, in order to conduct further research on this subject, we suggest proposition (P6):

  • Proposition 6 (P6): Blockchain is a powerful tool for reducing food fraud and improving traceability effectiveness, which can save both time and money.

Cluster 7: BCT and inventory tracking

Cluster 7 describes the applications of BCT in the supply chain of manufacturing firms to track inventory, manufacturing parts, and quality of the products. This cluster explores how companies may use BCT to obtain real-time transparency and cost reductions by implementing distributed ledger technology. In cluster 7, the study by Leng et al. (2020) have highest citations of 71 and studies how BCT can be used to overcome possible obstacles to sustainability from the point of the production process and product lifecycle management. Fernández-Caramés et al. (2019), with citations of 62, develop and deploys unmanned aerial vehicles and BCT-based design for Industry 4.0 inventory and traceability purposes. Ko et al. (2018), with citations of 62, explore how companies may use BCT to obtain real-time transparency and cost reductions by implementing distributed ledger technology. Sharma et al. (2018) suggest a distributed architecture that uses BCT for the automotive sector in the smart city. Thus, we create Proposition (P7):

  • Proposition 7 (P7): BCT- integration in supply chain increase consumer confidence in product quality, increase the value of goods and services and reduce transaction and production cost.

Cluster 8: BCT and triple bottom line sustainability

Cluster 8 investigates the role of BCT in promoting triple bottom line sustainability. In cluster 8, the study by Choi (2020) have highest citations of 115 and creates empirical studies to examine how supply chains and technology might change “static service operations” into “bring-service-near-your-home” mobile service operations. Choi and Luo (2019), with citations of 109, develop theoretical frameworks to investigate how issues with data quality impact supply chain management for sustainable fashion and mentions an instance where BCT improved social welfare. Nandi et al. (2021), with total citations of 80, present lessons learned from the COVID-19 outbreak for robust, transparent, and sustainable supply chains. Treiblmaier (2019), with total citations of 38, introduces and explains the BCT and the physical internet, then incorporate them within a broad framework for logistics and supply chain management. We recommend the following proposition (P8) for additional research:

  • Proposition 8 (P8): Businesses are utilizing BCT to handle a range of concerns with the sustainable supply chain, including food contamination, carbon credits, identification of waste, and planned maintenance.

Cluster 9: BCT and supply chain transparency

Cluster 9 describes BCT’s transparency in the agriculture supply chain and recommends a model for BCT implementation in the agriculture supply chain. BCT, one of the most rapidly developing and growing technologies, seeks to completely increase supply chain transparency by allowing simple and secure traceability, backtracking, and information tracing (Antonucci et al. 2019). In cluster 9, the study by Helo and Hao (2019) have highest citations of 118 and discusses immutable distributed ledger technology and its potential applications in operations and supply chains. Feng et al. (2020), with total citations of 111, study the features and functionalities of BCT, identify blockchain-based strategies for resolving issues with food traceability, and describe the advantages and difficulties of adopting BCT-based traceability solutions. The study by Astill et al. (2019) have total citations of 78 and outline enabling technologies offered by the Internet of Things, BCT, and big data analytics which could improve food production transparency. Köhler and Pizzol (2020), with total citations of 38, offer valuable information on how blockchain-based technologies may be deployed in the food supply chain and discuss about their social and environmental aspects.

As a result, in order to conduct further research on this subject, we suggest the following proposition (P9):

  • Proposition 9 (P9): The agriculture supply chain's significant effects, such as transparency and accountability, traceability and fraud prevention, security and authentication, and cybersecurity and protection, are recognized by blockchain-based traceability.

Cluster 10: BCT and circular economy

Cluster 10 support BCT as a tool to help circular economy initiatives through information sharing smart contracts. The adoption of circularity in supply chain management and logistics techniques can make it easier for customers to return products after using them and repurpose goods that have additional value (Agrawal et al. 2022a). In cluster 10, the study by Kouhizadeh et al. (2021) have highest citations of 120 and uses force field concepts and the technology-organization-environment model to examine BCT deployment obstacles, and Biswas and Gupta (2019) with total citations of 51 use the DEMATEL approach to provide a framework for examining obstacles to the implementation and effective use of blockchains across various businesses and services. Kouhizadeh et al. (2019), with total citations of 49, describe the connections between the circular economy, BCT, and product deletion. Through transparency and traceability, BCT may connect supply chains with circular economic sustainability (Upadhyay et al. 2021).

As a result, to conduct further research on this subject, we suggest the following proposition (P10):

  • Proposition 10 (P10): On a blockchain, precise data on recycling initiatives, material reuse, eco-friendly packaging, energy use, and carbon emissions can be made available.

Conceptual framework and implications of the study

By combining the findings of earlier studies, the present work has developed a conceptual framework to guide the operation of the SSC and BCT to aid policymakers and practitioners (Fig. 13).

Fig. 13
figure 13

Conceptual framework for the study

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This framework describes how various features of BCT can help in attaining sustainability in the supply chain. Various features of BCT found in the literature that support SSC are traceability, information sharing, transparency, disintermediation, immutability, smart contracts, and decentralization. These features support societal dimension of SSC by ensuring the quality of product (traceability), supporting sustainability certificate (information sharing), certifies authenticity of the product (transparency), fighting against corruption (disintermediation), ensuring originality of the product (immutability), preventing counterfeit goods (smart contracts), and guarantees an easily accessible information network (decentralization). Economic dimension of SSC is supported by BCT because of these various features by reduction of rework and recall (traceability), reduction of production costs (information sharing), reduce expenses (transparency), reduction in transaction costs (disintermediation), reduction in verification cost (immutability), payment initiation on return goods based on their state (smart contracts), and cutting back on storage facility (decentralization). BCT also support environmental dimension of SSC by calculation of carbon tax (traceability), identification of waste in the supply chain (information sharing), compliance with environmental norms (transparency), enhance waste recycling (disintermediation), trace recycled resource usage (immutability), monitors carbon footprint (smart contracts), and reduce the amount of energy lost over long distances (decentralization).

Theoretical implications

This study offers a series of recommendations on how BCT fits into a sustainable society, which adds to the literature on both sustainable development and BCT. Our study is a trailblazing investigation into the introduction and application of BCT to supply chains to enhance its sustainability. In light of the results, we provide five theoretical implications for improving future research on the use of BCT in SSC. First, our findings help scholars comprehend the extent and existing limitations of this field’s study. As a result, this work gives academicians, supply chain practitioners, and policymakers a fundamental and unbiased framework for understanding the concept of BCT and sustainability as well as current research trends on this topic.

Second, our findings may be used by researchers to draw attention to the unique and less-studied topics to promote the deeper adoption of BCT applications in SSC management. Third, researchers may gain from identifying influential individuals, journals and organizations in this field as prospective partners and guides for promoting the study in this domain. Fourth, the results of the cluster analysis give scholars crucial knowledge about eminent and significant articles that might be viewed as the bedrock of this research topic. Fifth, research propositions suggest important study themes that potential future researchers should tackle.

Practical implications

As this paper has shown, BCT has the ability to change how organizations conduct business and serve as a catalyst for SSC models. These broad assumptions lead the researchers to various managerial and policy implications. Utilizing digitally linked supply chains offers a number of advantages in the midst of intense competition, uncertain markets, time-to-market needs, and difficulties with access to essential technology (Rejeb et al. 2021). A conceptual model for managers in the supply chains might be created by the various types of clusters described in this article. Managers may use this data in their policy decisions to support the deployment of BCT-based supply chains. This study advises managers to take BCT into account in order to increase supply chain social, economic, and environmental sustainability.

Unique contributions

This article’s original contribution is the creation of a research framework for SSCs using BCT (see Fig. 13). This research is among the first to use bibliometric analysis and SSC views to examine the literature on BCT and SSC integration. This study has guided organizations for adopting BCT-based technologies in their supply chain sector. This study identified influential individuals, journals, and organizations in this field by using bibliometric analysis. Influential co-author, and keywords were identified, and page rank and cluster analysis were done using network analysis.

Also, this research classified the entire research of BCT-based supply chains into ten clusters and suggested ten specific propositions. Furthermore, to guide research scholars in this field, this study has suggested 33 future research directions. Overall, the study found that BCT has an enormous potential in the supply chain.

Conclusion and future research directions

The objective of this research was to present an exhaustive map of BCT research in the context of SSC by (i) identifying influential individuals, journals, and organizations in this field; (ii) identifying main keywords and research themes; (iii) presenting potential research propositions by conducting bibliometric and network analysis. The findings revealed ten major BCT research clusters in the SSC context, including BCT and green supply chain, BCT-based LCA, BCT and social sustainability, BCT and supply chain visibility, BCT and sustainable agriculture supply chain, BCT and supply chain traceability, BCT and inventory tracking, BCT and post-pandemic supply chain resilience, BCT and supply chain transparency, and BCT and circular economy. The major conclusions of the current study provide insight into the BCT research agenda and significantly aid in situating BCT practices and activities that are in line with the SSC fundamentals in the future. A real-time guideline to direct future research areas and a tool to assist BCT policy-makers and practitioners in supporting the SSC transition can be built on the presented comprehensive study environment of BCT systems and its salient highlight patterns. In order to promote SSC management, 33 specific directions for the future research agenda of BCT were suggested. A conceptual framework was developed to guide the operation of the SSC and BCT to aid policymakers and practitioners.

Some empirical limitations that this study had may be resolved in further studies. First, this study’s investigation of a single database, Web of Science, constrained the articles’ sectoral reach. Subsequent bibliometric research might take into account the other databases, including Scopus, IEEE, PsycINFO, and Google Scholar. Second, the dataset did not contain any working papers, reports, or books. Third, future study may use different software tools, such as Bib Excel and R-based biblioshiny, to perform a more thorough cluster analysis.

Further from the analysis of the clusters, a few problems that have not gotten much attention in the literature have been identified. Scholars should therefore concentrate on following the following research directions.

Future research directions identified from the cluster analysis have been summarized in Table 9.

Table 9 Future research directions
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