A Review of Supply Chain Digitalization and Emerging Research Paradigms

Abstract

Background: The global supply chain landscape is undergoing a significant transformation with the increasing adoption of digital tools. Despite the potential benefits, many organizations struggle to effectively integrate these technologies due to a lack of systematic understanding and frameworks. At the same time, the academic literature on supply chain digitalization lacks a clear taxonomy and analysis of research paradigms that guide scholarly investigations. Methods: To address these gaps, this paper conducts a comprehensive literature review utilizing an analytic approach, based on abductive reasoning, that establishes an analytical framework to identify, assess, and examine the application of various digital technologies in supply chain management. Results: Based on this analysis, the authors propose new systematic dimensions for digitalization in supply chains, alongside emerging research paradigms in this field. Conclusions: The findings provide valuable insights into the current research landscape, offering a foundation for future investigations. Additionally, practical recommendations are presented for advancing research, education, and management practices, with the goal of promoting innovation and the effective implementation of digital technologies in supply chain management.

1. Introduction

Supply chains are networks of actors that create and deliver value to end customers through global interactions [1]. The term “supply chain management” emerged as a result of the globalization of production in the early 2000s. However, effective supply chain management faces numerous challenges, including the drawbacks of globalization. The complexity of business networks involving independent units across the globe, sometimes with conflicting objectives, along with continuously changing demand and supply, uncertainty, and risks, can lead to inefficiencies in management [2]. For this reason, the concept of the digital supply chain was introduced in recent years. A digital supply chain is a customer-centric, demand-driven, dynamic, integrative, intelligent, visible, predictable, and sustainable network system powered by digital technologies such as blockchain, the Internet of Things, big data, and artificial intelligence. The advantages of a digital supply chain include real-time feedback on customer demand, which can be communicated directly to each node in the network, reducing risk, enhancing responsiveness, simplifying complexity, ensuring sustainability, lowering costs, and preventing conflicts.

Consequently, academics have proposed new strategic, tactical, and operational solutions to digitalize supply chains, and subsequently, there is more and more literature related to this topic. However, while the growing body of research offers valuable insights, organizations still face challenges in effectively implementing digital technologies due to a lack of in-depth understanding of how specific tools align with different supply chain management dimensions, such as objectives, difficulties, and decision-making levels. Meanwhile, the academic literature lacks a clear taxonomy and analysis of research paradigms to guide the scholarly studies. This literature review paper makes three main contributions: an analytical research methodology, the research question addressed, and the comprehensiveness of our analysis. We analyzed one thousand scientific studies using a framework developed to assess supply chain digitalization and to evaluate and identify the research paradigms in this field. Our findings can serve as a valuable resource for academics and practitioners seeking to deepen their understanding of digitalization. Additionally, other sectors, such as education, may use these insights to reformulate university programs in preparation for digitalization as part of the future industrial era.

The structure of the paper is as follows: After a general introduction, Section 2 presents and analyses the previous supply chain digitalization literature reviews. Section 3 outlines the research methodology based on a framework of analysis. Section 4 analyses the selected database based on two analysis frameworks. Section 5 proposes new systematized dimensions for supply chain digitalization and new research paradigms in digitalization. Finally, Section 6 concludes and proposes some research perspectives.

2. Literature Review

To differentiate our paper from recent literature reviews and to contextualize our contributions, this section begins with a general assessment of the existing literature review papers. Among the 83 selected review papers, 42 (51%) are systematic literature reviews. Narrative reviews account for 20% of the total, while combined systematic and bibliometric research makes up 11%, and bibliometric research alone represents 10%. The remaining papers are categorized as comprehensive literature reviews (8%).

We then conducted an in-depth analysis of eight literature review papers of various types. Perano et al. [3] selected 87 digital tools and grouped them into 11 macro-categories. They reviewed 1585 papers from 2019 to mid-2022 and developed a conceptual framework for analysis. The authors categorized all the papers in their database and attempted to determine which digital technology offers the best solution for different areas of the supply chain. However, they failed to clearly define supply chain digitalization, likely due to the lack of an exhaustive analysis of existing literature in this field. Although the authors used a systematic literature review as their research methodology, the paper focuses more on emerging practices than on academic research. Additionally, the paper lacks a methodological classification of approaches to supply chain digitalization.

Yadav et al. [4] systematically analyzed 146 articles published between 2010 and 2020. They focused on one supply chain management objective—quality—while neglecting others, such as cost and time. The paper is limited to a small number of digital technology tools, excluding some, like artificial intelligence and 3D printing.

Rusch et al. [5] followed a similar approach but focused on sustainable product. They employed mixed analysis to examine 146 papers. Both studies identified the contexts, usage frequencies, and trends related to digital tools. However, they analyzed a limited number of papers and had a narrow research scope. Although they used a systematic literature review, they merely categorized different digital tools according to their uses, rather than proposing a new classification.

Garay-Rondero et al. [6] proposed a new digital supply chain model through a systematic review of 267 papers. They identified the emerging elements and technological constructs in the digitalized supply chain. However, their analysis was limited to papers from six databases, which may not cover all relevant data sources, and lacked statistical analysis. Furthermore, the authors focused on conceptual papers to present a conceptual model.

In the bibliometric reviews, there are eight papers. Among them, Zekhnini et al. [7] analyzed 176 papers to identify and evaluate the relationship between digital technologies and supply chain management. They proposed a framework to understand the underlying structure and further research direction of supply chain digitalization. However, as a bibliometric review, they analyzed a limited number of research papers. The framework of the paper fails to provide a comprehensive analysis.

Seyedghorban et al. [8] conducted a bibliometric analysis of 331 articles and 12,709 references published on supply chain digitalization. They explored the anatomy of supply chain digitalization and identified the major research groups. The authors did not present any research methodology or framework for analysis, and only used a software called CiteSpace 5.2 R2.

The remaining papers conducted a narrative literature review. Among them, Beaulieu and Bentahar [9] conducted an umbrella review by analyzing the existing literature review papers. It proposed digitalization initiatives that can help managers to improve hospital supply chains. However, the authors chose a specific methodology that only analyses 15 previous literature review papers, which gives a minor source and a limited number of papers. Furthermore, they chose their research domain in the healthcare supply chain, which narrowed the result.

Büyüközkan and Göçer [10] reviewed the digital supply chain literature in detail from both academic and industrial points of view. They proposed the framework for the digitalization of supply chains, which aims to identify the features, challenges, and success factors for the development. However, the database of this paper contains magazine articles, websites, conference proceedings, reports, books, and non-peer-reviewed articles, and the number of peer-reviewed articles is limited, only about 25% of the database.

Using various literature review methodologies and databases of different scopes, the aforementioned papers examined supply chain digitalization from diverse perspectives. However, they have not yet sufficiently explored a comprehensive research model for supply chain digitalization reviews, nor have they fully analyzed the research logic within the existing literature. The choice of literature review methodology depends on the discipline, the volume of existing publications, and the review’s purpose. As the number of publications increases, narrative and comprehensive literature reviews become less effective. In multidisciplinary research areas with a high volume of literature, a combination of methodologies can be adopted to classify existing approaches and highlight gaps in the literature. Among the 83 papers reviewed, almost none offered a clear direction for future research in digitalization. To address this, our paper employs an analytical methodology, drawing on meta-analysis, systematic review, and scoping review techniques, to analyze the literature and propose new systematic dimensions for supply chain digitalization, as well as emerging research paradigms for future researchers and practitioners.

3. Methodology of Research

In this study, we develop a robust methodology drawing on meta-analysis, systematic review, and scoping review techniques to analyze the digitalization of supply chain management and identify prevailing research paradigms. To build our database, we systematically collected papers using a carefully selected set of keywords. After an initial screening, we excluded papers that do not align with the research scope. In the secondary analysis, we identified patterns and similarities across the remaining papers, leading to the creation of an analytical framework. This framework guides our content and statistical analysis of the selected papers. Based on the results of this analysis, we propose new systematic dimensions for supply chain digitalization and outline emerging research paradigms in the field.

3.1. Database Construction Methodology

To establish our database, we primarily utilized the Scopus search engine, which offers broader coverage compared to Web of Science and better aligns with our research needs. We employed an advanced search strategy with the following keywords: TITLE-ABS-KEY ({supply chain} AND {digital*} OR {blockchain} OR {cloud computing} OR {robotics} OR {automation} OR {3d*} OR {IoT} OR {mobile technology} OR {drones} OR {artificial intelligence} OR {big data} OR {additive manufacturing} OR {sensors} OR {augmented reality}) AND (LIMIT-TO (DOCTYPE, “ar”)).

Additionally, to ensure the inclusion of high-quality studies, we cross-referenced the search results with the Web of Science database and the top-ranked journals listed in the Chartered Association of Business Schools (CABS) Academic Journal Guide, and the FNEGE Journal Quality List (ranking academic journals in France). This process enabled us to select 51 journals from reputable publishers, including Springer Nature, Emerald, Wiley-Blackwell, Elsevier, Taylor & Francis, and others in the field of management science. The initial search yielded over 1200 papers, which were then subjected to a rigorous analytical selection process, resulting in a refined dataset of 897 papers.

3.2. Journal Distribution

To better understand the main contributing journals in the field of supply chain digitalization, we classified the journals based on the number of published papers in each. All selected journals are top-ranked sources listed in the CABS Academic Journal Guide and FNEGE Journal Quality List, ensuring the inclusion of high-quality and reputable publications. Figure 1 presents a Pareto chart with abbreviated journal names, illustrating the distribution of publications. The top three journals with the highest number of papers are the International Journal of Production Research (92 articles, 10% of total publications), Computers & Industrial Engineering (73 articles), and the International Journal of Production Economics (70 articles). These are followed by the Journal of Cleaner Production (47 articles) and other leading journals in the field. This distribution highlights the significant contributions of these top-tier journals to research on supply chain digitalization.

3.3. Annual Scientific Production

In this section, we present the annual scientific production of articles related to supply chain digitalization. Given the limited number of publications before 2008 and their discontinuous nature, we selected 2008 as the starting point for our analysis. Figure 2 illustrates the annual scientific production in our database. As shown in the graph, the number of articles on supply chain digitalization remained relatively stable before 2015, with fewer than 20 papers published each year. However, a steady upward trend emerged after 2015, which accelerated significantly after 2019. The sharp increase in publications in 2020 can likely be attributed to the COVID-19 pandemic, which spurred research on digitalization as a response to global supply chain disruptions. The subsequent surge in 2021 may reflect the time required for high-quality research to be published in reputable journals. While there was a slight decline in 2022, and the apparent decrease in 2023 is due to the fact that our database was updated only up to June 2023. The preliminary data suggest that the number of publications in 2023 has already surpassed the previous year, indicating continued growth in this field.

3.4. Distribution of Published Articles by Country

We also analyzed the distribution of published articles by country, focusing specifically on the country of the first author. Figure 3 illustrates the contributions of the top publishing countries. In total, 56 countries are represented in the database, with 13 countries accounting for 80% of the publications. These countries are China, India, the USA, the UK, France, Germany, Italy, Turkey, Iran, Australia, Brazil, Spain, and Canada. China leads with the highest number of publications, contributing 180 articles (20% of the total). Following China, three countries have publication numbers around 100: India (108 articles), the United States (101 articles), and the United Kingdom (94 articles). Other significant contributors include France (46 articles), Germany (44 articles), and Italy (34 articles). Together, these seven countries account for over 60% of the total publications in the database.

4. Analytical Framework

To develop our analytical framework, all 897 papers were thoroughly reviewed to identify key similarities in their methodologies, and we examined supply chain digitalization from two perspectives. First, we assessed the effectiveness of digital tools in achieving objectives, facilitating decision making, and addressing challenges in supply chain management. Second, we explored the various dimensions of the research through ontology, epistemology, methodology, reasoning approach, and research discipline.

4.1. Framework—Analyzing Digital Tools and Supply Chain Management Dimensions

Our statistical analysis revealed 43 distinct digital tools. A significant portion of the literature focuses on the use of individual digital technologies rather than combinations of tools. The details are as follows: blockchain (Block) (246 articles, 27.4%), big data (BD) (154 articles, 17.2%), artificial intelligence (AI) (82 articles, 9.1%), decision support systems (DSSs) (79 articles, 8.8%), additive manufacturing (AM) (73 articles, 8.1%), Internet of Things (IoT) (57 articles, 6.3%), automation (Auto) (33 articles, 3.7%), cloud computing (Cloud) (29 articles, 3.2%), and radio frequency identification (RFID) (15 articles, 1.7%). These nine categories collectively account for approximately 90% of the total database. Using the Pareto principle, we will focus on these nine digital tools to analyze the impact of digitalization.

4.1.1. Digital Tools and Supply Chain Management Objectives

First, we analyzed the relationship with supply chain management objectives. We categorized the objectives into six key areas: cost, quantity, quality, time, location, and sustainability [11]. By thoroughly reviewing and analyzing all articles, the interrelation matrix is presented in

Table 1. Interrelation matrix of digital tools and supply chain objectives.

		Digital Tools
		Block	BD	AI	DSS	AM	IoT	Auto	Cloud	RFID	Digital Status
Supply Chain Management Objectives	Cost	185	97	62	69	62	46	22	24	13	580
	Quantity	20	16	18	17	13	5	4	3	2	98
	Quality	87	26	13	18	14	15	4	4	4	185
	Time	28	24	28	35	39	20	19	10	3	206
	Location	0	4	4	13	14	2	7	0	1	45
	Sustainable	61	37	16	18	17	17	7	6	2	181
Integrity of Supply Chain Objectives	Uni-objective	57	40	21	23	12	17	6	9	6	191
	Multi-objective	147	72	50	55	52	36	23	16	7	458
	Total-objective	0	0	0	1	1	0	0	0	0	2

, with the last column of the table highlighting the frequency of research focused on these six management objectives.

We can observe that in 45% of the cases, digital tools are employed to reduce supply chain costs, while only 3% of the cases are related to location. This disparity can be attributed to the critical role of cost control in achieving competitive advantage, as emphasized by Christopher (2022) [1]. Digital tools, such as predictive analytics and automation, provide measurable and scalable solutions for cost optimization, making them a focal point for both research and practice. In contrast, location-related studies, though important, often address context-specific challenges or industry, such as regional logistics. Additionally, the availability of data and technological maturity for cost-related applications may further explain the higher focus on this area. Meanwhile, to clarify how digital tools address multiple supply chain management objectives simultaneously, our analysis shows that all digital tools are initially designed with a focus on economic objectives, and none of the tools addresses all six objectives. It is evident that digital tools are more commonly used to achieve multiple objectives (70%) rather than focusing on a single objective (29%).

Regarding individual digital tools, articles related to AM show the highest proportion of research addressing multiple objectives. Additionally, only two articles—one on AM and one on DSSs—examined all six management objectives.

Our analysis indicates that research on multiple objectives is the predominant trend. However, there is a significant research gap, with only 0.3% of studies addressing the use of digital tools to achieve all supply chain management objectives. This gap may be attributed to the limitations of individual digital tools and the necessity to integrate various tools to enhance supply chains by addressing multiple competitive factors.

4.1.2. Digital Tools and Supply Chain Decision-Making Levels

The second part of the analysis involves examining how digital tools are utilized across different levels of supply chain decision-making. We have three levels: (1) operational planning, which pertains to short-term planning typically measured in days; (2) tactical planning, which involves mid-term strategies aimed at achieving the final goal, measured in months; and (3) strategic planning, which encompasses long-term strategies and final goals, measured in years. This analysis is summarized in

Table 2. Interrelation matrix of digital tools and decision-making levels.

		Digital Tools
		Block	BD	AI	DSS	AM	IoT	Auto	Cloud	RFID	Digital Status
Supply Chain Decision-making level	Operational	17	6	7	31	4	14	10	5	4	98
	Tactical	14	9	23	29	7	5	10	3	0	100
	Strategic	220	145	54	40	65	42	20	25	13	624
Integrity of decision-making level	Uni-level	241	150	80	64	70	53	28	26	13	725
	Multi-level	5	4	2	15	3	4	5	3	2	43
	Total-level	0	0	0	0	0	0	0	0	0	0

.

We observe that 76% papers are related to strategic decisions, while operational and tactical levels are addressed with similar frequencies (approximately 12% each). This distribution may be attributed to the capacities and limitations of each tool. Digital tools that have been available for a longer period have seen more progress in adapting to various decision-making levels. For example, DSSs and Auto are utilized at all three levels, while AI tends to focus more on the tactical level, and IoT is predominantly used at the operational level.

Table 2 reveals that most digital tools are utilized at only one decision-making level, with only a few addressing multiple levels and no articles covering all three levels. Among the digital tools, DSSs are unique in their ability to address different levels. This suggests that single-level studies are predominant in the research on decision-making levels, highlighting a significant gap in using digital tools to address multiple levels within supply chain management.

4.1.3. Digital Tools and Supply Chain Management Difficulties

Following the ideas presented in the previous sections, we explored the relationships between supply chain digitalization and the difficulties in supply chain management. We categorized the difficulties into four major types: complexity, conflicting objectives, variation over time, and risk [2], and we obtained

Table 3. Interrelation matrix of digital tools and supply chain management difficulties.

		Digital Tools
		Block	BD	AI	DSS	AM	IoT	Auto	Cloud	RFID	Digital Status
Difficulties in Supply Chain Management	Complexity	29	9	18	27	12	12	9	2	5	123
	Conflicting	4	3	3	5	2	0	1	2	0	20
	Variation	5	13	19	17	13	4	2	5	0	78
	Risk	116	41	24	23	10	20	6	4	3	247
Integrity of Supply Chain Difficulty	Uni-difficulty	140	62	59	61	35	34	18	11	8	428
	Multi-difficulty	10	3	3	5	2	1	0	1	0	25
	Total-difficulty	0	0	0	0	0	0	0	0	0	0

.

Firstly, risk is the most frequently mentioned difficulty (53%), followed by complexity (26%), variation over time (17%), and conflicting objectives (4%). This indicates that current research primarily focuses on using digital tools to address different risks, which can be attributed to the pervasive and multifaceted nature of risks in supply chains. Especially after the global pandemic, more and more papers are focusing on risk studies. Risks, such as disruptions and supplier failures, have significant and immediate impacts on supply chain performance, making them a top priority for both researchers and practitioners. Digital tools, such as predictive analytics and blockchain, offer effective solutions for risk identification, mitigation, and resilience building. In contrast, other difficulties, though important, may require more context-specific cases, which could explain their relatively lower level of study. These findings highlight the need for future research to explore under-represented difficulties, such as complexity and conflicting objectives, to provide a more holistic approach to supply chain digitalization.

Secondly, we analyze how digital tools address various difficulties. Some tools are particularly effective in managing complexity, such as DSSs and RFID. Others are focused on addressing variation over time (AM, Cloud). Among all digital technologies, AI, DSSs, and AM each have a significant number of studies addressing the remaining challenges (excluding conflicting objectives). This suggests that these three technologies offer broader problem-solving capabilities. Our analysis indicates that enterprises can select digital tools based on the specific difficulties they face in their supply chains.

We also analyzed articles that study single versus multiple difficulties. Most studies focus on a single difficulty, with very few addressing multiple difficulties, and none examine all difficulties simultaneously. Our analysis highlights a significant gap in studies that tackle multiple difficulties concurrently. This suggests the potential for further development in this area.

4.1.4. Discussion

In this section, we explored the relationship between digitalization and supply chain management from three perspectives. Through these analyses, we have drawn several conclusions, identified research gaps, and provided valuable insights for both academic research and business management. A key gap identified is the limitation of individual tools in addressing all dimensions of supply chain management. To better understand the foundational aspects and underlying logic of research in supply chain digitalization, we will systematically analyze the research methods in the following sections. This will help uncover existing paradigms and offer improved guidance for future studies and enterprise management practices.

4.2. Framework—Analyzing Research Paradigms

To develop our analytical framework, we thoroughly reviewed all 897 papers to identify key similarities in the methodologies used. After analyzing these methods, we identified five main similarities that reveal the underlying logic of the research (Figure 4):

• Ontology (Objectivism (O), Subjectivism (S), Mixed (M));
• Epistemology (Positivism (PO), Interpretivism (I), Pragmatism (P));
• Methodology (Quantitative (Q), Qualitative (QL), Mixed (M));
• Reasoning approach (Deductive (D), Inductive (IN), Abductive (A), Automated (AU), Counterfactual (C), Evidential (E), Critical (R));
• Research discipline (Computer (C), Management (M), Mathematical (MA), Economic (E)).

According to the analytical framework, each paper in the database was examined using these dimensions. It is important to note that our database is current as of June 2023, and therefore the number of papers published in 2023 is still relatively low. Nevertheless, the overall analysis remains consistent, and the trends observed are still valid. Each of these key similarities is further broken down into several dimensions, as detailed below.

4.2.1. Ontology

Ontology studies assumptions about existence and serves as the basis for research on systems of belief and the interpretation of knowledge and reality. Ontology encompasses the assertions and assumptions made about the essence of the social entity, including what exists, what it is like, what its constituent units are, and how these units interact [12]. Ontology can be categorized into the following: (1) objectivism: it assumes that there is a single, context-free reality that can be discovered through the methods based on predetermined processes to test or simulate models [13]; (2) subjectivism: each paper reflects diverse mental constructions of reality, which are influenced by the subjective experiences and perspectives of the researchers [13]; (3) mixed approach: our research identified instances where mixed ontologies were in use, combining elements from various ontological positions to create a more comprehensive framework.

Among the 897 research papers, objectivism is the most frequently used ontology, appearing in 558 papers (62%). Subjectivism is represented in 258 papers, while mixed ontology accounts for 9% of the literature. Figure 5 illustrates the trends in the use of different ontologies. It is evident that objectivism holds a significant numerical advantage over both subjectivism and mixed ontology. Additionally, in 2022, both objectivism and subjectivism saw a decline, whereas mixed ontology experienced an upward trend.

4.2.2. Epistemology

Behind each proposed method lies a set of approaches to acquiring knowledge, which are classified into various epistemologies. Epistemology is the study of how we acquire knowledge and what we recognize as knowledge [13]. There are different types of epistemologies [14], but for our research, we classified the database into three main categories of epistemologies: (1) positivism: researcher follows the hypothetico-deductive model, starting with theories derived from the literature to formulate hypotheses and conduct simulations or empirical studies. The findings from such studies contribute back to the existing literature [15]; (2) interpretivism: it employs a subjective philosophy, where understanding is socially constructed through elements such as language, consciousness, shared meanings, and tools [16]; (3) pragmatism: it emphasizes the practical application of research, where authors are not bound to a specific method or mixed methods [17].

Among the three epistemological categories, pragmatism is the most commonly used, appearing in 490 papers, which accounts for 55% of the database. Approximately 29% of the literature employs interpretivism, while positivism is represented in 149 papers, making up 17% of the selected literature. Figure 6 below illustrates the trends in these epistemologies over the years, showing that pragmatism has the highest volume and is on an upward trend, whereas interpretivism demonstrates a noticeable decline.

4.2.3. Methodology

Research methodology refers to the systematic framework guiding how a study is conducted to ensure clarity and understanding. It is shaped by the specific ontological and epistemological assumptions underlying the research. Our database classifies studies into three primary strategies of scientific inquiry: (1) quantitative: it involves the use of mathematical measurements and statistical analysis, and researchers can generalize findings from collected data; (2) qualitative: uses the analysis of non-numerical data, and it is typically used when quantitative methods are insufficient to capture the complexity of the subject matter [18]; (3) mixed: it combines both quantitative and qualitative methodologies, and it allows for the triangulation of data, enhancing the validity of findings by drawing on the strengths of both methods.

Regarding methodology, out of 897 research papers, the most prevalent ontological approach is quantitative, represented by 558 papers, accounting for approximately 62% of the total database. Qualitative research follows, with 260 papers, constituting nearly 29% of the studies. The mixed-methods approach represents 9% of the literature analyzed. Figure 7 illustrates these trends. Notably, objectivist approaches, as reflected in the quantitative studies, hold a significant numerical advantage over subjectivist and mixed ontologies. However, in 2022, both quantitative and qualitative methodologies exhibited a declining trend, while the mixed-methods approach showed an upward trajectory.

4.2.4. Reasoning Approach

In addition to the aforementioned similarities, the research processes of the papers were analyzed and classified into seven distinct reasoning approaches: (1) deductive reasoning: this begins with established theories, followed by data collection and analysis, to test these hypotheses; (2) inductive reasoning: this involves drawing general conclusions from subjective observations which involve data collection, gathering facts without preconceived judgments; (3) abductive reasoning: this starts with an incomplete observation and then seeks the most straightforward and most likely conclusion from the observations; (4) automated reasoning: technology is utilized to assist individuals in making decisions across various projects; (5) counterfactual reasoning: this involves considering alternative scenarios to explore different possibilities and outcomes by analyzing how changes in assumptions could affect results; (6) evidential reasoning: this is a general evidence-based approach for multi-criteria decision making to address complex decision problems involving multiple factors under various uncertainties; (7) critical reasoning: this is employed by authors to formulate and evaluate statements to analyze cause-and-effect relationships, and researchers typically use mathematical methods and analytical processes to identify optimal solutions.

Among the seven reasoning approaches, inductive reasoning is the most frequently used, comprising 29% of the entire database. Automated reasoning and deductive reasoning follow, representing approximately 24% and 17%, respectively. Critical, counterfactual, and abductive reasoning each account for 10% of the articles, with a combined total of 30% of the database. Finally, evidential reasoning constitutes about 9% of the selected literature. The high prevalence of inductive reasoning is largely due to its inclusion in all papers employing qualitative methodology.

4.2.5. Research Discipline

Research discipline refers to the specific field of study or academic area where research activities are conducted. We have categorized the literature in our database into four main disciplines: (1) computer science: if it involves digital technology or programming; (2) management science: if it is a problem of management using interviews or surveys; (3) economic science: if the paper addresses critical questions about the production and exchange of goods and services; (4) mathematical science: if the paper uses mathematics.

We present a simple diagram in Figure 8 to illustrate the interdisciplinary connections. We use ‘M’ for management science, ‘C’ for computer science, ‘MA’ for mathematics science, and ‘E’ for economic science. These groups are mutually exclusive, meaning there is no overlap between them. There are 65 papers that belong to the intersection of all four disciplines and are highly interdisciplinary. Then, 261 papers belong to the intersection of management science and computer science but do not belong to mathematics science or economic science. And 571 papers were in the intersection of management science, computer science, and mathematics science.

4.2.6. Discussion

This research highlights prevailing trends and research paradigms in the literature. First, our results indicate that articles on supply chain digitalization predominantly align with objectivism. Second, there is significant diversity in research methodologies, with a strong preference for data and mathematical models in the analysis. Third, the number of papers utilizing mixed research methods is increasing, suggesting that this approach may become more mainstream in the future. This trend could further blur the boundaries between disciplines, promoting more interdisciplinary research in supply chain digitalization.

To provide a more comprehensive guide, based on the detailed methods of each paper, the following section uses the results of the analysis to propose new systematized dimensions and research paradigms in supply chain digitalization.

5. Supply Chain Digitalization

This section synthesizes the findings from previous sections to provide a comprehensive understanding of supply chain digitalization. First, we offered a systematic definition of supply chain digitalization which builds on the analysis of digital tools and supply chain management dimensions presented earlier. Second, we further explored the research paradigms framework, detailing the specific research methods associated with each paradigm and their applications in the field.

5.1. Systematization of Approaches to Supply Chain Digitalization

To analyze the actual supply chain digitalization approaches, we start with defining supply chain management as a set of approaches used to efficiently integrate the actors of supply chains to achieve a multi-stream of competitiveness based on the studied multi-objectives. For the difficulties in supply chain management, digitalization is an answer to streamline achieving multi-objectives while dealing with multiple difficulties. In this case, supply chain digitalization is a set of digital tools used to achieve multiple streams of results while dealing with sources of difficulties in supply chain management. Thus, we revised the supply chain digitalization approaches based on a few adaptable scenarios: supply chain digitalization can provide multi-objectives or total objective and can solve multi-difficulties or total difficulties. However, based on our analysis, none of these approaches have been given enough attention by the authors, the research is now mostly focused on solving a single problem. There is a real gap in the literature and the reality of the actual supply chains.

5.2. Emerging Research Paradigms in Supply Chain Digitalization

This literature analysis underscores the multidisciplinary nature of the field of supply chain digitalization research. Our objective is to identify and evaluate research paradigms in this area and provide guidance for researchers. While research methods are typically chosen based on research questions and availability of data, rather than paradigms, understanding the methods used by previous researchers is essential. Therefore, we present a new framework that includes not only seven research paradigms but also the various methods employed by different authors within each paradigm.

The detailed methods used in all seven paradigms are listed in the Appendix A. In the following seven subsections, we will discuss each of them based on our paradigm analysis framework and provide a more in-depth perspective.

5.2.1. Objectivism–Positivism–Deductive–Quantitative

There are 136 papers within the <Objectivism–Positivism–Deductive–Quantitative> paradigm, accounting for approximately 15.2% of the entire database. It is characterized by an objectivist ontology and a positivist epistemology, where researchers seek to uncover an independent reality through empirical evidence and logical measurement. The main techniques employed are a combination of survey and structural equation modeling [19,20,21,22], or partial least squares-structural equation modeling [23,24,25,26] to test proposed hypotheses. Additionally, some authors use alternative statistical methods, such as confirmatory factor analysis (CFA) [27], correspondence analysis (CA) [28], and exploratory factor analysis (EFA) [29], among others.

All papers under this paradigm are interdisciplinary, spanning management science, computer science, and mathematical science. This demonstrates both a concentration and diversity in the research methods employed, which can guide future researchers conducting similar studies. In terms of practical application, research within this paradigm often lacks direct applicability and cannot be readily translated into practical systems such as operating systems. However, by employing empirically collected data, these studies test hypotheses relevant to practical applications, thereby providing valuable technical support for real-world management operations or system design.

5.2.2. Objectivism–Pragmatism–Automated–Quantitative

There are 202 papers within the <Objectivism–Pragmatism–Automated–Quantitative> paradigm, representing 22.5% of the entire database. Using a quantitative methodology, these papers aim to address existing problems by developing automated technical systems. They are interdisciplinary, spanning management science, computer science, and mathematical science, and utilize mathematical analysis to optimize the performance of the proposed digital systems.

The most frequently employed method in this paradigm involves designing systems with various digital tools to address different supply chain challenges. Some authors focus on the Internet of Things (IoT); for instance, Bhargava et al. [30] proposed an IoT-based supply chain management model to tackle logistics and transportation issues. Salehi-Amiri et al. [31] utilized an IoT system to manage uncertainties in home healthcare supply chain networks. Other studies explore blockchain technology; for example, Miller et al. [32] proposed a blockchain system for supply chain finance management and demonstrated its application through a real-world pilot experiment in the livestock industry. Additionally, Manupati et al. [33] developed a model to predict disruptions in contexts where smart contracts based on blockchain technology have been implemented.

Some authors employ simulation methods in their research. Herding and Mönch [34] used discrete-event simulation to evaluate the performance of different planning functions in a semiconductor supply chain based on cloud computing. Lohmer et al. [35] utilized agent-based simulation to analyze resilience strategies and ripple effects in blockchain-coordinated supply chains. Other papers apply design science research [36,37] and Graph Neural Networks (GNNs) [38] for their research.

Papers within this paradigm demonstrate significant practical value, with some even proposing fully operational systems using various digital tools. Authors of these articles typically require a certain level of software proficiency or programming skills. Research following this paradigm is expected to play a substantial role in the future application of supply chain digitalization.

5.2.3. Objectivism–Pragmatism–Counterfactual–Quantitative

There are 64 papers within the <Objectivism–Pragmatism–Counterfactual–Quantitative> paradigm, representing 5.1% of the total database. These papers are interdisciplinary, spanning management, computer science, economics, and mathematical science. They focus on addressing cost–profit issues in supply chain digitalization and analyze alternative possibilities related to the research assumptions, which characterizes them as employing counterfactual reasoning.

The research primarily employs quantitative mathematical approaches, with game theory models being the main method. These include the Stackelberg game model [39,40,41], the differential game model [42,43], and the Nash game model [44,45], along with other mathematical models [46,47,48]. Research within this paradigm is oriented towards operations research and provides solutions for practical operational management issues. These papers offer high direct applicability, presenting operational outcomes under various scenario assumptions and delivering highly visualized results for enterprises.

5.2.4. Objectivism–Pragmatism–Critical–Quantitative

There are 105 papers within the <Objectivism–Pragmatism–Critical–Quantitative> paradigm, representing 11.7% of the entire database. The objective of research in this paradigm is to understand the logical connections between ideas, employing mathematical processes, which aligns with critical reasoning and quantitative methodology. This research is interdisciplinary, spanning management science, computer science, and mathematical science.

The primary method employed is modeling, with various types utilized, including the fuzzy Bayesian model [49], regression models [50], Markov models [51], and classical facility location models [52]. Additionally, some bibliometric analysis papers use software to examine the interconnections among the literature [53,54]. Other authors employ different programming methods, such as multi-level programming [55], bi-objective mixed-integer programming [56], linear programming [57], and mixed-integer non-linear programming [58].

In this research paradigm, articles typically possess logical, solid reasoning. The application of data and models in the argumentation process enhances the persuasiveness of the paper’s results, providing substantial reference value in academic research and practical management.

5.2.5. Objectivism–Pragmatism–Evidential–Quantitative

There are 51 articles within the <Objectivism–Pragmatism–Evidential–Quantitative> paradigm, accounting for 5.8% of the total. Papers in this paradigm are characterized by their use of multi-criteria decision making (MCDM) methods. This research is interdisciplinary, spanning management science, computer science, and mathematical science.

Various techniques can be applied within the MCDM methods. Some authors use the Best–Worst Method (BWM) [59,60,61]. Others adopt the Analytic Hierarchy Process (AHP) [62,63]. Additional techniques include Decision-Making Trial and Evaluation Laboratory (DEMATEL) [64], Interpretive Structural Modeling (ISM) [65], and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [66]. Some researchers employ a combination of two or more techniques in their studies.

In this research paradigm, articles typically aim to explore the inherent connections among various influencing factors, identify or assess the enablers for setting assumptions, or uncover obstacles affecting them. This approach can also form a theoretical framework to guide practical applications. When investigating specific issues within an enterprise, this method demonstrates strong feasibility, with implementation and calculation processes being manageable. However, the research outcomes of such papers often represent only the initial phase of problem solving: they assist in issue identification, but subsequent work requires further in-depth research and exploration.

5.2.6. Subjectivism–Interpretivism–Inductive–Qualitative

There are 258 papers within the <Subjectivism–Interpretivism–Inductive–Qualitative> paradigm (28.8%). Researchers in this paradigm analyze facts and establish relationships based on subjective observations, proposing solutions that integrate various individual perspectives. Since not all the papers involve mathematical or economic processes, they present interdisciplinary research between management and computer science.

The most commonly used method within this paradigm is the case study or interview [67,68,69]. Additionally, there are around seventy literature review papers. Some aim to design conceptual frameworks [70,71,72,73] to provide guidance or theoretical foundations for practical applications. Other authors employ content analysis [74], patent analysis [75], and other qualitative methods to offer valuable recommendations, principles, and solutions for their research topics. While these studies are grounded in subjectivity, they recognize that reality and truth are shaped by individual experiences and perspectives, offering significant insights and guidance for both researchers and enterprises.

5.2.7. Mixed–Pragmatism–Abductive–Qualitative and Quantitative

There are 81 papers within the <Mixed–Pragmatism–Abductive–Qualitative And Quantitative> paradigm, representing 9% of the database. Their research equally values personal perspectives and independent logical analysis, aligning their epistemology with pragmatism. These studies are interdisciplinary, spanning management, computer science, and mathematical science.

Some researchers combined interviews with multi-criteria decision making (MCDM) in their studies [54,76,77]. Others integrated interviews with system design [78,79,80]. Additionally, some researchers employed Delphi surveys [81,82].

This approach arises from researchers aiming to tackle complex issues without being restricted to specific research methods. As a result, the articles under this paradigm are more persuasive, often blending theory and practice, providing significant reference value for both academic research and enterprise management. Furthermore, these studies typically offer substantial practical application value.

5.2.8. Summary of Supply Chain Digitalization Paradigms

In this section, we proposed a novel framework comprising seven paradigms of supply chain digitalization and provided a detailed discussion of the research methods associated with each paradigm, offering valuable insights for scholars pursuing related research directions. Furthermore, the distribution of these research paradigms is presented in

Table 4. Summary of supply chain digitalization paradigms.

Ontology	Epistemology	Methodology	Reasoning	Percentage
Objectivism	Positivism	Quantitative	Deductive	15.2%
	Pragmatism		Automated	22.5%
			Counterfactual	7.1%
			Critical	11.7%
			Evidential	5.7%
Subjectivism	Interpretivism	Qualitative	Inductive	28.8%
Mixed	Pragmatism	Mixed	Abductive	9%

. As shown in the table, objectivism emerges as the most frequently applied ontology, accounting for 62% of the reviewed articles. Pragmatism dominates as the primary epistemology, appearing in 56% of cases, while quantitative methodologies are employed in 72% of the studies. The most common paradigm is <Subjectivism–Interpretivism–Inductive–Qualitative>, representing 28.8% of the articles. This paradigm is widely used in management science research, although there is a noticeable trend toward adopting alternative methods based on diverse reasoning approaches.

Ontology, epistemology, methodology, and reasoning are fundamental dimensions of our analytical framework, each contributing uniquely to the construction of research paradigms. While certain subcategories, such as Objectivism/Quantitative and Subjectivism/Qualitative, exhibit a one-to-one relationship, these dimensions cannot be combined into a single category. Ontology defines the nature of reality being studied, epistemology addresses how knowledge is acquired, methodology specifies the research methods used, and reasoning determines the logical approach to analysis. Together, these dimensions provide a comprehensive structure for understanding and evaluating research in supply chain digitalization, ensuring that the complexity and diversity of academic inquiry are fully captured.

6. Conclusion, Research Limitations, Future Directions, and Recommendations

The following subsections offer a general conclusion, outline the research limitations, and propose future directions. Additionally, they present a three-dimensional set of recommendations encompassing research, educational, and managerial perspectives, along with suggestions for future studies.

6.1. General Conclusions

This research offers a distinctive and original contribution in three main areas: its methodology, the research question, and the depth of its analysis. By examining nearly 900 papers, we introduced a new definition for supply chain digitalization and proposed an advanced systematization of approaches, focusing on how specific digital tools align with different supply chain management dimensions. Additionally, we developed a new framework consisting of seven paradigms of supply chain digitalization, summarized in Table 4.

Our framework advances existing models by providing a more granular and actionable understanding of digital tools in supply chain management. Unlike previous studies that often focus on isolated tools or generic applications, our framework systematically categorizes tools based on their alignment with specific supply chain dimensions. This approach allows organizations to identify the most suitable tools for their unique challenges and objectives. The superiority of our framework is assessed through its ability to do the following: (1) Integrate multiple dimensions: by linking tools to objectives, difficulties, and decision-making levels, our framework offers a holistic view that addresses the multifaceted nature of supply chain management. (2) Enhance practical applicability: the framework is designed to bridge the gap between theoretical research and practical implementation, providing clear guidance for businesses. (3) Support interdisciplinary research: by categorizing research paradigms and methodologies, our framework facilitates interdisciplinary collaboration and knowledge integration.

To illustrate the applicability of our findings, we highlight a few real-world examples of companies that have successfully implemented digitalization strategies. For instance, Amazon has leveraged advanced analytics and automation to optimize inventory management and reduce delivery times, aligning with our framework’s emphasis on decision-making efficiency. Similarly, Walmart has utilized blockchain technology to enhance supply chain transparency, addressing the dimension of difficulties related to traceability and trust. These examples demonstrate how our framework can guide organizations in selecting and implementing digital tools tailored to their specific needs.

From an academic perspective, our study outlines academic writing paradigms and calls for researchers to explicitly declare their research paradigms. As shown in Table 4, even articles with the same epistemologies may adopt different ontological and methodological approaches, leading to variations in reasoning. Establishing a comprehensive logical framework is essential to ensure the validity and coherence of research arguments, including data acquisition, collection, and validation processes. From a business management perspective, this research provides a detailed analysis of how different digital tools align with three critical dimensions of supply chain management: objectives, difficulties, and decision-making levels. By categorizing digital tools and their applications, we offer practical insights for organizations seeking to implement digital technologies effectively. Our findings highlight the limitations of individual tools in addressing all supply chain dimensions, emphasizing the need for integrated solutions tailored to specific organizational contexts.

In summary, this study not only advances academic understanding of research paradigms in supply chain digitalization but also provides actionable guidance for businesses navigating the complexities of digital transformation. By bridging the gap between theory and practice, our work contributes to both scholarly discourse and real-world supply chain management.

6.2. Research Limitations and Future Directions

While this study provides a comprehensive analysis of how digital tools align with three key dimensions of supply chain management—objectives, difficulties, and decision-making levels—it has certain limitations that should be acknowledged. First, our research primarily focuses on the functional applications of digital tools and does not extensively address the organizational, cultural, and legal challenges associated with digital transformation. These broader challenges, such as resistance to change, cultural barriers, and regulatory compliance, are critical to the successful implementation of digital technologies and represent an important area for future research.

Second, our analysis is based on a systematic review of existing literature, which may be influenced by publication bias or the predominance of certain research themes. For example, the overrepresentation of studies from specific regions or industries may limit the generalizability of our findings. Future studies could expand the scope by incorporating case studies or empirical data from diverse contexts to validate and extend our conclusions.

Finally, while our study identifies gaps in the current research landscape, it does not provide specific solutions or frameworks for addressing these gaps. Future research could focus on developing practical tools or guidelines to help organizations navigate the complexities of digital transformation, particularly in overcoming the challenges highlighted in this study.

6.3. Recommendations

6.3.1. Recommendations for Research

Our analysis highlights several key aspects. First, we recommend adopting a pragmatic approach to research. In the field of supply chain digitalization, there is a strong emphasis on practical applications. Pragmatism involves developing applicable research through a variety of reasoning approaches and methods, necessitating the integration of multiple scientific disciplines.

Our statistical analysis reveals that the proportion of articles employing a mixed ontology and methodology paradigm is currently low and not yet mainstream. However, the previous section indicates a noticeable upward trend. As a multidisciplinary field, supply chain digitalization research demonstrates significant methodological diversity. This research paradigm offers flexibility in selecting research methods and enhances the persuasiveness of the findings. In future research, the number of articles utilizing mixed methods will increase and potentially become the predominant focus of study.

Second, we recommend that researchers enhance their studies with digitalized analysis. Among all quantitative research paradigms, the paradigm utilizing automated reasoning currently occupies the largest share because supply chain digitalization inherently involves computers, automation, and digital systems. Additionally, this trend reflects the growing prominence of this approach within the research domain, suggesting that studies employing automated reasoning are likely to hold significant value for publication.

Third, we recommend aligning research with the challenges faced by companies. Although many companies aspire to achieve supply chain digital transformation, there are relatively few successful implementations. Technological advancements require robust theoretical support to translate into practical success, making theoretical research outcomes essential for guiding and facilitating effective digital transformation.

6.3.2. Educational Recommendations

Based on our statistical analysis, it is evident that articles employing a combination of qualitative and quantitative methods, as well as interdisciplinary approaches, are increasingly prevalent in supply chain digitalization research. In light of this observation, we offer the following two educational recommendations, targeting universities as a key sector for fostering future research and innovation in this field. Universities are uniquely positioned to bridge the gap between academic research and practical application, as they serve as hubs for knowledge creation, interdisciplinary collaboration, and talent development. By aligning educational strategies with the evolving needs of supply chain digitalization, universities can play a pivotal role in advancing both theoretical understanding and practical implementation.

First, from the perspective of strategic decision making, university laboratories should promote communication and collaboration across different disciplines while respecting their diversity. Breaking down barriers and fostering integration among disciplines can be highly beneficial. For instance, in the digitalization of supply chain management, fields such as management science, computer science, and economics are all involved. Establishing strategically integrated interdisciplinary laboratories could attract researchers interested in these areas. Given the high level of knowledge spillover in doctoral communities, this initiative has considerable experimental value from a strategic standpoint. Such laboratories would not only advance academic research but also provide a platform for testing and refining digital tools in a controlled environment, ultimately contributing to their practical application in industry.

Second, from a tactical planning perspective, universities should enhance traditional disciplinary teaching programs by incorporating new courses that align with current business development needs. For example, introducing elective courses in computer science could allow students to explore popular information technologies. This approach would provide students with early insights into enterprise requirements, thereby boosting their competitiveness and personal development potential. By equipping students with interdisciplinary knowledge and practical skills, universities can cultivate a new generation of professionals capable of driving innovation in supply chain digitalization. This alignment between education and industry needs is critical for ensuring that academic research remains relevant and impactful.

6.3.3. Managerial Recommendations

Since the widespread disruptions in global supply chains caused by the COVID-19 pandemic, there has been a surge of interest in academic research on supply chain digitalization, and enterprises have been seeking digital transformation and methods to build more stable and flexible supply chain systems. Through the analysis presented above, we provide the following three managerial recommendations.

First, the current mainstream research trend focuses on improving supply chain performance through individual digital technologies. However, our analysis indicates that a single digital tool alone is insufficient for achieving multiple objectives at different decision-making levels or for addressing various challenges. Integrating multiple digital tools can better achieve these goals, making supply chain management more flexible and efficient. Therefore, companies undergoing digital transformation in their supply chains should consider adopting a reform model that combines various digital tools, leveraging their full potential for long-term benefits.

Second, all reforms come with risks, and digital transformation should be approached with caution. While supply chain digitalization aims to mitigate various risks or respond effectively when they arise, introducing new digital tools can also impact the existing supply chain structure, potentially leading to transformation failures. This is evident from the global pandemic, which highlighted the importance of supply chain digitalization and led many companies to express a desire for reform. However, the number of companies that have genuinely attempted or successfully achieved such reform remains relatively small.

Lastly, enterprises need to cultivate or attract professionals skilled in digital transformation. A company’s robust development relies heavily on talented individuals, including both strategic-minded managers and technically skilled professionals. Both types of expertise are crucial for successful transformation. As various fields increasingly move towards digitalization, companies must enhance their talent pool to excel in this digital era. Digital tools can achieve their full potential and drive efficient results when used effectively by those who understand how to leverage them.