The DOI matches its Crossref record, with a small difference in title or year; 147 of the 155 references it cites were found in a registry or an index; none of the 22 sources its claims cite is checked for support yet.
Big Data and supply chain management: a review and bibliometric analysis
Open accessOverview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper reviews and maps research on Big Data and supply chain management (SCM) from 2006 to 2016 using bibliometric and network analyses of 286 articles. It identifies influential authors, countries, works, and six research clusters, and proposes future directions for Big Data in SCM.
Key findings
- The review evaluated 286 articles published over 10 years (2006–2016), drawn from an initial 7,868 Scopus records and refined to 2,564 relevant documents before focusing on the top 20 journals.
- Wang was the top contributing author with 6 publications, followed by Li and Wang with 5 each; the United States, China, and United Kingdom were the major contributing countries.
- The most cited work was Chiu et al. (2006) with 804 citations, followed by Boyd and Crawford (2012) with 351 citations and McAfee and Brynjolfsson (2012) with 153 citations; PageRank analysis placed Jacobs (2009) at the top, with McAfee and Brynjolfsson (2012) second.
- Co-citation analysis of a 233-node, 1,096-edge network produced six clusters: conceptualisation of Big Data and analytics; Big Data and SCM; Big Data tools and algorithms; Big Data applications in healthcare; Big Data and forecasting; and data mining and applications, with a modularity index of 0.19.
Methods
- The authors used a five-step literature review process, searching Scopus for “Big Data” and “Big Data AND Supply Chain” in title, abstract, and keywords.
- Initial results were 7,868 articles; duplicates were removed to leave 5,486, then the search was restricted to peer-reviewed journal articles and reviews, yielding 2,564 documents from 2006–2016.
- Bibliometric analysis was performed with BibExcel, and network analysis with Gephi, including citation analysis, PageRank, co-citation analysis, and Louvain modularity-based clustering.
Limitations
- The paper states that using particular keywords may have yielded different results with other keywords.
- The paper states that it used bibliometric and network analysis based on Pilkington and Meredith (2009), and other methods may be used.
- The paper states that the six-cluster classification may differ under other methods; evident from the text, the review relies on Scopus only and on the top 20 journals for the 286-article analysis, which may limit coverage.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
29 Own result · 16 Not checked yet · 8 Own interpretation · 3 Own method · 3 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This search resulted in 2564 relevant documents, published during a 10-year period i.e. 2006-2016.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.3 Refining the initial results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On excluding these outliers we are left with a network having 233 nodes and 1096 edges.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3 Co-citation analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we identified the top 20 journals appearing in the data, and it was found that these journals have published 286 articles in this field of research.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.4 Initial data statistics
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It was found that researchers belonging to cluster 1 have contributed by giving theoretical and conceptual studies on Big Data.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second highly cited paper, Boyd and Crawford (2012), is in sixth position”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Lastly, data mining and its applications were the main topic of interest for researchers in cluster 6.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It can be observed that cluster 1 is the most popular one”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it is observed that the topmost paper based on citations, Chiu et al. (2006) is not present in this list”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The most influential article during this period, having received 804 citations, is the work published by Chiu et al. (2006).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.1 Citation analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this algorithm was applied to 233-node network (see previous section) thereby creating six major clusters”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 22
- The quote only partly states this claim. 1
- It may state two separate claims as one. 9
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For instance, in both tables the top keywords include a combination of Big Data, supply chain management, data mining and privacy.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 4.1 Author influence
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The initial search resulted in 7868 articles.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.2 Initial results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Hence, cluster 1 was targeted to study the concept of Big Data and analytics.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be clearly observed that Wang with 6 publications dominates the list, and is followed by Li and Wang each with 5 publications.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 4.1 Author influence
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“McAfee and Brynjolfsson ( 2012) which was at third position in Table 5 is at second position in Table 6.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Research in cluster 2 mainly revolved around the application of Big Data in SCM, the problems associated with data quality and how this concept can be used to resolve the problems of supply chains.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By comparing these two tables it can be seen that there is a uniformity in the use of keywords in the title and the list of keywords.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 4.1 Author influence
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Jacobs (2009), not present in Table 5, dominates the list in Table 6.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the article by McAfee and Brynjolfsson (2012), cited 153 times”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.1 Citation analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On eliminating them, 5486 papers were left.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.3 Refining the initial results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Studies in cluster 5 were concerned with developing methods and models that can be used in forecasting election results or social science problems with a main focus on Bayes formula, and in improving these forecasts by using specific predictors.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be clearly seen from the figure that the number of publications on Big Data increased slowly from 2006 to 2013, but since then it has been increasing dramatically.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 7. Conclusion
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The set of 286 articles retrieved using the chosen keywords is representative of the literature on Big Data and SCM.
In the paper
“Since Rodriguez and Navarro(2004) categorised articles and reviews as certified knowledge”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.3 Refining the initial results
What the sources say
- Not checked yetcites Rodriguez and Navarro(2004) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, in order to build applications for SCM and healthcare, clusters 3, 5, and 6 are needed to build robust optimised applied tools for Big Data and data mining.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.
In the paper
“Recently, Chae (2015) noted that Big Data and social media have not been thoroughly examined in the field of SCM.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 2.3 Big Data and supply chain management
What the sources say
- Not checked yetcites Chae (2015) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“On comparing Scopus and Web-of-Science (WoS) databases, Yong-Hak (2013) observed that Scopus database is the more comprehensive since WoS includes only ISI indexed journals, only 12,000 titles.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.2 Initial results
What the sources say
- Not checked yetcites Yong-Hak (2013)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“To ensure that popularity and prestige are correlated, PageRank was introduced by Brin and Page (1998) to measure these concepts and prioritise the results of web keyword searches (Mishra et al. 2016a;2016b).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
What the sources say
- Not checked yetcites Brin and Page (1998) source
- Not checked yetcites Mishra et al. 2016a
- Not checked yetcites Mishra et al. 2016b
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while there is a scope of future work in cluster 6.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Rests on an assumption: The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.
In the paper
“The most widely used method for measuring the importance of a paper is citation analysis (Cronin and Ding 2011)”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
What the sources say
- Not checked yetcites Cronin and Ding 2011
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In particular, conceptualising Big Data (Cluster 1) is the first step to building applications for Big Data analytics focusing on SCM (in Cluster 2) and healthcare (Cluster 4).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.
None of the sources cited for this claim have been checked yet.
In the paper
“Co-citation analysis can be used in authors and/or publications order to track and study the relationship between authors, topics, journals or keywords (Small 1973;Pilkington and Liston Heyes 1999).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3 Co-citation analysis
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Understanding the role of BDA in improving SCM is extremely valuable since integration of BDA in operations and supply chains aids firms in realizing their customers in a better way, minimizing cost to serve, managing risk efficiently, and in generating new and unexpected sources of revenue (Sanders and Ganesan, 2015).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 6.1 Contributions to theory
What the sources say
- Not checked yetcites Sanders and Ganesan, 2015
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Data clustering aims at placing together sets of articles that share same characteristics (Radicchi et al. 2004).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.3.1 Data clustering
What the sources say
- Not checked yetcites Radicchi et al. 2004 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The value of parameter d has been the subject of debate, with scholars suggesting a value of 0.85 (Brin and Page 1998) while others a value of 0.5 (Chen et al. 2007).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it illustrates the relationships between the clusters and argues that better conceptualisation and consensus of Big Data and use of particular tools and techniques will result in better applications of Big Data in SCM (and healthcare)”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 7. Conclusion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To the best of our knowledge this is the first study attempting to identify the top contributing authors, countries and key research topics related to this field.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 7. Conclusion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: The set of 286 articles retrieved using the chosen keywords is representative of the literature on Big Data and SCM.
None of the sources cited for this claim have been checked yet.
In the paper
“The current study contributes to the literature on Big Data and extends current reviews (Sagiroglu and Sinanc 2013;Fosso-Wamba et al. 2015;Gandomi and Haider 2015;Wang et al. 2016;Khorheh et al. 2015;Mishra et al. 2016a;2016b)”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 6.1 Contributions to theory
What the sources say
- Not checked yetcites Sagiroglu and Sinanc 2013
- Not checked yetcites Fosso-Wamba et al. 2015
- Not checked yetcites Gandomi and Haider 2015 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“We used the bibliometric and network analysis for reviewing the literature based on Pilkington and Meredith (2009).”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 6.3 Limitations of the study
What the sources say
- Not checked yetcites Pilkington and Meredith (2009) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We have used classified the literature in six research clusters.Other methods may result in other classifications.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 6.3 Limitations of the study
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We have used particular keywords for this research, and it may be that the use of other keywords may have yielded different results.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 6.3 Limitations of the study
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The 286 articles published in the top 20 journals are representative of the broader literature on Big Data and supply chain management.
“we identified the top 20 journals appearing in the data, and it was found that these journals have published 286 articles in this field of research.”
The paper relies on this without support.
The set of 286 articles retrieved using the chosen keywords is representative of the literature on Big Data and SCM.
“Our review was based on the review of the literature on Big Data and SCM using 286 articles published in past 10 years.”
“We have used particular keywords for this research, and it may be that the use of other keywords may have yielded different results.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Similar methodologies are quite common in the literature [71,72].”
From A Review of Blockchain-Based Systems in Transportation (2019) · cites this paper as [72]
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 4.1 Author influence
“The top 20 keywords used in the paper titles and most popular keywords from the list of keywords are shown in Tables 4 and 5 respectively.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 4.1 Author influence
“Table 4: Top 20 keywords search results Table 5: Top 20 commonly used words in paper titles”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 5.2 PageRank analysis
“When comparing Table 5 and Table 6, it is observed that the topmost paper based on citations, Chiu et al. (2006) is not present in this list whereas McAfee and Brynjolfsson ( 2012) which was at third position in Table 5 is at second position in Table 6.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 4.1 Author influence
“Table 5: Top 20 commonly used words in paper titles”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.2 Initial results
“The initial search resulted in 7868 articles.When using "Big Data" as a keyword, the research yielded 6534 articles, whereas when using "Big Data and supply chain" 1334 articles.”
§ Big Data and Supply Chain Management: A Review and Bibliometric Analysis > 3.3 Refining the initial results
“To refine our search results, we removed the duplicates as few papers may be present in more than one combination of keywords.On eliminating them, 5486 papers were left.”
Citations
Cites
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Valente, T. W. (2003) · Genetics in Medicine · doi:10.1097/01.gim.0000061743.67794.c4 · Cited by 20,559
(1993) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(93)91697-o · Cited by 18,452
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Maximizing the spread of influence through a social network
Kempe, D., Kleinberg, J. M., & Eva Tardos, D. (2003) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956750.956769 · Cited by 7,541
Finding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
boyd, D., & Crawford, K. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.678878 · Cited by 5,479
Research works printed but not linked: 10
- Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70–76.
- Diebold, F. X. (2012). A personal perspective on the origin(s) and development of “Big Data”: The phenomenon, the term, and the discipline. Scholarly Paper No. ID 2202843, Social Science Research Network. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstractid=2202843 .
- Elberse, A. (2008). Should you invest in the long tail? HBS Centennial Issue Harvard Business Review, 86(7–8), 88–96. (HBS Centennial Issue).
- Hopkins, M. S., Lavalle, S., & Balboni, F. (2010). The new intelligent enterprise: 10 insights: A first look at the new intelligent enterprise survey. MIT Sloan Management Review, 52(1), 22.
- LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data: Analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big Data: The Next Frontier for Innovation. McKinsey Global Institute: Competition and Productivity.
- Mishra, D., Luo, Z., Jiang, S., Papadopoulos, T., & Dubey, R. (2016b). A bibliographic study on Big Data: Concepts. Trends and Challenges (Accepted Paper): Business Process Management Journal.
- Oxford Economics. (2011). The new digital economy: How it will transform business. http://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CD0QFjAC&url=http%3A%2F%2Fwww.businessandleadership.com%2Fdownload%2Ffs%2Fdoc%2Freports%2Fthe-new-digital-economy-1.pdf&ei=DeHUUpuWM66QiQfjs4HIBA&usg=AFQjCNGQKXaQccqoEy8Na0ctStXgaJPH1Q&bvm=bv.59378465,d.aGc . Accessed 10 January 14.
- Records, R. L., & Fisher, Q. K. (2014). Manufacturers connect the dots with Big Data and analytics. Computer Science Corporation (pp. 1–6). Accessed 31 May 2014.
- Wong, D. (2012). Data is the next frontier, analytics the new tool: Five trends in big data and analytics, and their implications for innovation and organisations. London: Big Innovation Centre.
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Metadata
- Authors
- Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J.
- Affiliations
- Indian Institute of Technology Kanpur (IN)
- University of Massachusetts Dartmouth (US)
- University of Plymouth (GB)
- Venue
- Annals of Operations Research
- Published
- 2016-06-06
- Volume
- 270
- Issue
- 1-2
- Pages
- 313-336
- ISSN
- 0254-5330 (ISSN-L)
- 1572-9338
- OpenAlex
- W2416848540
- Semantic Scholar
- 131cef9d6caf65a8cf7ad6b771c531c409e8930d
- Cited by
- 382
- References
- 155
- Citations by year
- 2026: 18 · 2025: 37 · 2024: 47 · 2023: 74 · 2022: 49 · 2021: 51 · 2020: 59 · 2019: 19 · 2018: 17 · 2017: 9 · 2016: 2
- FWCI
- 28.12
- Type
- journal article
- Language
- en
- Open access
- green
- kar.kent.ac.uk
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Annals of Operations Research (publishedVersion)
- Kent Academic Repository (University of Kent) (submittedVersion)
- PEARL (University of Plymouth) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Decision Sciences › Management Science and Operations Research › Supply Chain and Inventory Management
- Keywords
- big data, supply chain management, big data analytics, research trends, research clusters, future research directions, citation analysis, PageRank, research collaboration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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