The DOI matches its Crossref record; 110 of the 118 references it cites were found in a registry or an index; 10 of the 66 sources its claims cite were checked for support.
The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance
Open accessAbstract
Study of digital capabilities and hospital supply chain process integration with performance implications in hospital contexts.
Overview
Integrity signals
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Claims the paper makes
10 of 66 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, as underlined by Marra et al. (2012), there is no evidence that digital technology in itself contributes to the performance of an organization.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 1. Introduction
What the sources say
- Partly supportedcites Marra et al., 2012 sourceExperimental — accuracy not yet measured
Section 5.2 says the reviewed studies could not establish a correlation between IT solutions and firms’ growth, and calls for combining technology with management practices. But the review describes the evidence for a positive relationship as weak, not nonexistent, and its conclusions concern firms in a supply-chain context rather than organizational performance generally. Page numbers are not available in our copy. (LLM, conf=0.87)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Research exploring the impact of big data on the supply chain is still in its infancy, especially for the environmental dimension.”
- Also stated in: § 1. Introduction · plain
“With a few exceptions (e.g., Dubey et al., 2019a), empirical studies that have shown the impact of big data on the integration of green practices and environmental performances are still rare (Song et al., 2017).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 1. Introduction
What the sources say
- Partly supportedcites Song et al., 2017 sourceExperimental — accuracy not yet measured
Section 2.6, “Green supply chain management” (page unavailable), discusses four papers on big data and sustainable supply chains and says they highlight further research directions. That supports the idea of a developing research area, but the source does not establish that supply-chain research is in its infancy or that empirical work is especially scarce on green supply chain integration and environmental performance. (LLM, conf=0.74)
- Not matchedcites Dubey et al., 2019a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 1. Introduction · plain
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The PLS method can thus constitute an interesting alternative to estimate a more general model than SEM (Smart PLS) based on covariance, and it is less impacted by model specification errors (Henseler et al., 2015).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 5. Data analysis
What the sources say
- Could not verifycites Henseler et al., 2015 sourceExperimental — accuracy not yet measured
The Introduction describes PLS as a variance-based SEM method, and the Objectives discuss its use in a discriminant-validity simulation. Neither passage compares PLS with covariance-based SEM for sensitivity to model-specification errors; searches for that comparison and for model misspecification found no supporting passage in our copy. Page numbers were not provided. (LLM, conf=0.72)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Could not verify
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 suggests a partial mediating effect of GSCC.In other words, EPI had both a direct and an indirect impact on EP through the mediating role of GSCC.”
- Also stated in: § -----------------------------------------------Table 4 -------------------------------------------------------5.2. Model testing results · hedged
“This suggests a partial mediating effect of GSCC.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > -----------------------------------------------Table 4 -------------------------------------------------------5.2. Model testing results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § -----------------------------------------------Table 4 -------------------------------------------------------5.2. Model testing results · hedged
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 findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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 results highlight the moderating role of green digital learning in the relationships between BDA-AI and green supply chain collaboration, a major finding that has not been highlighted in the extant literature.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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 study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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 findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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 study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“GSCC had a significant but low impact (β = 0.250; t = 3.199) on the hospital's EP (H7).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > -----------------------------------------------Table 4 -------------------------------------------------------5.2. Model testing results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Probably not a substantial claim. 66
- It may state two separate claims as one. 27
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“However, the impact of big data on decision-making processes regarding green supply chain integration and environmental performance is not well established in the literature (Song et al., 2017).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.1. Big data analytics
What the sources say
- Could not verifycites Song et al., 2017 sourceExperimental — accuracy not yet measured
Section 2.6 (page unavailable) discusses big-data approaches to green supply chain management and mentions further research directions, but does not say that big data’s effects on decisions about supply chain integration and environmental performance are poorly established. Searches for green supply chain integration and for a research gap concerning environmental performance found no passage addressing that claim. (LLM, conf=0.72)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In this area, the literature suggests that the evolution of environmental practices in an external direction is only possible when companies possess the capacity to internally coordinate and integrate their processes (Koufteros et al., 2005).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Partly supportedcites Koufteros et al., 2005 sourceExperimental — accuracy not yet measured
The Discussion reports that internal integration enables customer and supplier integration and says external integration probably would not occur without a facilitating internal system. The study concerns product-development practices, not the evolution of environmental practices, and its qualified language does not establish that internal coordination makes external evolution possible only under that condition. The supplied passages have section labels but no page numbers. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Data and knowledge need to be shared in real time among internal functions to understand the changing situation and adapt to it in an appropriate way, particularly in dynamic organizations (Singh & El-Kassar, 2019).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Partly supportedcites Singh & El-Kassar, 2019 sourceExperimental — accuracy not yet measured
The Theory and hypotheses section (p. 2) urges firms facing change to use real-time information, cross-functional relationships, and communication to reorganize knowledge. It does not specifically say that both data and knowledge must be shared in real time among internal functions. (LLM, conf=0.78)
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“A learning focus leads organizations to collaborate externally and be more cross-cutting internally, which makes it easier to share new knowledge (Kumar et al., 2020).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Supportedcites Kumar et al., 2020 source
The Discussion of the results (section 5; page not provided) says learning orientation entails close collaboration with external supply-chain partners and leads to cross-functional knowledge sharing within the firm. Section 2.2 explains that learning-oriented organizations open their boundaries to internal and external sources to identify and assimilate new knowledge. (LLM, conf=0.93)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Acharya et al. (2018) report that BDA supported by new technologies is a means for sharing knowledge and making decisions within organizations.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Supportedcites Acharya et al. (2018) source
Section 2.2 (p. 3) says big data plays a crucial role in IT-supported knowledge co-creation and enables effective decision-making. The same section describes big data analytics as assisting knowledge sharing, while the Introduction (p. 1) links ICT-supported knowledge co-creation to organizational decision-making. (LLM, conf=0.92)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“the literature positions the role of internal integration mainly as a tangible resource and a formative antecedent of the use of BDA (Mikalef et al., 2020).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
What the sources say
- Partly supportedcites Mikalef et al., 2020 sourceExperimental — accuracy not yet measured
The Research model and Measurements sections describe tangible resources as formative components of big data analytics capability (BDAC). They do not identify internal integration as a tangible resource or model it as a formative antecedent of BDA use; the paper models BDAC rather than BDA use. (LLM, conf=0.81)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The recent study by Kumar et al. (2020) shows that learning-oriented organizations open their borders to external sources to identify and assimilate new knowledge.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
What the sources say
- Supportedcites Kumar et al. (2020) source
Section 2.2 states that learning-oriented organizations open their boundaries to external and internal sources to identify and assimilate new knowledge. The Discussion (section 5) repeats this point. Page numbers are not available in our copy. (LLM, conf=0.94)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
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
“R 2 for GSCC = 0.405, R 2 for EPI = 0.559, and R 2 for EP = 0.541”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > -----------------------------------------------Table 4 -------------------------------------------------------5.2. Model testing results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“According to the Environmental Protection Agency (EPA), hospitals consume the highest amount of energy among commercial buildings after the catering industry (Johnson, 2010).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.3. Green supply chain in hospitals
What the sources say
- Not checked — no full text of the sourcecites Johnson, 2010 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These authors claim that over 25% of U.S. hospitals use reprocessing as a means to reduce the volume of disposable waste generated.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.3. Green supply chain in hospitals
What the sources say
- Not checked — no full text of the sourcecites Kwakye et al., 2010 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“This theory defines how the structure of an organization should respond to uncertainty to achieve optimal performance.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Not checked — no full text of the sourcecites Galbraith, 1974 source
- Not matchedcites Tushman and Nadler, 1978
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Big data analytics associated with artificial intelligence plays a critical role in GSC management by eliminating information asynchronization and managing complex environmental data (Wu and Pagell, 2011).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Not checked — no full text of the sourcecites Wu and Pagell, 2011 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The healthcare supply chain lags behind compared to supply chains in other sectors and is characterized by poor integration of activities, limiting the dissemination of green practices (Johnson, 2010).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.3. Green supply chain in hospitals
What the sources say
- Not checked — no full text of the sourcecites Johnson, 2010 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“A study by Johnson (2010) concluded that hospitals tend to look for greener alternatives but they remain limited to certain activities and do not cover the entire supply chain.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.3. Green supply chain in hospitals
What the sources say
- Not checked — no full text of the sourcecites Johnson, 2010 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In the area of hospital pharmacies, the combination of AI and BDA has already been proved effective, notably in the design and development of new drugs (Wang et al., 2019).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.4. BDA-AI and hospitals
What the sources say
- Not checked — no full text of the sourcecites Wang et al., 2019 source
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The pollution generated by hospitals causes numerous risks related to pathological, pharmaceutical, chemical, radioactive, and safety aspects (Kaplan et al., 2012).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 1. Introduction
What the sources say
- Not matchedcites Kaplan et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“In the United States, for example, hospitals produce 6,600 tons of waste per day (Kaplan et al., 2012)”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 1. Introduction
What the sources say
- Not matchedcites Kaplan et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The OIPT emphasizes that a company evolves in a system, integrating several internal and external processes characterized by complexity and uncertainty (Thompson, 1967).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Not matchedcites Thompson, 1967
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“It provides insights for decision-making processes to improve GSCM and enhance environmental performance (Dubey et al., 2016b).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Not matchedcites Dubey et al., 2016b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“and consume more energy than any other type of commercial building (Singer & Tschudi, 2009).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 1. Introduction
What the sources say
- Not matchedcites Singer & Tschudi, 2009
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“According to Wong et al. (2015), an organization's capacity to process data could be increased by an improved culture of sharing mutually useful interorganizational information to strengthen the collaborative environment and reduce uncertainties related to coordination.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.2. Organizational information processing theory
What the sources say
- Not checked — no full text of the sourcecites Wong et al., 2015 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
“Thus, BDA-AI offers a viable opportunity for integrating environmental approaches in the internal and external supply chains, thereby reducing waste and air pollution.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 2.3. Green supply chain in hospitals
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: Waste flow management is a major hospital challenge.
- Rests on an assumption: Although hospital managers are aware of green issues, their environmental implementation efforts stay limited.
In the paper
“We supplemented the previous tests with a test for pathological collinearity, a sign for CMV, using the full collinearity test by Kock (2015).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
What the sources say
- Not matchedcites Kock (2015)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
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
“A Harman test showed that no single factor explained all of the variances of the items (41%) and that the first factor did not represent the majority of the variance.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The literature indicates that the Harman test is the most widely used verification tool for CMB (Podsakoff et al., 2003).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
What the sources say
- Not checked — no full text of the sourcecites Podsakoff et al., 2003 source
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For Graham (2018), the capacity to learn is an important antecedent for developing a collaborative environmental practice with suppliers.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Not checked — no full text of the sourcecites Graham (2018) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Following the same rationale as information processing, we support the idea that the use of BDA technologies help hospitals to exploit and process data from external and internal sources and also creates opportunities for collaborating with suppliers in the environmental decision-making process.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“A GSCC approach strengthens the monitoring level of suppliers who commit to supplying and using equipment and raw materials that respect the environment (Seman et al., 2019).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 5: GDLO positively moderates the relationship between BDA and EPI.
What the sources say
- Not checked — no full text of the sourcecites Seman et al., 2019 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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 pre-selected each respondent through closed questions concerning their knowledge of big data functionalities in relation to the supply chain.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The results of Zhu et al. (2012) confirm that internal environmental integration can minimize waste and toxic emissions and promote the use of materials that respect the environment in manufacturing companies.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 5: GDLO positively moderates the relationship between BDA and EPI.
What the sources say
- Not checked — no full text of the sourcecites Zhu et al. (2012) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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
“A second questionnaire was developed following some corrections and modifications suggested at the end of the pre-test.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A learning focus also encourages internal teams to voluntarily implement green action to improve EP (Jabbour, 2013).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Not checked — no full text of the sourcecites Jabbour, 2013 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“According to Hulland (1999), evaluating a PLS model requires three main factors: determining the nature of the relationships between the measurements and constructs, evaluating the reliability and validity of the measurements, and evaluating the final model.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 5. Data analysis
What the sources say
- Not matchedcites Hulland (1999)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The PLS approach was adopted because of its capacity to solve the system of simultaneous equations representing the network of relationships among the variables and, thus, to estimate coefficients that quantify these relationships (Tenenhaus, 1998).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4. Methodology
What the sources say
- Not matchedcites Tenenhaus, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
In the paper
“In the hospital sector, the study carried out by Oruezabala and Rico (2012) underlines how difficult it is for hospitals to measure their EP.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 5: GDLO positively moderates the relationship between BDA and EPI.
What the sources say
- Not checked — no full text of the sourcecites Oruezabala and Rico (2012) source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“For this purpose, several studies were used to operationalize the constructs of the conceptual model.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.3. Construct operationalization (measures)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Similarly, Zhu et al. (2012) observe that an external collaborative approach can help companies reduce waste, attain substantial EP, and improve their green image.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 5: GDLO positively moderates the relationship between BDA and EPI.
What the sources say
- Not checked — no full text of the sourcecites Zhu et al. (2012) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
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
“These comments were included in the final survey instrument.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Of the 520 surveys distributed, we received 181 completed surveys.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For this purpose, we reviewed the academic literature to guide us in the development of the questionnaire and the measurement scales of the model variables before purification (Dillman, 1978).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
What the sources say
- Not matchedcites Dillman, 1978
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Zhao et al. (2011) add that technological resources can play a key role in sharing information and knowledge with suppliers.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Not checked — no full text of the sourcecites Zhao et al. (2011) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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
“The hospitals in the sample were private and public and had different sizes in terms of the number of beds and the number of employees.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Supplier collaboration implies mutual engagement involving the sharing of resources and decision-making aimed at minimizing environmental impacts on the product development cycle (Yang et al., 2013).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 5: GDLO positively moderates the relationship between BDA and EPI.
What the sources say
- Not checked — no full text of the sourcecites Yang et al., 2013 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Environmental process integration is a matter of concern for companies when organizing their communication resources and sharing information among departments (Graham, 2018).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Not checked — no full text of the sourcecites Graham, 2018 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“A glossary of key terms from our study was provided to respondents to avoid confusion in understanding the questions.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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 carried out a pre-test with seven experts who had experience in logistics and supply chain management in hospitals.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“The early group included 98 respondents and the late group included 70 respondents.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Many studies have noted the direct impact of internal integration on external integration (Flynn et al., 2010).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Not checked — no full text of the sourcecites Flynn et al., 2010 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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
“In addition, we consulted four academics conducting research on the topic of healthcare supply chain and technology management to verify the content of the measures.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Dubey et al. (2019a) suggest that companies can use technologies such as AI to interpret and combine complex information from diverse sources.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Not matchedcites Dubey et al. (2019a)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“As a consequence, the use of BDA technologies creates greater visibility for interfunctional flows and a common alignment with the organization's objectives (Hofmann, 2017).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 3.1. Conceptual framework
What the sources say
- Not checked — no full text of the sourcecites Hofmann, 2017 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“At the end of the literature review, a first version of the questionnaire was reviewed and corrected by academics conducting research on the topic of healthcare supply chain and technology management to verify the content of the measures (DeVellis, 2012).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
What the sources say
- Not matchedcites DeVellis, 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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
“The questionnaire was then emailed to seven experts who had experience in logistics and supply chain management in hospitals.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Aragon-Correa and Sharma (2003) affirm that the interfunctional capacity of companies helps them to achieve proactive EP.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 5: GDLO positively moderates the relationship between BDA and EPI.
What the sources say
- Not checked — no full text of the sourcecites Aragon-Correa and Sharma (2003) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Thus, the final sample size was 168, which represents a response rate of 32% (Malhotra & Grover, 1998).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
What the sources say
- Not matchedcites Malhotra & Grover, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
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
“Respondents were assured that their identity would be kept confidential.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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 this article, we maintain that interfunctional, interorganizational collaboration on green issues ensures hospitals' EP.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 5: GDLO positively moderates the relationship between BDA and EPI.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
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 did not integrate 13 surveys because they contained incomplete responses.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For Sendlhofer and Lernborg (2018), digital learning is more effective because it encourages the participative training of workers, who can either learn on their own or in a group using videos and quiz-type activities.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Not checked — no full text of the sourcecites Sendlhofer and Lernborg (2018) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Internal environmental integration allows companies to avoid any disconnection and fragmentation between processes (Yang et al., 2013).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Hypothesis 2: BDA-AI-enabled decisions will have a positive effect on EPI.
What the sources say
- Not checked — no full text of the sourcecites Yang et al., 2013 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Kock and Lynn (2012) note that pathological collinearity is suggested by variance inflation factors (VIF) with a higher value than 3.3, indicating that the model could be loaded with CMV.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.1. Sampling and data collection
What the sources say
- Not matchedcites Kock and Lynn (2012)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
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
“The questionnaire items of the survey were adapted from previous studies.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 4.3. Construct operationalization (measures)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“learning can help organizations find the best ways to work and adopt a proactive environmental policy (Longoni et al., 2018).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
What the sources say
- Not checked — no full text of the sourcecites Longoni et al., 2018 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. 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
“the use of BDA-AI by a healthcare facility improves supply chain collaboration and internal integration among departments”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > -----------------------------------------------Table 4 -------------------------------------------------------5.2. Model testing results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“Our study was limited to the learning orientation at the environmental level”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 7. Limitations and future research avenues
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“This result may be related to the reduced focus of GDLO on the technical elements and hard skills often mobilized by decision-makers to improve the integration of internal processes.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“we consider that the existing big data in hospitals represent an asset that requires development in terms of analysis and internal processing to reduce the organizational complexity of hospitals and support GSC initiatives.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: No empirical study has demonstrated the relationship between BDA-AI technologies and environmental internal integration in a hospital context.
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
“the OIPT insists on the importance of aligning information processing capacities simultaneously at the internal and external levels of the hospital supply chain to strengthen environmental performance.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > . Discussion
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Activities of the internal processes (quality, purchasing, logistics, production, administration) can benefit from the advanced medical activities in terms of the use of BDA and AI to develop a robust environmental model.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The discriminant validity was verified if the shared variance between the latent variable and its indicators (AVE) was greater than the variances (squared correlation) of each variable with the other latent variables (Fornell & Larcker, 1981), as displayed in Table 4.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > -----------------------------------------------Table 3 -------------------------------------------------------
What the sources say
- Not checked — no full text of the sourcecites Fornell & Larcker, 1981 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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
“our conclusions are specific to the French healthcare sector and thus could be different for other countries, depending on their technological capabilities and green organizational culture applied to supply chain operations.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > 7. Limitations and future research avenues
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Sendlhofer and Lernborg (2018) recommend the use of digital learning in multidisciplinary and multi-stakeholder organizations.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
What the sources say
- Not checked — no full text of the sourcecites Sendlhofer and Lernborg (2018) source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“hospitals cannot succeed by only having access to good data and efficient information processing; they should also rely on green digital learning to strengthen collaboration and communication with suppliers and promote the co-construction of common ideas and initiatives for green practices.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. 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
“the findings contribute to the literature on the central role of the digitalization of learning, which seems necessary to support the knowledge of stakeholders on green practices and the improvement of EP.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. 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
“hospitals that provide strong cross-functional integration enhance collaboration with suppliers and achieve better green performance.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“BDA-AI participates not only to provide treatment capacities with the supplier network but also to the hospital's internal process by creating a shared vision supporting EP.”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > Research implications
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Hospitals consist of several interest groups with different preferences; hence, the decisionmaking process requires consensus among stakeholders to ensure the integration of the circular economy philosophy (Jabbour et al., 2019).”
§ The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance > . Discussion
What the sources say
- Not checked — no full text of the sourcecites Jabbour et al., 2019 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
BDA-AI research is plentiful in medicine but rarely applied to hospital supply chain processes or green practices.
“However, though research on the link between BDA and AI is abundant at the medical level, it is scarcely mobilized in the improvement of hospital supply chain processes and, in particular, green practices.”
The paper relies on this without support.
Few studies have examined BDA-AI, internal and external integration, and environmental performance together.
“Nevertheless, the combination of these three mechanisms, namely BDA-AI, internal and external integration, and the impact on EP, has rarely been studied.”
The paper relies on this without support.
How organizational learning can support green supply chain practices remains unclear.
“However, the mobilization of organizational learning to support green practices in supply chains is not yet clearly understood.”
The paper relies on this without support.
OIPT has not been empirically tested on hospital green supply chains.
“However, this theory has not yet been empirically applied to the specific topic of hospital green supply chain.”
The paper relies on this without support.
Hospital supply chain operations face uncertain conditions tied to patient demand and unpredictable medical activities.
“In hospital organizations, supply chain operations depend on several uncertain conditions that change in line with patient demand and the unpredictability of medical activities.”
The paper relies on this without support.
Hospitals have high interdependence between units, increasing organizational complexity.
“In addition, hospitals share a high degree of interdependence between their different units, which increases organizational complexity.”
The paper relies on this without support.
Waste flow management is a major hospital challenge.
“The management of waste flows, for example, constitutes a major challenge for hospitals, because it involves a rigorous process of collection, sorting, transport, storage, processing, and disposal.”
The paper relies on this without support.
Although hospital managers are aware of green issues, their environmental implementation efforts stay limited.
“Despite the awareness of hospital managers about green issues and their growing interest in environmental approaches, their endeavors to implement environmental approaches remain restricted.”
The paper relies on this without support.
Hospital managers underuse available data for green decisions.
“Despite the wide range of sources available, hospital managers do not sufficiently exploit these data to support green decision-making.”
The paper relies on this without support.
No previous study has focused on how internal integration related to green collaborations with suppliers is taken into account in hospital management.
“Although GSCC has become a widely debated subject in academic research, no previous study has focused on how internal integration related to green collaborations with suppliers is taken into account in hospital management.”
The paper relies on this without support.
No studies have focused on the relationship between internal environmental integration and supplier collaboration in the hospital sector.
“Although this subject has been widely debated in the literature, no studies have focused on this relationship in the hospital sector.”
The paper relies on this without support.
BDA-AI is a key construct for managing intra- and inter-organizational environmental issues.
“Furthermore, BDA-AI is a key construct to manage intra-and inter-organizational environmental issues.”
The paper relies on this without support.
The application of OIPT in the study of hospital operations remains insufficiently studied.
“the application of the OIPT in the study of hospital operations remains insufficiently studied.”
The paper relies on this without support.
No empirical study has demonstrated the relationship between BDA-AI technologies and environmental internal integration in a hospital context.
“no empirical study has demonstrated the relationship between BDA-AI technologies and environmental internal integration in a hospital context.”
The paper relies on this without support.
Previous studies have not empirically investigated the relationships and complementarities among BDA-AI, GSCC, EPI, and EP specifically in the hospital context.
“previous studies have not empirically investigated the relationships and complementarities among the BDA-AI, GSCC, EPI, and EP, specifically in the hospital context.”
The paper relies on this without support.
These studies have not explored the moderating role of digital learning on the relationship between BDA-AI technologies and the GSC process.
“these studies have not explored the moderating role of digital learning on the relationship between the use of BDA-AI technologies and the GSC process.”
The paper relies on this without support.
The hospital sector lacks capacity and maturity in implementing environmental practices.
“supply chain collaboration is a critical process in the hospital sector, which lacks capacity and maturity in the implementation of environmental practices.”
Self-check
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.
H6 standard deviation conflicts with its significant t-value
MajorThe H6 table gives the coefficient and standard deviation as the same value, yielding a t-ratio of 1 rather than 8.070. That discrepancy bears directly on the reported significance of H6.
Reported 8.07 · recomputed 1
“| H6 | EPI | EP | 0.563 | 0.563 | 8.070 | 0.000 | Supported |”
Hospital energy-use ranking changes across sections
MinorThe introduction says hospitals use more energy than every other type of commercial building, but a later section places the catering industry ahead of them.
“consume more energy than any other type of commercial building (Singer & Tschudi, 2009).”
“hospitals consume the highest amount of energy among commercial buildings after the catering industry (Johnson, 2010).”
H2 t-value does not match its coefficient and standard deviation
MinorThe H2 coefficient divided by its standard deviation is about 6.381, rather than the table's 6.967.
Reported 6.967 · recomputed 6.381443
“| H2 | BDA-AI | EPI | 0,619 | 0.097 | 6.967 | 0.000 | Supported |”
H4 t-value does not match its coefficient and standard deviation
MinorThe reported H4 coefficient and standard deviation yield a ratio of about 1.803, not 1.711. The difference is larger than rounding of the displayed inputs permits.
Reported 1.711 · recomputed 1.803279
“| H4 | GDLO*BDA GSCC | 0.110 | 0,061 | 1,711 | 0,088 | Supported | |”
H3 has conflicting t-values in the results and table
MinorThe results text reports an H3 t-value of 1.1961, while Table 5 gives 1.961. These values imply different assessments of the claimed statistical significance.
“The results highlighted a positive influence of EPI on GSCC (H3) (β = 0.259; t = 1.1961).”
“| H3 | EPI | GSCC | 0.259 | 0.132 | 1.961 | 0.050 | Supported |”
H2 t-value differs between prose and table
MinorThe results text gives the BDA-AI-to-EPI t-value as 6.697, whereas Table 5 reports 6.967.
“on EPI (β = 0.619; t = 6.697).”
“| H2 | BDA-AI | EPI | 0,619 | 0.097 | 6.967 | 0.000 | Supported |”
H1 t-value does not match its coefficient and standard deviation
MinorDividing the reported H1 coefficient by its standard deviation gives about 3.946, not the listed t-value of 4.144.
Reported 4.144 · recomputed 3.945946
“| H1 | BDA-AI | GSCC | 0,438 | 0.111 | 4.144 | 0.000 | Supported |”
Citations
Cites
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Abbad, H., Souak, S., & Mahjoub, S. (2025) · Revue Française de Gestion Industrielle · doi:10.53102/2025.39.01.1183 · Cited by 2
The effect of financial technology on sustainability performance: Mediating role of green finance
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AI-enabled Integration in the Supply Chain
Kieu, M.-K., Nayak, R., & Akbari, M. (2022) · Journal of Resilient Economies · doi:10.25120/jre.2.1.2022.3919 · Cited by 2
“It also aligns with the development of big data analytics and AI in supply chain integration field (Benzidia et. al.,2021).”
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Bibliometric Analysis of Logistics and Artificial Intelligence Research Trends in the Last 10 Years
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“Pesquisas anteriores encontraram uma associação significativa entre BDA-AI e o processo da cadeia de suprimento verde no contexto de hospitais (Benzidia et al., 2021) .”
InfluentialPatel, S. S., Bains, A., Ali, N., & Chawla, P. (2025) · Circular Economy and Sustainability · doi:10.1007/s43615-025-00611-6 · Cited by 2
“A study focused on a collaborative implementation of BD and AI to optimize the application of GSC in 168 French hospitals [ 102].”
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The Impact of Technology in Healthcare Supply Chain Performance: A SWOT Analysis
Raji, H. (2024) · International Conference on Connected Innovation and Technology (ICCITX) · doi:10.1109/iccitx61791.2024.11070509 · Cited by 1
“Benzidia et al. (2020) [45] demonstrate in their quantitative study how the deployment of Big Data Analytics (BDA)- AI technologies enhances internal coordination, supplier collaboration, and environmental performance in hospitals.”
Huy, P. Q., & Phuc, V. K. (2025) · Operations Research Forum · doi:10.1007/s43069-025-00485-2 · Cited by 2
Integration of AI in the SupplyChain: Systematic literature review on WOS
Bensfia, C., & AMINE, B. E. (2025) · International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA) · doi:10.1109/logistiqua66323.2025.11122794 · Cited by 1
“First, previous studies have confirmed discriminative AI's beneficial impact on performance and have mostly focused on [20,21,22].”
Using AI to Improve Emergency Care: A Detailed Study of Benefits, Challenges and What's Next
Sen, A. A. A., Alshehri, A. M. M., Tayan, O., Alkhodre, A. B., Bahbouh, N. M., & Alfarag, F. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115299 · Cited by 2
“[51] and [18] reported AI applications in fleet management enhanced operational efficiency by 20%, minimizing resource shortages.”
Impacts of Specific Big Data Capabilities on Large-Scale Group Decision-Making Characteristics
Aydıner, A. S., & Taskin, S. (2025) · Group Decision and Negotiation · doi:10.1007/s10726-025-09947-8 · Cited by 1
“However, previous studies did not identify any direct relationships, even though they suggested that management units should have a consciousness of BDA-TC in organizations (Benzidia et al. 2021; Gupta and George 2016; Mikalef et al. 2019).”
Pérez, E. V., & Prieto, A. V. (2025) · Innovar · doi:10.15446/innovar.v35n96.105881 · Cited by 1
“Una tendencia investigativa se enfoca en la Industria 4.0 y soluciones sostenibles, con el fin de aprovechar las capacidades internas de las empresas para alcanzar metas de innovación (Benzidia et al., 2021).”
InfluentialAldás, D. S., Mula, J., & Díaz‐Madroñero, M. (2025) · Sustainable Development · doi:10.1002/sd.70202 · Cited by 2
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“A substantial body of literature indicates that factor loading, composite reliability, and average variance extracted (AVE) are commonly used for performing validity and reliability tests (Alhwaiti, 2023; Benzidia et al., 2021; Hair et al., 2019; Shiferaw & Mehari, 2019).”
Sumiati, S., & Wahyundaru, S. D. (2025) · KnE Social Sciences · doi:10.18502/kss.v10i27.20053 · Cited by 1
“…evidence shows that manufacturers have used cutting-edge business analytics to optimize their operations to be more environmentally friendly (Benzidia et al., 2021), maximize their supply chain productivity (Cheng et al., 2021), and discover parameters that increase green output (Belhadi et…”
Xu, W., & Wang, D. (2025) · Systems · doi:10.3390/systems13111030 · Cited by 1
“Some studies focus on technology adoption, viewing it as the implementation of digital tools like blockchain, AI, and cloud computing [38].”
Tsai, F. M., Kurrahman, T., Lim, M. K., Sethanan, K., & Tseng, M. (2025) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2025.2592550 · Cited by 1
“Prior studies have mostly assumed that attributes associated with AIGPIE are independent [2,7,9].”
Xue, L., SATPAYEVA, Z. T., Tyulkubayeva, A., & Kangalakova, D. M. (2025) · Investment Management and Financial Innovations · doi:10.21511/imfi.22(4).2025.24 · Cited by 2
“AI enhances green efficiency mechanisms, reduces carbon emissions and energy costs for enterprises (Benzidia et al., 2021), and translates efficiency improvements into business profit growth (Cheng et al., 2022).”
4IR technology-enabled green supply chain management in China's manufacturing sector
Kim, C.-B., Liu, X., & Jeong, K. (2025) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-02-2025-0118 · Cited by 1
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Wu, X., Zhang, W. Y., Wang, Y., et al. (2026) · SAGE Open · doi:10.1177/21582440251415300 · Cited by 1
“Sustainability (SUS) is evaluated through environmental (Benzidia et al., 2021; M. Y. Wang et al., 2021), and social dimensions (X. W. Li et al., 2020 A 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree), is used to capture distinct responses (Willits et al., 2016).”
Zeng, B., Liang, L., Tiwari, A. V., Albishri, N. A., & Mohanty, J. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103279 · Cited by 1
Antecedents of hospital resilience: integrating resources, capabilities, and contingencies
Luca, C. D., Pellegrino, R., & Carbonara, N. (2026) · International Journal of Production Economics · doi:10.1016/j.ijpe.2026.109981 · Cited by 3
Badre, E. M., Sidouna, S., Chahbar, H., Mrabti, A., & Lahrech, A. (2026) · Business Management and Economics Engineering · doi:10.3846/bmee.2026.24087 · Cited by 1
“Benzidia et al. (2021) is the highest, with 306 citations, followed by Belhadi et al. (2024) with 248 citations.”
InfluentialThe Governance of Artificial Intelligence and Big Data in Healthcare Supply Chain Management
Panhwar, P., Sikder, M., & Zhai, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0216-2.ch009 · Cited by 1
Cybersecurity in governing artificial intelligence – the case of Polish cities
Sienkiewicz-Małyjurek, K. (2026) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-11-2025-0327 · Cited by 2
AI- and ML-driven intelligent design of digital twins
Manasa, R., Navya, K., Keshavamurthy, R., Madhura, R., & Kumar, N. M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27388-9.00011-8 · Cited by 1
Linking environmental management capabilities to learning and performance: evidence from SMEs
Li, Y., Lim, A., Pek, C. K., Xiang, X., & 黄海 (2026) · Journal of Enterprise Information Management · doi:10.1108/jeim-09-2025-0883 · Cited by 2
Luo, J., Shafiei, M. W. M., Ismail, R., & Chen, L. (2026) · Journal of Strategy & Innovation · doi:10.1016/j.jsinno.2026.200573 · Cited by 2
Artificial intelligence assimilation shapes sustainable performance through dynamic capabilities
Zhang, M., & Dong, W. (2026) · Scientific Reports · doi:10.1038/s41598-026-46291-6 · Cited by 9
“Benzidia et al. 10 also indicate that AI has a positive impact on corporate environmental performance.”
Ali, F., Amin, M. Y., Khan, S., et al. (2026) · SN Business & Economics · doi:10.1007/s43546-026-01136-2 · Cited by 1
“These innovative financial technologies modernise and strengthen the operational capabilities of finan - cial institutions, supporting global efforts to achieve sustainability goals (Benzidia et al. 2021).”
Kılıç, Z., GÜLEÇ, K., & Yanardağ, C. H. (2026) · Journal of International Health Sciences and Management · doi:10.48121/jihsam.1924894 · Cited by 1
AI for humanity: overcoming ethical and technical barriers in humanitarian aid
Omidiji, C., Ogbuju, E., Jimba, J., & Monday, A. M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01158-4 · Cited by 1
Song, M., Yang, M. X., Zeng, K. J., Wang, C., & Wang, F. (2026) · Business Strategy and the Environment · doi:10.1002/bse.70984 · Cited by 2
Green leadership and sustainable supply chain performance: The mediating and moderating effects
Hoque, M. O., & Das, D. C. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102973 · Cited by 4
Javed, A., Basit, A., Dawood, M., AlWadi, B. M., Ejaz, F., & Hossain, M. B. (2026) · Discover Sustainability · doi:10.1007/s43621-026-03465-6 · Cited by 2
“For the measurement of EP items used from [21] and for FP [63].”
Nguyen‐Viet, B., Chi-Luong, L., & Nguyen-Khanh, N. (2026) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-10-2025-1072 · Cited by 2
Li, C., Pan, H., Zhou, M., et al. (2026) · Systems · doi:10.3390/systems14070733 · Cited by 1
Digital Transformation, Organizational Learning, and Supply Chain Resilience: An fsQCA Analysis
Yang, C., Yang, Q., & Lu, Y. (2026) · Systems · doi:10.3390/systems14081027 · Cited by 1
“It enhances supply chain information processing by accelerating information acquisition, improving transparency, and facilitating coordination among supply chain partners [12,19,31,32,47,49,50], but its three dimensions perform distinct and interdependent functions.”
Seddik, S., Routaib, H., & Haddadi, A. E. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-90321-3_60 · Cited by 1
Bigdata Intervention in the Healthcare Supply Chain
Jinugu, A., Jinugu, V. T., & V., M. L. V. (2022) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-7998-9506-0.ch015 · Cited by 1
Sustainability and the Digital Supply Chain
Orji, I. J., Kusi‐Sarpong, S., & Okwara, U. K. (2022) · Springer eBooks · doi:10.1007/978-3-030-89822-9_93-1 · Cited by 1
Bélanger, V., Cousineau, M., Snowdon, A. W., & Beaulieu, M. (2022) · Management & Avenir Santé · doi:10.3917/mavs.009.0081 · Cited by 1
Ali, A., Rafiq, A., Hussien, M., Sarwat, S., & Raziq, A. (2023) · Global Business Review · doi:10.1177/09721509231183767 · Cited by 2
Wang, S. (2024) · Agricultural & Forestry Economics and Management · doi:10.23977/agrfem.2024.070104 · Cited by 1
“This integrated perspective can fully grasp all aspects of the agricultural supply chain to ensure comprehensive planning and effective management [11].”
BackgroundIsnin, U. H. M., Almadani, K. S., Hamdika, H., Alias, N. B., Aziz, N. A. B., & Saipol, H. F. S. (2024) · AIP conference proceedings · doi:10.1063/5.0192079 · Cited by 1
Delgoshaei, A., & Ariffin, M. K. A. M. (2024) · International Journal of Pharmaceutical and Healthcare Marketing · doi:10.1108/ijphm-02-2024-0018 · Cited by 1
Zaid, A. A., Othman, M. A., Qubbaj, I., & Asaad, A. R. (2024) · International Journal of Quality & Reliability Management · doi:10.1108/ijqrm-03-2024-0084 · Cited by 7
Anand, J., Ghani, H. A., Yusoff, N. B., & Thoti, K. K. (2024) · International Conference on Artificial Intelligence and Data Sciences (AiDAS) · doi:10.1109/aidas63860.2024.10730136 · Cited by 1
“Research does a thorough examination of how Green Artificial Intelligence impacts agriculture and biology [1].”
BackgroundKuo, S., & Chen, L.-B. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3513775 · Cited by 2
“…more pronounced than their loading on other constructs within the model; 2) the composite reliability (CR) should be greater than 0.6; and 3) the average variance extracted (AVE) should be greater than 0.5 and the square root of the AVE should be greater than the interconstruct correlations [75].”
InfluentialTuyishime, A., Hahirwa, I., Njunwa, K. J., & Nyiligira, J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12239-1 · Cited by 1
Health education and technological innovation: an unpostponable relationship
Castillo, V. S., & Cano, C. A. G. (2024) · Health Leadership and Quality of Life · doi:10.56294/hl2024.436 · Cited by 1
The Role of Data Analytics in Enhancing Decision-Making Processes in Healthcare Management
Dey, S., & Sharma, C. K. (2022) · Health Leadership and Quality of Life · doi:10.56294/hl2022.93 · Cited by 2
Szczepaniuk, H., & Szczepaniuk, E. K. (2022) · Journal of Security and Sustainability Issues · doi:10.47459/jssi.2022.12.12 · Cited by 1
Implementing Industry 4.0 Technologies: Future Roles in Purchasing and Supply ManagementPreprint
Delke, V., Schiele, H., Buchholz, W., & Kelly, S. (2023) · Social Science Research Network · doi:10.2139/ssrn.4017007 · Cited by 34
“Reflecting the complementary nature of Industry 4.0 technologies, artificial intelligence can also support big data analysis (Benzidia et al., 2021a; Dubey et al., 2020), which can provide additional support to smart PSM activities (Xu et al., 2021).”
BackgroundInfluentialHaipeng, Z., & Furuoka, F. (2024) · Preprints.org · doi:10.20944/preprints202403.1743.v1 · Cited by 1
Tetteh, F. K., Nyamekye, B., Williams, E., Attah, J., & Degbe, F. D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4848537 · Cited by 1
Impact of the pandemic on digital management: a bybliometric analysis
Shagyrbay, A., & Аширбекова, Л. (2021) · Central Asian Journal of Social Sciences and Humanities · doi:10.26577/cajsh.2021.v7.i4.03
Guanyuan, L. (2021) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca52323.2021.9675989
“At the same time, a new mutation operator is proposed to enhance the local optimization ability of the algorithm [14].”
Background(2022) · Presses universitaires de Provence eBooks · doi:10.4000/books.pup.57517
Opportunities for Simulation-Based Optimization in a Hospital Environment
Bottani, E., Franchi, B., Monferdini, L., et al. (2022) · International Journal of Practical Healthcare Innovation and Management Techniques · doi:10.4018/ijphimt.315771
Why Do We Need Sustainable Digital Marketing?
Tchanturia, N., & Dalakishvili, R. (2023) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-8681-8.ch020
Avaliação de Desempenho Logístico Hospitalar: uma Revisão de Literatura
Lima, L. S. D. S., VASCONCELOS, V. Y. Q. D., Lopes, E. D. J., & Bouzon, M. (2023) · Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção · doi:10.14488/enegep2023_tn_st_400_1961_45699
Artificial Intelligence and Big Data Analytics in Green Supply Chain Management
Jrad, M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1046-5.ch001 · Cited by 1
Artificial Intelligence and Supply Chain Management: An Integrative Literature Review
Ennajeh, L., & Najar, T. (2023) · IEEE Afro-Mediterranean Conference on Artificial Intelligence (AMCAI) · doi:10.1109/amcai59331.2023.10431506
Li, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4757661
Mattayaphutron, S. (2021) · The Asian Bulletin of Big Data Management · doi:10.62019/abbdm.v1i1.18
“Big data applications are prominent across industries with easy access and streamlined technology (Benzidia et al.,2021).”
BackgroundThe Impact of Covid-19 on Health Care Organization
Ministry of Health, M., & Zadjali, N. A. (2023) · Annals of Public Health and Research · doi:10.47739/2378-9328.publichealth.1125
Future of Supply Chain Management
Harland, C. M. (2024) · Research for development · doi:10.1007/978-3-031-52247-5_8
Yang, X. (2024) · Journal of Electrical Systems · doi:10.52783/jes.3737
“Benzidia et al. [24] have suggested the effects of intelligence along big data analytics on hospital environmental performance and green supply chain process integration.”
BackgroundAntecedents of Hospital Resilience: Integrating Resources, Capabilities, and ContingenciesPreprint
Luca, C. D., Pellegrino, R., & Carbonara, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4846301
Network Threat Awareness Model with Smart Grid for Big Data Analysis
Dang, H., Shao, D., & Yue, L. (2024) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece60827.2024.10548632
“Industrial big data is also multi-source and heterogeneous [1], [2].”
BackgroundThe Role of Ai in Facilitating Supplier Collaboration and Enhancing Supply Chain PerformancePreprint
Mahama, U. F. A., Boison, D. K., Antwi-Boampong, A., Doumbia, M. O., & Doshi, R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4862726
العال, م. ع., رياض, ع., & مليجى, م. م. ع. (2024) · Journal of Environmental Studies and Researches · doi:10.21608/jesr.2024.360578
Harnessing the Power of Big Data Analytics to Transform Supply Chain Management
Singh, T., Poulose, J., & Sharma, V. (2024) · Springer eBooks · doi:10.1007/978-981-97-2902-9_13
Text Analysis on Green Supply Chain Practices of Electronic Companies
Balan, S., Conlon, S. J., & Reithel, B. J. (2024) · International Journal of Decision Support System Technology · doi:10.4018/ijdsst.358950
Hrouga, M., & Said, D. (2024) · Logistique & Management · doi:10.1080/12507970.2024.2400888 · Cited by 1
“De plus, la technologie BD a été également utilisée pour améliorer les performances durables de la CA (Benzidia, Makaoui, et Bentahar 2021).”
Nguyen, T. T., Le-Anh, T., Nguyen, A. Q., Pham, H. Q., & Nguyen, T. N. (2025) · Springer proceedings in business and economics · doi:10.1007/978-981-97-9992-3_17
MANAGING HOSPITAL LOGISTICS USING AI
Stawiarska, E. M., Stawiarski, P., & Stawarski, M. (2024) · SWS International Scientific Conference on Social Sciences · doi:10.35603/sws.iscss.2024/s16/92
Artificial Intelligence for Smart Supply Chain Management: Opportunities and Challenges
Ali, M. R., & Nipu, S. M. A. (2025) · Springer eBooks · doi:10.1007/978-981-96-1333-5_2
Big data analytics in supply chain management and its impact on business performance
Yin, D., & Hui, Z. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-0202
Vijyakumar, R., & Geetha, A. (2024) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses63760.2024.10910669
“Information of this nature might be useful in the strategies for attracting green customers [16].”
Herawati, N. D., Tyas, W. M., & Winahyu, P. (2024) · Ilomata International Journal of Management · doi:10.61194/ijjm.v6i2.1588
A Review of AI-Driven Dynamic Pricing and Emission Reduction Strategies in Green Supply Chains
Hong, P., & Chen, W. (2025) · Frontiers in Humanities and Social Sciences · doi:10.54691/501r5m22
“Benzidia et al. (2021) extended the organizational information processing theory to incorporate BDA-AI and positioned digital learning as a moderating factor in green supply chain processes.”
InfluentialComparative Evaluation of Ai Techniques Using the MOORA Method in Supply Chain Operations
Pandipati, S. (2025) · Journal of Artificial intelligence and Machine Learning · doi:10.55124/jaim.v3i1.252
“[8]Artificial intelligence (AI) is frequently used to examine large amounts of data techniques that can manage large amounts of data to support clinical research.”
Zhang, Y., Li, H., & Yin, C. (2025) · Journal of business and marketing. · doi:10.62517/jbm.202509112
Leveraging Human-Machine Interaction to Optimize Sustainability with Big Data and AI Technologies
Khan, A., Hsieh, C.-H., & Hung, C.-W. (2025) · doi:10.1109/icct-pacific63901.2025.11012901
“It has also been emphasized that a company's inter-functional capabilities are crucial for achieving proactive SP [17].”
InfluentialThe Impact of Digital Capabilities on Supply Chain Efficiency, Transparency and Resilience
Majidzadeh, M. (2025) · ANCIENT LAND · doi:10.36719/2706-6185/47/264-272
Impact Of Green Human Resource Management to Environmental Performance
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Metadata
- Authors
- Benzidia, S., Makaoui, N., & Bentahar, O.
- Affiliations
- Centre Européen de Recherche en Economie Financière et Gestion des Entreprises (FR)
- Corresponding author
- Bentahar, Omar
- ORCID
- Bentahar, Omar
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 165
- Pages
- 120557
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W3118651707
- Semantic Scholar
- 7b6ea9c9634f01d4a4eefa67f4401d79598a7830
- Cited by
- 703
- References
- 118
- Citations by year
- 2026: 126 · 2025: 190 · 2024: 175 · 2023: 119 · 2022: 68 · 2021: 25
- FWCI
- 86.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Technological Forecasting and Social Change (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- HAL (submittedVersion, cc-by-nc)
- Funders
- Agence Nationale de la Recherche (ANR-20-GES1-0003)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Sustainable Supply Chain Management
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- big data analytics, artificial intelligence, green supply chain, organizational information processing theory, healthcare logistics, moderating effect
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 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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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