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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“Similarly, Benzidia et al. (2021) emphasized the importance of sustainable treatment processes, aligning with our emphasis on the low sustainability of current treatment methods.”
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“The business aspects of AI focus on organizational performance and productivity (Awan et al., 2021; Benzidia et al., 2021).”
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The Road to corporate sustainability: The importance of artificial intelligence
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“BDAC refers to the capacity of manufacturers to accumulate, manipulate, scrutinise and extract valuable insights from large quantities of data from diverse sources both internal and external to their organisation (Benzidia et al., 2021).”
BackgroundMethodInfluentialSong, Y., Du, C., Du, P., et al. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123159 · Cited by 126
AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability
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“It achieves this by optimizing energy consumption, minimizing waste, and reducing carbon emissions (Benzidia et al., 2021).”
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Generative AI usage and sustainable supply chain performance: A practice-based view
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Generative AI-enabled supply chain management: The critical role of coordination and dynamism
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Can digital transformation alleviate corporate tax stickiness: The mediation effect of tax avoidance
Zhou, S., Zhou, P., & Ji, H. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122028 · Cited by 123
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“Thus, AI not only enhances demand prediction but also introduces a new paradigm for developing forecasting strategies [6].”
AI adoption in supply chain management: a systematic literature review
Shahzadi, G., Jia, F., Chen, L., & John, A. (2024) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-09-2023-0431 · Cited by 174
Bag, S., Rahman, M. S., Srivastava, G., Shore, A., & Ram, P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122154 · Cited by 123
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Achieving sustainability through multifaceted green functions in manufacturing
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“Third, extant literature has primarily focused on moderating roles of intellectual capital [37], lockdown during Covid-19 [38], and green digital learning [39].”
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“The use of big data analytics and AI technologies have significant positive effects on environmental process integration, green supply chain cooperation, and environmental performance (Benzidia et al. 2021), and they significantly improve urban green development efficiency (Chen and Liu 2022).”
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“(Benzidia, Makaoui, and Bentahar 2021) comprehensive solution to resolving these issues.”
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Cheng, J., Singh, H., Zhang, Y.-C., & Wang, S. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139410 · Cited by 114
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“Consistent with the results of this research, the effect of using AI technology in green supply chain processes (Benzidia et al., 2021) and the development of sustainable cities (WU, 2021) has been referred in various studies.”
ResultWamba, S. F., de Queiroz, M. M., & Trinchera, L. (2023) · International Journal of Production Economics · doi:10.1016/j.ijpe.2023.109131 · Cited by 108
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“The impact on the performance has been also investigated by studies that have linked benefits of BDA and artificial intelligence with green supply chain integration process [40].”
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Zhu, C., Du, J., Shahzad, F., & Wattoo, M. U. (2022) · Sustainability · doi:10.3390/su14063379 · Cited by 95
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Role of artificial intelligence in carbon cost reduction of firms
Tseng, C.-J., & Lin, S. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141413 · Cited by 86
Teixeira, A. R. D. B., Ferreira, J. V., & Ramos, A. L. (2025) · Information · doi:10.3390/info16050399 · Cited by 153
“Efforts to develop explainable AI frameworks, such as the SHapley Additive exPlanations (SHAP) technique, have been explored to enhance transparency and interpretability in AI-driven decision support systems [57].”
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How does ESG performance promote corporate green innovation?
Long, H., Feng, G., & Chang, C. (2023) · Economic Change and Restructuring · doi:10.1007/s10644-023-09536-2 · Cited by 78
“Using artificial intelligence and big data technology in supply chain management significantly promotes environmental process integration and green supply chain collaboration (Benzidia et al. 2021).”
BackgroundBenzidia, S., Bentahar, O., Husson, J., & Makaoui, N. (2023) · Annals of Operations Research · doi:10.1007/s10479-022-05157-6 · Cited by 84
“Additionally, internal teams do not have sufficient knowledge of other functional areas (Benzidia et al., 2021).”
InfluentialSelf-citationRole of lean, agile, resilient, green, and sustainable paradigm in supplier selection
Sonar, H. C., Gunasekaran, A., Agrawal, S., & Roy, M. H. (2022) · Cleaner Logistics and Supply Chain · doi:10.1016/j.clscn.2022.100059 · Cited by 77
Zou, W., & Pan, M. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-22159-w · Cited by 76
“Digital technology integrates and manages green consumption preference information and assists enterprises to carry out corresponding green R&D and decision-making (Benzidia et al., 2021).”
BackgroundBag, S., & Rahman, M. S. (2023) · Business Strategy and the Environment · doi:10.1002/bse.3507 · Cited by 78
Chotia, V., Cheng, Y., Agarwal, R., & Vishnoi, S. K. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123315 · Cited by 86
Nonlinear effect of industrial robot applications on carbon emissions: Evidence from China
Liu, B., Yang, X., & Zhang, J. (2023) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2023.107297 · Cited by 76
Alghamdi, O., & Agag, G. (2023) · Sustainability · doi:10.3390/su151914296 · Cited by 91
Can climate risk exposure compel companies to undergo a green transformation?
Fang, X. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.140310 · Cited by 74
Munir, A., Hussain, A., Farooq, M. U., Rehman, A. U., & Masood, T. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123146 · Cited by 90
A literature review on green supply chain management for sustainable sourcing and distribution
Hariyani, D., Hariyani, P., Mishra, S. K., & Sharma, M. K. (2024) · Waste Management Bulletin · doi:10.1016/j.wmb.2024.11.009 · Cited by 120
Digitalization drives green transformation of supply chains: a two-stage evolutionary game analysis
Fan, W., Wu, X., & He, Q. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06050-0 · Cited by 75
“Digital technology can enable supply chain enterprises to break down information barriers, establish information interconnections, promote green cooperation, and improve the efficiency of supply chain operations (Benzidia et al., 2021).”
Martínez‐Falcó, J., Sánchez‐García, E., Marco‐Lajara, B., & Visser, G. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142186 · Cited by 73
Abideen, A. Z., Pyeman, J., Sundram, V. P. K., et al. (2021) · Sustainability · doi:10.3390/su13168997 · Cited by 69
“BD [65] A partial least squares regression-based structural equation modeling method was used to test a sample of data from 168 French hospitals.”
MethodAl‐Khatib, A. W. (2022) · Business Process Management Journal · doi:10.1108/bpmj-08-2022-0416 · Cited by 72
A critical review on applications of artificial intelligence in manufacturing
Mypati, O., Mukherjee, A., Mishra, D., Pal, S. K., Chakrabarti, P. P., & Pal, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10535-y · Cited by 80
Le, T. T., Nhu, Q. P. V., Bao, T. B. N., Thao, L. V. N., & Pereira, V. E. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141290 · Cited by 80
Resilience and agility in sustainable supply chains: A relational and dynamic capabilities view
Akram, M. U., Islam, N., Chauhan, C., & Yaqub, M. Z. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114855 · Cited by 67
“Technologies such as big data and predictive analytics can be utilised to locate the sources of potential disruptions and improve resilience (Benzidia et al., 2021; Papadopoulos et al., 2017).”
BackgroundInfluentialTruant, E., Borlatto, E., Crocco, E., & Bhatia, M. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139493 · Cited by 69
Rahat, N., Sahni, S., & Nasim, S. (2023) · International Journal of Consumer Studies · doi:10.1111/ijcs.12997 · Cited by 70
Liu, Y., Fang, W., Feng, T., & Gao, N. (2022) · Industrial Management & Data Systems · doi:10.1108/imds-11-2021-0696 · Cited by 74
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“Big data analytics can boost green supply chain performance by exploiting data from different external sources that create new opportunities for collaboration with suppliers and key stakeholders in the environment-related decision-making process (Benzidia et al., 2021).”
BackgroundAl‐Khatib, A. W. (2023) · European Journal of Innovation Management · doi:10.1108/ejim-09-2022-0491 · Cited by 71
Halbusi, H. A., Soto‐Acosta, P., Popa, S., & Hassani, A. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3277078 · Cited by 61
“When referred to using knowledge, learning orientation is an organizational process that emphasizes getting knowledge that will likely be helpful to the business through the supply of data and information [18], [24].”
BackgroundMethodInfluentialXie, Z., Wang, J., & Miao, L. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121091 · Cited by 62
Shafique, M. N., Yeo, S. F., & Tan, C. L. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123074 · Cited by 69
Cheng, W., Li, Q., Qun-qi, W., Ye, F., & Jiang, Y. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32290 · Cited by 78
“This conclusion validates the findings of the studies conducted by Benzidia et al. [31] and Li and Shen [46].”
BackgroundResultInfluentialFeng, T., & Sheng, H. (2023) · Business Strategy and the Environment · doi:10.1002/bse.3414 · Cited by 66
Shi, H., Feng, T., & Zhu, Z. (2023) · Business Process Management Journal · doi:10.1108/bpmj-08-2022-0411 · Cited by 62
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Big Data Analytics and AI for Green Supply Chain Integration and Sustainability in Hospitals
Allahham, M. I., Sharabati, A. A., Hatamlah, H. M. A., Ahmad, A., Sabra, S., & Daoud, M. K. (2023) · WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT · doi:10.37394/232015.2023.19.111 · Cited by 83
“The path coefficients for H1 and H2 are 0.438 and 0.619, respectively, indicating a strong positive relationship, [1].”
BackgroundInfluentialChu, Z., Zhang, Z., Tan, W., & Chen, P. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120806 · Cited by 57
Ma, L., & Chang, R. (2024) · Management Decision · doi:10.1108/md-10-2023-1822 · Cited by 77
Wu, B., Ren, K., Fu, Y., He, D., & Pan, M. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123789 · Cited by 65
Shahzad, F., Du, J., Khan, I., & Wang, J. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.911392 · Cited by 52
“However, the impact of BDAC on GSCM and environmental performance (EP) decision-making processes is not entrenched in the literature (Benzidia et al., 2021).”
BackgroundInfluential
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
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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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