Improving Corporate Environmental Performance Through Big Data Analytics Implementation: The Role of Industry Environment
Abstract
:1. Introduction
2. Literature Review
2.1. Big Data Analytics Conceptualization
2.2. Corporate Environmental Performance
2.3. Organizational Learning Theory
3. Conceptual Framework and Hypotheses Development
3.1. The Relationship Between BDA and CEP
3.2. The Influence of Industry Environment
4. Research Methodology
4.1. Sample and Data Collection
4.2. Measures
4.2.1. Big Data Analytics
4.2.2. Corporate Environmental Performance
4.2.3. Industry Environment
4.2.4. Control Variables
4.2.5. Model Specification
β6CFit−1 + β8BDAit−1 x IDE it−1 + εit
4.2.6. Assessment of Causality, Heterogeneity, and Statistical Analytics Technique
5. Results
5.1. Descriptive Statistics
5.2. Testing the Relationship Between BDA and CEP
5.3. The Moderating Effect of Industry Environment
5.4. Robustness Checks
6. Discussion
6.1. Key Findings
6.2. Theoretical Implications
6.3. Managerial Implications
7. Limitations and Future Research Directions
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Company Name w/25 | w/50 |
---|---|
(announc* or select* or choos* or chose* or deploy* or “work with” or “working with” or “works with” or “worked with” or collaborat* or partner* or “rolling out” or “rolled out” or “rolls out” or “roll out” or “uses” or “using” or “used” or tap* or “turned to” or “turns to” or “turning to” or “turn to” or implement* or agreement* or contract* or purchas* or signs or sign or signing or signed or award* or licens* or install* or acquir* or “goes live” or “going live” or “went live” or launch* or unveil* or adopt* or align* or add* or build* or built or establish* or expan* or form* or spend* or spent or ink* or leverag* or exten* or utiliz* or compact* or deal* or hir* or employ* or increas* or join* or alliance* or complet* or introduce* or “in effect” or “in use” or “apply” or “applies” or “applied” or “applying” or “application” or arrang* or procur* or cooperat* or “work together” or “working together” or “worked together” or “works together”) | (“analytics” or “data virtualization” or analysis technique* or “data warehouse” or decision support system* or “Hadoop” or “HANA” or “artificial intelligence” or “high performance analytics” or “API” or “high performance computing” or “behavioral analytics” or “historical data analytics” or “information analysis software” or “big data” or “infrastructure-as-aservice” or “Iaas” or “big data analytics” or “in-memory database” or “algorithms” or “intelligence technology” or “data mining” or “intelligent diagnostics system” or intelligent system* or “machine learning” or “marketing analytics” or “marketing promotion optimization” or “data visualization” or “natural language” or “business analytics” or “optimize processes” or “business intelligence” or “parallel processing” or “predictive analytics software” or “predictive modeling” or “security intelligence” or “business process analysis” or “software as a service” or “Saas” or “cloud computing” or “SQL” or “cloud” or “social intelligence” or “collaboration tools” or “collaboration tool” or “text mining” or “crowdsourcing solution” or “visual analytics” or “data center” or “Watson” or “data driven” or “warehouse miner” or “data processing” or “warehousing technology” or “data science” or “workforce analytics” or “data stewardship model” or “NoSQL” or “in-database” or “non-relational database” or “data lake” or “platform as a service” or “Paas” or “database as a service” or “Daas” or “social listening” or “complex event system” or “event processing” or “unstructured data” or “web analytics” or “stream analytics” or “web mining” or “social network analysis” or “social network analytics” or “rule-based system” or “mobile intelligence” or “internet of things” or “IoT”) |
Industry | Number of Firms | Percentage of Sample (%) | SIC Code |
---|---|---|---|
Manufacturing Retail Financial and Insurance Wholesale Transportation Utility Healthcare Communication Others Total | 31 29 26 22 18 15 12 10 9 172 | 18 17 15 13 10 9 7 6 5 100 | 10–37 52–59 61–66 51 4–45 49 80 48 Others |
Variable | Definition and Measure | Sources |
---|---|---|
BDAi,t | We counted the cumulative number of BDA implementations in each year firm to measure this variable | [90,94,95] |
CEPi,t−1 | Logarithm of the sum of enterprise operating income/Logarithm of enterprise pollutant discharge fees | [96,97] |
COMi,t−1 | We used the Herfindahl index to measure complexity. | [103,104] |
DYMi,t−1 | We measured dynamism as the volatility of sales in a dominant industry over a period of 5 years | [98,99,100] |
MUNi,t−1 | We used the sales growth in a dominant industry over a period of 5 years to measure munificence | [101,102] |
CashFlowi,t−1 | A fund from operations over sales for firm i at time t − 1 as a percentage | [105] |
Leveragei,t−1 | Total debt over total assets for firm i at time t − 1 as a percentage | [105] |
Sizei,t−1 | Natural log of assets of each firm i at time t − 1 | [105] |
R&Di,t−1 | Research and development expenses over total sales revenue at time t − 1 charged to firm i as a percentage | [105] |
Variables | Mean | SD | CEPit−1 | BDAi,t | COMi,t | DYMi,t | MUNt−1 | CashFlowit−1 | R&Dit−1 | SIZEi-t | LEVi-,t |
---|---|---|---|---|---|---|---|---|---|---|---|
CEPii,t−1 | 27.32 | 16.29 | 1.00 | ||||||||
BDAi,t | 0.217 | 0.108 | 0.41 ** | 1.00 | |||||||
COMi,t | 0.021 * | 0.310 ** | 0.227 ** | 0.409 ** | 1.00 | ||||||
DYMi,t | 0.172 ** | 0.206 ** | 0.129 ** | 0.083 | 0.106 * | 1.00 | |||||
MUNt−1 | 0.094 | 0.024 * | 0.012 * | 0.105 * | 0.075 | 0.011 * | 1.00 | ||||
CashFlowii,t−1 | 14.230 | 11.215 | 0.21 ** | 0.105 * | 0.322 ** | 0.410 ** | 0.023 * | 1.00 | |||
R&Dii,t−1 | 4.023 | 2.658 | 0.08 | 0.06 | 0239 ** | 0.226 ** | 0.329 ** | 0.199 ** | 1.00 | ||
SIZEi,t−1 | 89.344 | 62.319 | 0.084 | 0.077 | 0.0931 | 0.031 * | 0.021 * | 0.07 | 0.09 | 1.00 | |
LEVi,t−1 | 23.120 | 16.328 | 0.328 ** | 0.217 ** | 0.053 * | 0.312 ** | 0.429 ** | 0.112 * | 0.143 ** | 0.329 ** | 1.00 |
Variables | Main Effect Model 1 | Moderating Effect | |||
---|---|---|---|---|---|
Model 2 | Model 3 | Model 4 | Model 5 | ||
Constant | 0.503 (0.310) | 0.793 (0.196) | 0.805 (0.208) | 0.863 ** (0.437) | 0.816 ** (0.402) |
Lagged CEP | 0.714 ** (0.267) | 0.394 ** (0.104) | 0.612 ** (0.251) | 0.593 ** (0.308) | 0.162 ** (0.415) |
BDA | 0.028 ** (0.031) | 0.034 ** (0.072) | 0.042 ** (0.016) | 0.029 ** (0.026) | 0.016 * (0.021) |
Firm size | 0.08 (0.003) | 0.07 (0.004) | 0.05 (0.001) | 0.07 (0.003) | 0.08 (0.004) |
Time period | 0.05 (0.002) | 0.06 (0.006) | 0.08 (0.004) | 0.03 (0.005) | 0.02 (0.009) |
Leverage | 0.01 * (0.031) | 0.02 * (0.051) | 0.02 * (0.049) | 0.04 * (0.049) | 0.02 * (0.021) |
Cash flow | 0.016 * (0.049) | 0.03 * (0.041) | 0.11 * (0.083) | 0.12 * (0.015) | 0.18 * (0.019) |
R & D | 0.12 ** (0.040) | 0.15 ** (0.019) | 0.16 ** (0.014) | 0.03 * (0.019) | 0.03 * (0.040) |
Profitability | 0.29 ** (0.114) | 0.18 ** (0.106) | 0.22 ** (0.113) | 0.13 ** (0.102) | 0.16 ** (0.105) |
Moderating effect | |||||
COM | 0.428 ** (0.217) | 0.347 ** (0.018) | |||
BDA × COM | −0.082 (0.034) | −0.371 (0.047) | |||
DYM | 0.319 ** (0.266) | 0.402 ** (0.438) | |||
BDA× DYM | −0.172 ** (0.317) | −0.219 ** (0.510) | |||
MUN | 0.017 (0.410) | 0.083 (0.472) | |||
BDA × MUN | 0.062 (0.379) | 0.091 (0.416) | |||
Residual | −0.029 (0.041) | −0.034 (0.027) | −0.020 (0.039) | −0.015 (0.027) | −0.011 (0.092) |
Model information | |||||
Number of observations | 2408 | 2408 | 2408 | 2408 | 2408 |
R2 | 0.792 | 0.789 | 0.792 | 0.789 | 0.799 |
DV/CEP | (1) Winsorization | (2) Alternative Model |
---|---|---|
BDAt−1 | 0.027 ** (0.160) | 0.029 ** (0.183) |
BDAt−12 | − | 0.079 (0.261) |
Control variables | Included | Included |
Firm fixed effect | Yes | Yes |
Year fixed effect | Yes | Yes |
Number | 2408 | 2408 |
R2 | 0.781 | 0.799 |
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Alyahya, A.; Agag, G. Improving Corporate Environmental Performance Through Big Data Analytics Implementation: The Role of Industry Environment. Sustainability 2025, 17, 2928. https://doi.org/10.3390/su17072928
Alyahya A, Agag G. Improving Corporate Environmental Performance Through Big Data Analytics Implementation: The Role of Industry Environment. Sustainability. 2025; 17(7):2928. https://doi.org/10.3390/su17072928
Chicago/Turabian StyleAlyahya, Ahmed, and Gomaa Agag. 2025. "Improving Corporate Environmental Performance Through Big Data Analytics Implementation: The Role of Industry Environment" Sustainability 17, no. 7: 2928. https://doi.org/10.3390/su17072928
APA StyleAlyahya, A., & Agag, G. (2025). Improving Corporate Environmental Performance Through Big Data Analytics Implementation: The Role of Industry Environment. Sustainability, 17(7), 2928. https://doi.org/10.3390/su17072928