Table 1.
The Summary of Relevant Green Banking Literature
| SL NO. | Author (s) and Year | Theoretical Basis | Sample Size, Type, and Country, Data Collection Tool, Unit of Analysis) | Outcome Variable and Context | Study Variables | Significant Results |
|---|---|---|---|---|---|---|
| 01 | Chen et al (2022)3 | Legitimacy theory | 322, banking employees of private commercial banks (PCBs), Bangladesh, cross-sectional sample, IBM SPSS (version 22.0) and AMOS (version 23.0) | Bank Environmental Performance (BEP) | Banks Employee Related Practices, Banks Operation Related Practices, Banks Customer Related Practices, Banks Policy Related Practices, Green Financing, Bank Environmental Performance (BEP) |
Direct predictors of Green Financing: Banks Employee Related Practices (+), Banks Operation Related Practices (+), Banks Customer Related Practices (-), Banks Policy Related Practices (+) Direct predictors of Bank Environmental Performance (BEP): Green Financing (+), Banks Employee Related Practices (-), Banks Operation Related Practices (+), Banks Customer Related Practices (-), Banks Policy Related Practices (+) Demographics: Female vs males |
| 02 | Ding et al (2022)4 | Organizational Learning Theory, Theory of Reasonable Action (TRA), Self-Efficacy Theory |
65 teams that belong to green finance industries, China, field survey and online questionnaires, SPSS22.0 | Team Effectiveness | Innovative Climate, Knowledge Sharing, Knowledge Heterogeneity, Team Effectiveness |
Direct predictors of Knowledge Sharing: Innovative Climate (+) Direct predictors of Team Effectiveness: Knowledge Sharing (+), Innovative Climate (+) Indirect predictors of Team Effectiveness: Innovative Climate through Knowledge Sharing (+) Moderating role of Knowledge Heterogeneity: Between Knowledge Sharing and Team Effectiveness (+) Demographics: Female vs males |
| 03 | Desalegn et al (2022)17 | Explanatory research design and a Quantitative research approach, Hungary, voluntary questionnaire survey, purposive sampling techniques, Vector Autoregressive Analysis |
Green Finance | Broad money supply, lending Investment rate, Outward foreign direct investment, Inward foreign direct investment, Domestic Investment, Green House Gas | Direct predictors of Green Finance: lending Investment rate (+), Broad money supply (+), Inward foreign direct investment (+), Outward foreign direct investment (+), Domestic Investment (+), Green House Gas (+) | |
| 04 | Li & Yang (2022)18 | 31 provinces of China, Regression | Corporate Technological innovation | Green finance, Corporate Technological innovation, CSR, |
Direct predictors of Corporate Technological innovation: Green finance (+), CSR (+) Indirect predictors of Corporate Technological innovation: Green finance Through CSR (+) |
|
| 05 | Fang & Shao et al (2022)7 | re-measurement of the green finance, China, Durbin model |
Green Technology Innovation | Command and control environmental regulation, Market incentive environmental regulation, Green finance, |
Direct predictors of Green Technology Innovation: Command and control environmental regulation (+), Market incentive environmental regulation (+), Green finance (+), Moderating role of Green finance: Between Command and control environmental regulation and Green Technology Innovation (+); Between Market incentive environmental regulation and Green Technology Innovation (+) Direct predictors of Neighborhood Green Technology Innovation: “Command and control” environmental regulation and “market incentive” environmental regulation (+); Green finance (+) |
|
| 06 | Guang-Wen & Siddik (2022)13 | Legitimacy Theory | 388, Bankers of private commercial banks (PCBs), Bangladesh, Non-Probabilistic convenience sampling method, AMOS |
Environmental Performance of Bankers | Economic, Social, Environmental, CSR Activities, Environmental Performance |
Direct predictors of Environmental Performance: CSR Activities (+), Economic (+), Social (+), Environmental (+) Demographics: Female vs males |
| 07 | Yan et al (2022)12 | 351 employees of banking institutions, Bangladesh, convenience sampling method, IBM’s SPSS | Sustainability Performance (SP) of employees | Fintech Adoption, Green Finance, Green Innovation |
Direct predictors of Sustainability Performance: Fintech Adoption (+), Green Finance (+), Green Innovation (+) Direct predictors of Green Finance: Fintech Adoption (+) Direct predictors of Green Innovation: Fintech Adoption (+) Indirect predictors of Sustainability Performance: Fintech Adoption through Green Finance (+), Fintech Adoption through Green Innovation (+) Demographics: Female vs males |
|
| 08 | Ye et al (2022)19 | 30 provincial administrative regions, China | Green Development | Green Finance Green Technological Innovation, Special Interaction Effect, Green Development | Direct predictors of Green Development: Green Finance (+), Green Technological Innovation (+), Special Interaction Effect (+) | |
| 09 | Zeng et al (2022)96 | 639, Enterprises, China, | Haze Pollution | Green Finance, Haze Pollution, Enterprise Innovation Output, Enterprise Innovation Input, |
Direct predictors of Haze Pollution: Green Finance (+) Direct predictors of Enterprise Innovation Output: Green Finance (+) Direct predictors of Enterprise Innovation Input: Green Finance (+) Indirect predictors of Haze Pollution: Green Finance through Enterprise Innovation Output (+), Green Finance through Enterprise Innovation Input (-) Demographics: Enterprises |
|
| 10 | Zhang et al (2022)6 | 352, Employee of Private commercial banks, Bangladesh, convenience sampling method, SPSS 22.0 and AMOS 23.0 | Bank’s environmental performance of Employee of Private commercial banks | Green banking activities, Sources of green financing, Bank’s environmental performance |
Direct predictors of Bank’s environmental performance: Green banking activities (+) Direct predictors of Sources of green financing: Green banking activities (+) Direct predictors of Bank’s environmental performance: Sources of green financing (+) Indirect predictors of Bank’s environmental performance: Green banking activities through Sources of green financing (+) Demographics: Female vs males |
|
| 11 | Wang et al (2021)97 | 30 provincial panel, China, Regression | Green development | Green finance pilot Zone, Industrial structural upgrade, Technological innovation ability |
Direct predictors of Green development: Green finance pilot Zone (+), Industrial structural upgrade (+) Direct predictors of Industrial structural upgrade: Green finance pilot Zone (+) Direct predictors of Technological innovation ability: Green finance pilot Zone (+), Green development (+) |
|
| 12 | Rehman et al (2021)1 | Socially Responsible Investment (SRI) | 200 employees of retail banks at managerial level, Pakistan, Survey questionnaire | Green banking practices of employees of retail banks at managerial level | Policy, Operations, Investments, Green banking practices |
Direct predictors of Green banking practices adoption: Bank polices for green environment (+) Direct predictors of bank daily operations: Adoption of green banking practices (+) Direct predictors of Green banking practices: Banks investment decision (+) Demographics: Female vs males |
| 13 | Zheng et al (2021a)98 | 296, Bankers of Private commercial banks, Bangladesh, Nonprobability sampling method, SPSS 22.0 and AMOS 23.0. | Bankers’ perception of Green Finance |
Economic Dimension, Social Dimension, Environmental Dimension, Sources of Group Financing, Bankers’ perception of Green Finance |
Direct predictors of Economic Dimension: Bankers’ perception of Green Finance (+) Direct predictors of Social Dimension: Bankers’ perception of Green Finance (+) Direct predictors of Environmental Dimension: Bankers’ perception of Green Finance (+) Direct predictors of Sources of Group Financing: Bankers’ perception of Green Finance (+) Demographics: Female vs males |
|
| 14 | Zheng et al (2021b)99 | 302, Bankers, Bangladesh, convenience sampling (non-probabilistic) method, SPSS 22.0 and AMOS 23.0 | Sustainability Performance of Bankers | Economic Dimensions, Social Dimensions, Environmental Dimensions, Sustainability Performance |
Direct predictors of Social Dimensions: Economic Dimensions (+) Direct predictors of Environmental Dimensions: Economic Dimensions (+), Social Dimensions (+) Direct predictors of Sustainability Performance: Economic Dimensions (+), Social Dimensions (+), Environmental Dimensions (+) Demographics: Female vs males |
|
| 15 | Taneja & Ali (2020)22 | Theory of Planned Behavior | 440 respondents who use at least one of the various digital banking services, India, cross-sectional sample, self-administered questionnaires | Behavioral Intention | Trust, attitude, Perceived Environmental Outcomes, Environmental Consciousness, Perceived Behavioral Control, Subjective Norms, Behavioral Intention |
Direct predictors of Behavioral Intention: Attitude (+), Subjective Norms (+), Perceived Behavioral Control (+), Environmental Consciousness (-), Perceived Environmental Outcomes (+), Direct predictors of Attitude: Trust (+), Environmental Consciousness (+) Direct predictors of Perceived Environmental Outcomes: Environmental Consciousness (+) Direct predictors of Trust: Perceived Environmental Outcomes (+) Demographics: Female vs males |
| 16 | Burhanudin et al (2020)21 | Self-regulation Theory & Theory of Planned Behavior | 313 respondents in commercial banking customers, Indonesia, | Intention to use green Banking Services | Guilt, Attitude towards Green Banking, Perceived Consumer Effectiveness, Negative Word-of-Mouth, Intention to use green Banking Services |
Direct predictors of Attitude towards Green Banking: Guilt (-) Direct predictors of Perceived Consumer Effectiveness: Guilt (+) Direct predictors of Perceived Negative Word-of-Mouth: Guilt (+), Perceived Consumer Effectiveness (+) Direct predictors of Attitude towards Green Banking: Perceived Consumer Effectiveness (+) Direct predictors of Intention to use green Banking Services: Attitude towards Green Banking (-), Perceived Consumer Effectiveness (-), Negative Word-of-Mouth (+) Indirect predictors of Intention to use green Banking Services: Perceived Consumer Effectiveness through Attitude towards Green Banking (-); Negative Word-of-Mouth through Perceived Consumer Effectiveness (+) Demographics: Female vs males |
| 17 | Sun et al (2020)11 | Theory Of Social Identity | 489, 18 years old with a bank account, Pakistan, Multi-stage sampling method, Correlations | Green Customer Loyalty | Corporate Social Responsibility, Green Banking, Co-creation, Green Consumer Loyalty |
Direct predictors of Co-creation: Corporate Social Responsibility (+) Direct predictors of Consumer Loyalty: Co-creation (+), Corporate Social Responsibility (+) Indirect predictors of Consumer Loyalty: Corporate Social Responsibility through Co-creation (+) Moderating Role of Green Banking: The indirect relationship of CSR and green consumer loyalty through co-creation (+) Demographics: Female vs males |
| 18 | Ibe-enwo et al (2019)20 | Socially Responsible Investment (SRI) | 850 customers of the retail banking sector, North Cyprus, Turkey, quantitative survey | Bank Loyalty of customers of the retail banking sector | Green Banking practice, Green Image, Bank Trust, Bank Loyalty |
Direct predictors of Green Image: Green Banking practice (+) Direct predictors of Bank Trust: Green Banking practice (+), Green Image (+) Direct predictors of Bank Loyalty: Green Banking practice (+), Green Image 5(+) Bank Trust (-) Indirect predictors of Bank Loyalty: Green Banking practice through Green Image (+); Green Banking practice through Green Trust (-) Demographics: Female vs males |
| 19 | Bose et al (2017)23 | Institutional Theory | 205 Bank’s Annual Report, Bangladesh, | Green banking disclosure index (GBDI) | Issuance of the central bank’s guidelines, Imitation behavior over the years, Routine process over time, Board size, Independence, Institutional ownership |
Direct predictors of Green banking disclosure: Issuance of the central bank’s Guidelines (+), Board size (+), Independence (+), Institutional ownership (+) Direct predictors of Imitation behavior over the years: Green banking disclosure (+) Direct predictors of Routine process over time: Green banking disclosure (+) |
| 20 | Xu et al (2020)100 | Neoclassical Theory | 62,051, Comprehensive Meta-Analysis Software (CMA) 2.0 |
Enterprise Green Performance | Green finance, Green credit, Green investment, Green subsidy, Green bond, Environmental Performance, Innovation Performance, Sustainability Performance |
Direct predictors of Enterprise Green Performance: Green finance (+), Green credit (+), Green investment (+), Green subsidy (+), Green bond (+) Direct predictors of Environmental Performance: Green finance (+) Direct predictors of Performance Performance: Green finance (+) Direct predictors of Sustainability Performance: Green finance (+) Moderating role of Profitability of enterprise: Between Green Finance and Green Performance (-) Moderating role of type of enterprises: Between Green Finance and Green Performance (+) Moderating role of Region: Between Green Finance and Green Performance (+) |