Yaoteng Zhao, Supat Chupradit, Marria Hassan, Sadaf Soudagar, Alaa Mohamd Shoukry and Jameel Khader
Recently, the financial sector has faced significant challenges regarding the market competition, its technical efficiency and risk factors around the globe and gain recent…
Abstract
Purpose
Recently, the financial sector has faced significant challenges regarding the market competition, its technical efficiency and risk factors around the globe and gain recent researchers' intentions. Thus, the present study aims to examine the impact of technical efficiency, market competition and risk in banking performance in Group of Twenty (G20) countries.
Design/methodology/approach
Data have been obtained from the World Development Indicator from 2008 to 2019. For analysis purpose, random effect model and generalized method of moments (GMMs) have been executed using Stata.
Findings
The results revealed that market competition and banks' capital efficiency have a positive impact on banking performance, while banks' lending efficiency and non-performing loans have a negative association with the banking sector performance of G20 countries. These outcomes provide the guidelines to the regulators that they should formulate the effective policies related to the lending practices and non-performing loans that could improve the banking sector performance worldwide.
Research limitations/implications
The study has examined only three economic factors like the technical efficiency rate, market competition and risk element, and their influences on banking institutions' operational and economic performance. But the analysis has proved that except these factors, several factors affect banking institutions' operational and economic performance. Thus, future scholars recommend they analyze all the banking sector areas, pick more factors and enlighten their operational and economic performance influences. Moreover, the author of this article has chosen a particular source for collecting data to meet his study's objective. Only a single piece of software has been applied to analyze data; thus, the data collected for this paper may be incomplete, lack accuracy and reliability. Therefore, the future authors are recommended to use multiple sources to collect data and its analysis to ensure the comprehension, completeness and accuracy.
Originality/value
Last but not least, this study with the evidences from the banking sector of G20 countries tries to show on the banking management how the risk element matters in the banking sector in an economy. It makes it clear in which areas the banking institutions may be exposed to the risks, and how much sever different kinds of risks may be. Thus, it motivates the management to set a body of persons within the organization to monitor the risks, to try to avoid them and to overcome the problems created by these risks events.
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Zhao Yaoteng and Li Xin
The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.
Abstract
Purpose
The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.
Design/methodology/approach
From the perspective of big data, this paper uses quantitative and qualitative analysis methods to judge the correlation among green finance, environmental supervision and financial supervision. Green finance gives the entropy method to calculate the score of green finance and environmental regulation, and establishes the spatial lag model under the double fixed effects of time and space.
Findings
Spatial autocorrelation test shows that economic spatial weight matrix has obvious spatial effect on green innovation. Through the model selection test, the space lag model with fixed time and space is selected. The regression coefficients of green finance, environmental regulation and their interaction are 0.1598, 0.0541 and 0.1763, respectively, which significantly promote green innovation. The regression coefficients of openness, higher education level and per capita GDP are 0.0361, 0.0819 and 0.0686, respectively, which can significantly promote green innovation.
Originality/value
In view of the current situation of large-scale application of big data technology in green innovation of domestic energy-saving and environmental protection enterprises, this paper establishes a fixed time lag evaluation model of green innovation. M-test statistics show that the original hypothesis with no obvious spatial effect is rejected.
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This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the…
Abstract
Purpose
This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).
Design/methodology/approach
To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.
Findings
The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.
Research limitations/implications
One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.
Originality/value
This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.
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This study attempts to examine the relationship between reliable big and cloud data analytics capabilities (RB&CDACs) and comparative advantages (CA) of manufacturing firms (MFs…
Abstract
Purpose
This study attempts to examine the relationship between reliable big and cloud data analytics capabilities (RB&CDACs) and comparative advantages (CA) of manufacturing firms (MFs) in the Middle East region as developing countries using green product innovation (GPI) and green process innovations (GPrI) mediating factors, further assess the role of data-driven competitive sustainability factor as a moderated factor.
Design/methodology/approach
436 useable online surveys were analyzed using the quantitative approach for the data-gathering process, applying structural equation modeling in the Smart-PLS program as an analysis tool. The sample unit for analysis included all middle- and senior-level managers and employees within MFs. The authors performed convergent validity and discriminant validity tests, bootstrapping also was applied. The authors included GPI and GPrI as mediating factors while using data-driven competitive sustainability as a moderated factor.
Findings
The findings of this study indicated that there is a positive significant effect in the relationship between reliable big and cloud data analytics capabilities and comparative advantages, which is supported by the formulated hypothesis. Furthermore, the findings confirmed that there was a positive and significant effect through the mediating factors (i.e. GPI and GPrI) on comparative advantage, additionally, it confirmed and supported that the moderating factor represented by data-driven competitive advantage suitability has significant effect as well.
Research limitations/implications
This study has some limitations represented by using only one type of methodological approach (i.e. quantitative), further, it was conducted on only Asian countries in the Middle East region.
Originality/value
This piece of work improved the proposed conceptual research model and included several factors such as reliable big and cloud data analytics capabilities, comparative advantage, data-driven competitive sustainability, GPI and GPrI. This research offered new and valuable information and findings for managers, practitioners and decision-makers in the MFs in the Middle East region as a road map and gaudiness for the importance to apply these factors in their firms for enhancing the comparative advantages in their firms. Further, this research fills the gap in SCM literature and makes a bridge of knowledge and contribution to the existence of previous studies.
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The purpose of this study is to investigate the impact of big data (BD) analytics capabilities (BDACs) on green supply chain integration (GSCI) and green innovation (GI) in the…
Abstract
Purpose
The purpose of this study is to investigate the impact of big data (BD) analytics capabilities (BDACs) on green supply chain integration (GSCI) and green innovation (GI) in the context of a developing country, Jordan. In addition, the mediating effect of GSCI on the relationship between BDAC and GI is investigated.
Design/methodology/approach
Data collection was carried out through a survey with 300 respondents from food and beverages manufacturing firms located in Jordan. Partial least squares-structural equation modeling (PLS-SEM) technique was applied to analyze the collected data. Natural resource-based view (NRBV) theory was the adopted theoretical lens for this study.
Findings
The results revealed that BDAC positively and significantly affects both GSCI and GI. In addition, the results demonstrated that GSCI positively and significantly affects GI. Further, it is also found that GSCI positively and significantly mediates the relationship between BDAC and GI.
Originality/value
This study developed a theoretical and empirical model to investigate the relationship between BDAC, GSCI and GI. This study offers new theoretical and managerial contributions that add value to the supply chain (SC) management literature by testing the mediation model in food and beverages manufacturing firms located in Jordan.
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This study explores the connection between big data analytics capabilities and the competitive advantage of the manufacturing sector in Jordan through the mediating role of green…
Abstract
Purpose
This study explores the connection between big data analytics capabilities and the competitive advantage of the manufacturing sector in Jordan through the mediating role of green radical innovation and green incremental innovation as well as the moderating role of a data-driven culture.
Design/methodology/approach
For the purpose of this study, 356 questionnaires were analysed. Convergent validity and discriminant validity tests were performed through structural equation modelling in the Smart-PLS programme, and the data reliability was confirmed. A bootstrapping technique was used to analyse the data. The mediating effect for green radical and green incremental innovation and the moderating effect for data-driven culture were performed.
Findings
The empirical results showed that the proposed moderated-mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between big data analytics capabilities and the competitive advantage as well as a mediating effect of green radical innovation and green incremental innovation. It was confirmed that there is a moderating relationship for data-driven culture between green radical innovation, green incremental innovation and competitive advantage.
Research limitations/implications
This cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalization of its results, and the results are limited to one country.
Originality/value
This research developed a theoretical model to incorporate big data analytics capabilities, green radical innovation, green incremental innovation, data-driven culture, and competitive advantage. This study provides new findings that bridge the existing research gap in the literature by testing the moderated mediation model with a focus on the organizational benefits of big data analytics capabilities to improve levels of green innovation and competitive advantage in the Jordanian manufacturing sector.
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This study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation and…
Abstract
Purpose
This study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation and moderating effect of technological intensity.
Design/methodology/approach
This study is based on primary data that were collected from the food and beverages manufacturing sector operating in Jordan. A total of 420 samples were used for the final data analysis. Data analysis was performed via structural equation modeling (SEM) using SmartPLS 3.3.9.
Findings
The results of the data analysis supported a positive relationship between big data analytics capabilities and the green supply chain performance as well as a mediating effect of green innovation. It was confirmed that technological intensity moderated the relationship of green innovation on green supply chain performance.
Research limitations/implications
The study faced many limitations such as the method of collecting primary data, which relied on a questionnaire only and the use of cross-sectional data, as well as studying one context and in one country.
Practical implications
The findings can guide managers and policymakers in the Jordanian food and beverage manufacturing sector on how to manage organizational capabilities related to big data analytics to enhance green supply chain performance and improve green innovation in these firms.
Originality/value
This study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, green innovation, technological intensity and green supply chain performance. This study offers new theoretical and managerial contributions that add value to the supply chain management and innovation literature by testing the moderated mediation model of these constructs in the food and beverages manufacturing sector in Jordan.
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Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However…
Abstract
Purpose
Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London.
Design/methodology/approach
This study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)).
Findings
GB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework.
Originality/value
Industry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully.
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The objective of this paper is to examine the impact of big data analytics capabilities (BDAC) on green radical supply chain innovation (GRSCI), green incremental supply chain…
Abstract
Purpose
The objective of this paper is to examine the impact of big data analytics capabilities (BDAC) on green radical supply chain innovation (GRSCI), green incremental supply chain innovation (GISCI), and green supply chain performance (GSCP) in the context of a developing country, Jordan. In addition, the mediating effect of GRSCI and GISCI on the relationship between BDAC and GSCP is tested.
Design/methodology/approach
Data collection is carried out through a survey with 303 respondents from manufacturing firms located in Jordan. Partial least squares-structural equation modelling approach is applied to analyse the collected data. Resource-based view and natural resource-based view theory form the adopted theoretical lens for this study.
Findings
The results reveal that BDAC positively and significantly affects GRSCI, GISCI, and GSCP. In addition, the results demonstrate that GRSCI and GISCI positively and significantly affect GSCP. Further, it is also found that GRSCI and GISCI positively and significantly mediate the relationship between BDAC and GSCP.
Originality/value
This study's author develops a theoretical and empirical model to investigate the relationship among BDAC, GRSCI, GISCI, and GSCP. This study offers new theoretical and managerial contributions that add value to the supply chain management literature by testing the mediation model in manufacturing firms located in Jordan.