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This paper aims to examine the influence of financial inclusion (FI) on poverty, income inequality and financial stability from the perspective of public good (PG) theory in developing countries.

This study applies the fixed effects model (FEM), pooled ordinary least square (OLS) regression and generalized method of moment (GMM) across panal data of 69 developing countries from 2002 to 2020 inclusive.

Multiple regression analyses show that FI reduces poverty and income inequality while improving financial stability. Secondary enrolment ratio, GDP per capita, and trade openness reduce poverty and income inequality. However, a higher inflation rate increases poverty and income inequality while reducing financial stability. Finally, age dependency ratio and population do not affect poverty, income inequality or financial stability.

The regulators and policymakers in developing countries should raise the level of formal FI by expanding the size of the formal financial sector and improving the access of the large unbanked population to financial products/services. Improving FI enables the unbanked population to take over productive activities and ease consumption, which in turn complementing economic growth.

The increase in FI enables the developing countries to include the financially excluded population through formal financial products and services, which improve financial stability and eradicate poverty and income inequality in society. Thus, the FI enhances the social welfare of society.

This is the first study that examines the impact of FI poverty, income inequality and financial stability in the context of developing countries. This study contributes to the theoretical implications of the PG theory by examining the influence of FI on poverty, income inequality and financial stability in the context of developing countries.

This study aims to investigate the impact of extensive board diversity on firm performance from the perspective of resource-based view (RBV) theory in the context of Pakistan.

The analyses are made using a panel random-effects model and generalized method of moment (GMM) across 188 non-financial firms listed in the Pakistan Stock Exchange (PSX) over the period of 2009–2020. The robustness of findings is checked through alternative measurements of the variables and alternative estimation techniques.

The results show that board members' nationality, ethnicity and educational level diversities are significantly positively related to firm performance. In contrast, age and educational background diversities negatively affect firm performance. However, gender and tenure diversities have an insignificant relationship with firm performance.

This study is conducted in the context of Pakistani firms; thus, the findings may not be generalizable to other economies because different economies have different institutional settings and governance structures.

The policy-makers should encourage the inclusion of board members' nationality, ethnicity and educational level diversities having relevant educational backgrounds to improve firms' competitive performance. The suggested structure of the corporate board may improve firm performance by attracting multiple stakeholders and fulfilling their expectations.

The appointment of a director should be based on merit rather than on political connections or personnel relationships to improve social welfare and avoid their negative impact on firm competitive performance.

To the best of the authors' knowledge, this is the first study that investigates the impact of board diversity on firm accounting-based performance and market-based performance in the emerging economy of Pakistan. This study uses RBV theory to provide a unique corporate governance structure based on board diversity, particularly in Pakistan.

The aim of the study is to examine the impact of financial inclusion on poverty, income inequality and financial stability using panel data of 54 African countries.

To achieve this objective, the current study used multiple regressions across an unbalanced panel data of 54 African countries which are based on the four years mean value for the period 2001–2019.

The results show that financial inclusion (FI) is a valuable indicator; it reduces poverty, income inequality and improves financial stability.

The study invokes the attention of government and policymakers to build up a financially inclusive system which, in turn, leads to improve financial stability and lower poverty and income inequality. They should focus on quality and sustainable financial products and services in terms of financial inclusion to avoid dominant accounts and ensure consumer protection.

This adds to the scarce literature on the impact of financial inclusion on poverty, income inequality and financial stability in the context of African countries. The study contributes to the literature on the issue of financial inclusion and poverty, income inequality and financial stability by reconfirming (or otherwise) findings of previous studies.

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.

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.

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.

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.

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).

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.

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.

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.

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.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantage and high efficiency by customers because of the expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm’s success. Therefore, paying attention to customer feedback in supply chain and logistics processes has recently increased so that manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumer purchasing behaviour and to analyse the interrelationship among them to develop a framework for customer-centric RL. These features are studied based on website analysis of several mobile phone manufacturers. The special focus of this paper is on social media data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

A portfolio of mobile phone features that affect consumer’s mobile phone purchasing decisions has been taken from website analysis by several mobile phone manufacturers to achieve this objective. Then, interrelationships between the identified features have been established by using big data supplemented with interpretive structural modelling (ISM). Apart from that, cross-impact matrix multiplication, applied to classification analysis, was carried out to graphically represent these features based on their driving power and dependence.

During the study, it has been observed from the ISM that the chip (F5) is the most significant feature that affects customer’s buying behaviour; therefore, mobile phone manufacturers realize that this is to be addressed first.

The focus of this paper is on social media data (Twitter) so that experts can understand the interaction between mobile phone features that affect consumer’s decisions on mobile phone purchasing by using the results.

The firms’ adoption and improvement of big data analytics capabilities to improve economic and environmental performance have recently increased. This makes it important to discover the underlying mechanism influencing the association between big data analytics (BDA) and economic and environmental performance, which is missing in the existing literature. The present study discovers the indirect effect of green innovation (GI) and the moderating role of corporate green image (CgI) on the impact of BDA capabilities, including big data management capability (MC) and big data talent capability (TC), on economic and environmental performance.

A time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.

The empirical outcomes indicate that both BDA capabilities of firms significantly influence green innovation (GI), which significantly mediates the relationship between BDA and economic and environmental performance. Our findings also revealed that CgI strengthened the effect of GI on economic and environmental performance. The empirical evidence provides important theoretical and practical repercussions for manufacturing SMEs and policymakers.

This study contributes to the literature on BDA by empirically exploring the effects of MC and TC on improving the EcP and EnP of manufacturing firms. It does so through the indirect impact of GIs and the moderating effect of CgI, thereby extending the Dynamic capabilities view (DCV) paradigm.

This study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes.

The author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions.

The author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme “Social Media information in Supply chain.”

The results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes.

This study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain.

This study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.

Nowadays, many firms are finding ways to enhance the survivability of sustainable supply chains (SUSSCs). The present study aims to develop a model for the SUSSCs of small and medium enterprises (SMEs) during the COVID-19 pandemic.

With the help of exhaustive literature review, constructs and items are identified to collect the responses from different SMEs. A total of 278 complete responses are received and 6 hypotheses are developed. Hypotheses testing have been done using structural equation modeling (SEM).

Major constructs identified for the study are supply chain (SC) performance measurement under uncertainty (SPMU), supply chain cooperation (SCCO), supply chain positioning (SCP), supply chain administration (SCA), supply chain feasibility (SCF) and the SUSSCs. From statistical analysis of the data collected, it can be concluded that the considered latent variables contribute significantly towardsthe model fit.

The present study contributes to the existing literature on disruptions and survivability. The study can be further carried out in context to different countries and sectors to generalize the findings.

The research findings will be fruitful for SMEs and other organizations in developing strategies to improve survivability during uncertain business environments.

The study has developed a model that shows that the identified latent variables and their indicators contribute significantly toward the dependent variable, i.e. survivability. It contributes significantly in bridging the research gaps existing in context to the survivability of SMEs.