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This study aims to investigate the influence of systemic banking crises, currency crises and global financial crisis on the relationship between export and economic growth in China by using the annual time series data from the period of 1972 to 2014.

The Johansen and Jeuuselius’ cointegration, auto regressive distributed lag bound testing cointegration, Gregory and Hansen’s cointegration and pooled ordinary least square techniques with error correction model have been used.

Results indicate the positive and significant effect of export of goods and services on economic growth in both long and short run, whereas the negative influence of systemic banking crises and currency crises over economic growth is observed. It is also concluded that the impact of export of goods and service on economic growth becomes insignificant in the presence of systemic banking crises and currency crises. The currency crises effect the influence of export on economic growth to a higher extent compared to systemic banking crises. Surprisingly, the export in the period of global financial crises has a positive and significant influence over economic growth in China, which conclude that the global financial crises did not drastically affect the export-growth nexus.

This paper makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effects of systemic banking crises and currency crises on the relationship of export and economic growth by using long-time series data and applying more rigorous econometric techniques.

Given the limited literature on depression as a contributing factor to compulsive social media use, the present research examines the role of perceived depressive mood (PDM) in developing compulsive social media use behavior. The authors also identify and hypothesize channels such as contingent self-esteem (CSE), social interaction anxiety (SIA) and fear of negative evaluation (FNE), which may explain how PDM affects compulsive social media use.

The research model was empirically tested with a survey of 367 Chinese university students using structural equation modeling by drawing on the escape and self-presentation lenses.

The findings indicate that PDM contributes to compulsive social media use behavior both directly and indirectly through CSE. Furthermore, the impact of CSE on compulsive social media use is mediated by the FNE, whereas SIA fails to mediate this effect.

The results can advance the authors’ knowledge of the role and process by which depressive mood impacts compulsive social media use. These findings may add insights into psychological treatment and help in, for example, developing counseling programs or coping strategies for depressed people to protect them from using social media excessively.

This research identifies the pathway mechanism between PDM and compulsive use of social media. It also increases the understanding of how CSE and social interaction deficiencies contribute to compulsive social media usage (CSMU).

The influence of website quality on online compulsive buying behavior (OCBB) in the context of online shopping based on the usage of a credit card (UCC) and online impulsive buying behavior (OIBB) was investigated in this study.

The authors used a research model to examine the relationships between the study components as per the prescription. For this investigation, the authors used an online survey form to obtain primary data from 350 respondents on social media. A covariance-based structural equation modeling approach was used to evaluate the structural research model and data.

The findings reveal that the quality of online shopping websites positively affects consumers' UCC and OIBB, and these in turn positively influence their OCBB.

The study emphasized impacting elements on consumer behavior and gave advice for future research based on the results. Using several dimensions of website quality, this study bridges the knowledge gap between UCC, OIBB and OCBB.

Based on UCC and OIBB, the authors developed a new model to investigate the link between website quality and OCBB. To the best of the authors' knowledge, it is the first experimental result that assesses the impact of website quality on OCBB.

In this study, the authors evaluate seven calendar anomalies’–the day of the week, weekend, the month of the year, January, the turn of the month (TOM), Ramadan and Eid festivals–effects in both the conventional and Islamic stock indices of Bangladesh. Also, the authors examine whether these anomalies differ between the two indices.

The authors select the Dhaka Stock Exchange (DSE) Broad Index (DSEX) and the DSEX Shariah Index (DSES) of the DSE as representatives of the conventional and Islamic stock indices respectively. To carry out the investigation, the authors employ the generalized autoregressive conditional heteroskedasticity (GARCH) typed models from January 25, 2011, to March 25, 2020.

The study’s results indicate the presence of all these calendar anomalies in either conventional or Islamic indices or both, except for the Ramadan effect. Some significant differences in the anomalies between the two indices (excluding the Ramadan effect) are detected in both return and volatility, with the differences being somewhat more pronounced in volatility. The existence of these calendar anomalies argues against the efficient market hypothesis of the stock markets of Bangladesh.

The study’s results can benefit investors and portfolio managers to comprehend different market anomalies and make investment strategies to beat the market for abnormal gains. Foreign investors can also be benefited from cross-border diversifications with DSE.

To the authors’ knowledge, first the calendar anomalies in the context of both conventional and Islamic stock indices for comparison purposes are evaluated, which is the novel contribution of this study. Unlike previous studies, the authors have explored seven calendar anomalies in the Bangladesh stock market's context with different indices and data sets. Importantly, no study in Bangladesh has analyzed calendar anomalies as comprehensively as the authors’.

The purpose of this study is to explore whether different board diversity attributes (corporate governance aspect) can be used to predict financial distress. This study also aims to identify what type of prediction models are more applicable to capture board diversity along with conventional predictors.

This study used Chinese A-listed companies during 2007–2016. Board diversity dimensions of gender, age, education, expertise and independence are categorized into three broad categories; relation-oriented diversity (age and gender), task-oriented diversity (expertise and education) and structural diversity (independence). The data is divided into test and validation sets. Six statistical and machine learning models that included logistic regression, dynamic hazard, K-nearest neighbor, random forest (RF), bagging and boosting were compared on Type I errors, Type II errors, accuracy and area under the curve.

The results indicate that board diversity attributes can significantly predict the financial distress of firms. Overall, the machine learning models perform better and the best model in terms of Type I error and accuracy is RF.

This study not only highlights symptoms but also causes of financial distress, which are deeply rooted in weak corporate governance. The result of the study can be used in future credit risk assessment by incorporating board diversity attributes. The study has implications for academicians, practitioners and nomination committees.

To the best of the authors’ knowledge, this study is the first to comprehensively investigate how different attributes of diversity can predict financial distress in Chinese firms. Further, this study also explores, which financial distress prediction models can show better predictive power.

The purpose of this paper is to study the role of institutions (including civil law origin), financial deepening and degree of regime authority on growth rates in the Middle East and North Africa region.

This paper examines the implications of industrial firm-related and national factors for the determinants of economic growth using panel data through a fixed effect model.

The results reveal that English civil law origin and the establishment of the rule of law work with the development of financial institutions to increase economic growth in these economies; however, the democratization of the political institutions and foreign direct investment do not assist financial development in promoting economic growth.

Data covered is limited to four years.

The findings emphasize the prominence of overcoming institutional weaknesses and establishing transparent public policy governing businesses as a pre-requisite for successful universal integration in developing countries.

This paper contributes to the literature on the relationship between finance and economic growth in two aspects. First, the authors focus on the contribution of the institutional setting and its interaction with the financial development and how this affects economic growth of the manufacturing firms. Second, the authors explore the relationship between the role of institutions, governance, the country civil law origin and the economic growth.

Compulsive buying continues to be a maladaptive behavior that draws the attention of both scholars and marketers. The present study aims to investigate the determinants of compulsive buying, which are conceptualized as impulsive and obsessive–compulsive buying, and the mediation effect of brand attachment.

Using purposive sampling, a self-administered questionnaire was completed by 600 young consumers in Malaysia. Partial least squares structural equation modeling was used to test the hypothesized relationships.

The results show that materialism, utilitarian value, and brand attachment are positively related to impulsive buying, while materialism, hedonic value, and brand attachment have a positive effect on obsessive–compulsive buying. In addition, brand attachment is found to mediate the effect of materialism and utilitarian value on both compulsive buying.

The study provides new insights into brand management literature by examining the predictors of impulsive and obsessive–compulsive buying. Moreover, brand attachment is found to be a significant mechanism that induces negative buying behavior. However, due to the growth of online shopping, future research should consider different types of retailers to provide a more comprehensive understanding of the subject matter in the modern business landscape.

Being one of the few studies to address both impulsive and obsessive–compulsive buying behaviors among consumers, this study highlights the essential role of brand attachment as a mediator in the contemporary setting. Moreover, the interrelationships between self-congruence, materialism, hedonic value, utilitarian value, brand attachment, and compulsive buying behavior are examined in a holistic manner.

Governance quality has been a dominant factor to formulate policies for the development of financial institutions in the world. Therefore, this study aims to explore the impact of governance quality on financial institutions along with globalization in the case of Pakistan.

Time series data from 1996 to 2018 are considered for analysis. The NG-Perron is applied to check the order of integration. In addition, Kim and Perron (2009) structural break unit root test is used to identify break years. The autoregressive distributive lags (ARDL) bound testing approach is used to detect the long-run association among governance quality, financial institutions and globalization.

The results of unit root analysis show that all series are stationary at a different level of integration, I(0)/I(1). However, the long-run association is detected in the presence of break years. The authors find a positive impact of governance quality to determine financial institutions in the long-short-run. Similarly, globalization also enhances financial institutions but only in long run.

This study fills the gap in the economic literature by exploring the linkages between the financial institution and disaggregated governance indicators in the case of Pakistan. Moreover, a role of structural break is also captured during analysis. This study also opens some new insights for policymaking.

The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method.

Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks.

MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches.

The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry.

Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.