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Wheat, which is one of the major staple food grain crops in India, continues to depict occasional fluctuation in the prices though Union government has adopted administered price policy for wheat by intervening in its procurement at assured prices and distribution. Such fluctuations in prices are usually attributed to inefficient functioning of the agricultural markets. Since spatially separated markets also play an important role to determine efficiency of the agricultural markets, the study has used market integration as one of the tools to analyze the price transmission across the spatially separated markets to identify causes of price fluctuations and suggest ways to stabilize wheat prices.

The study utilizes monthly wholesale prices for January, 2006 to May, 2016 for dara wheat. First, the study employs augmented Dickey and Fuller (ADF), Phillips and Perron (PP) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests to check stationarity in wheat prices. Second, Johansen's cointegration test is applied to assess the integration of wholesale prices between selected pairs of wheat markets to determine long-run relationship among them. Third, Granger casualty test is used to find the direction of causality between the wheat market pairs. Finally, threshold vector error correction model (TVECM) and likelihood ratio (LR) tests are employed to examine long-run adjustment of prices towards the equilibrium in selected wheat markets.

Since wheat wholesale prices for the selected markets are found to be integrated of the order one, that is [I(1)], Johansen's test of cointegration is employed and its findings reveal that the selected wheat market pairs exhibit cointegration and show a long-run price association among themselves. There exists a bi-directional causality among the wheat market pairs. Since LR test is in favor of threshold model (except for Etawah–Delhi pair), one and two threshold models were also performed accordingly. Findings show that wholesale prices of wheat in Delhi markets remain higher than the prices of all other regional markets as regional markets are found to adjust their prices towards Delhi market. Distance of the wheat markets from each other is directly associated with threshold parameters, which are analogous to the transaction costs. Geographically dispersed wheat markets incorporate high transaction and vice versa.

The study argues that there is need to improve rural infrastructure and connectivity of the agricultural markets and remove market asymmetries through unified market regulating mechanisms across the states. This will enable price adjustment process from primary wholesale markets (in production regions) to the secondary wholesale markets (in scarcity regions) quickly.

The contribution of the study in the existing literature lies in the fact that there are no empirical evidences in the context of India that use price transmission as a tool of market integration among spatially separated wheat markets using TVCEM as this model examines role of transaction costs in efficient functioning of the agricultural markets.

Effective agricultural product price regulation policies depend on market integration and the degree of symmetry in the transmission of agricultural product price signals. This study analyzes the transmission and asymmetry of the price series between the Ouagadougou consumer market and assembly markets considering three primary cereal products in Burkina Faso.

This study applies the nonlinear autoregressive distributed lag (NARDL) econometric model, which is an asymmetric extension of the ARDL cointegration model. The price series examined covers the period extending from January 2005 to December 2020.

Our analysis provides novel insights regarding short- and long-term asymmetric effects in the transmission of price signals between assembly markets and the consumer market. We also determine that the effects of negative shocks are more persistent than those of positive shocks in several markets.

For markets that exhibit symmetrical responses of assembly market prices to consumer market prices, the results could reflect the continuous efforts of market players, particularly the government, to eliminate market failures and ensure the long-term efficiency of cereal markets. To this end, an agricultural market information system can have a crucial role in easing information access for all market players.

This study provides new evidence regarding the nature of the transmission and asymmetry of price information on primary cereal products in the largest markets in Burkina Faso. Applying the NARDL model makes it possible to simultaneously estimate short- and long-term asymmetry.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.

This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).

In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).

In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.

It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.

The underdevelopment of the financial sector could be one of the barriers to the deployment of renewable energies in developing countries. The purpose of this paper is therefore to analyse the effect of financial development in the deployment of renewable energies in sub-Saharan African countries.

The empirical analysis is based on a production approach and a cross-sectionally augmented autoregressive distributive lag error correction model estimate for 25 sub-Saharan African countries over the period 1990–2018. The augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators were used for the robustness analysis.

Two results emerge: financial development contributes positively to renewable energy deployment in sub-Saharan African countries in the short and long run; and fossil fuel dependence impedes significantly renewable energy deployment in the short and long run. The robustness analyses using the AMG and CCEMG methods confirm these results.

These results suggest the need for policies to support and strengthen the development of the financial sector to improve its ability to effectively finance investments in renewable energy technologies.

The originality of this paper lies in the fact that the analysis is based on a renewable energy production approach. Indeed, the level of renewable energy deployment is measured by the production and not the consumption of renewable energy, unlike other previous work. In addition, this research uses recent econometric estimation techniques that overcome the problems of cross-sectional dependence and slope heterogeneity.

Replication is a primary self-correction device in science. In this paper, we have two aims: to examine how and when the results of replications are used in management and organization research and to use the results of this examination to offer guidelines for improving the self-correction process.

Study 1 analyzes co-citation patterns for 135 original-replication pairs to assess the direct impact of replications, specifically examining how often and when a replication study is co-cited with its original. In Study 2, a similar design is employed to measure the indirect impact of replications by assessing how often and when a meta-analysis that includes a replication of the original study is co-cited with the original study.

Study 1 reveals, among other things, that a huge majority (92%) of sources that cite the original study fail to co-cite a replication study, thus calling into question the impact of replications in our field. Study 2 shows that the indirect impact of replications through meta-analyses is likewise minimal. However, our analyses also show that replications published in the same journal that carried the original study and authored by teams including the authors of the original study are more likely to be co-cited, and that articles in higher-ranking journals are more likely to co-cite replications.

We use our results to formulate recommendations that would streamline the self-correction process in management research at the author-, reviewer- and journal-level. Our recommendations would create incentives to make replication attempts more common, while also increasing the likelihood that these attempts are targeted at the most relevant original studies.

The main objective of the quantitative study is to ascertain the relationship between the circular economy (CE) and carbon emissions. And also, the study examines the threshold beyond which the quality of governance reduces carbon emissions.

The autoregressive distributed lag approach is employed for the econometrics analysis. The study employed quarterly data from 2006Q1 to 2017Q4 on Ghana.

The results indicated that, although the CE had a positive and significant effect on carbon emissions, the moderating term had an adverse and significant effect on carbon emissions. This result suggests that to mitigate carbon emissions, a robust and efficient quality of institutions should be sustained. Finally, the study also identified a quality of governance threshold of 1.155 beyond which a shift to a CE would result in a reduction in carbon emissions.

The study recommends that policymakers should initiate policies that would enhance quality governance.

The main contributions of the study are that the paper ascertained the threshold beyond which quality of governance assists circular economic practices to mitigate carbon emissions. Also, the study revealed that quality of governance is a catalyst to promote circular economic practices in reducing carbon emissions. Finally, the study ascertains the long-run effect of the variables of interest on carbon emissions.

While some countries have used debt to drive economic growth, the asymmetric effect on sub-Saharan African (SSA) countries has received little attention in the empirical literature. This paper therefore examines the asymmetric effect of external debts on economic growth.

The panel nonlinear autoregressive distributed lag (NARDL) approach was employed in the study for 29 sub-Saharan African countries from 1990 to 2021. The cross-sectional dependence test was used to determine the presence of cross-sectional dependence, while the second-generation panel unit root tests was used to examine the unit-root properties.

The empirical results show that external debt has an asymmetric effect on economic growth in both the short and long run. In the long run, a positive shock in external debts of 1% triggers an upturn in economic growth by 0.216% while a negative shock triggers 0.354% decline in economic growth. This implies that the negative shock of external debts has a much stronger impact on economic growth than the positive shock. In the short run, a positive shock in external debts by 1% triggers a decline in economic growth by 0.641%, while a negative shock of 1% triggers a fall in economic growth of 0.170%.

The paper used the NARDL model to examine the asymmetric impact of external debt on the economic growth of SSA countries, which has not been extensively studied. It is recommended that governments in the selected countries in sub-Saharan Africa should drive economic growth by promoting domestic revenue mobilization since external debts impede economic growth.

This study examines the impact of raising the ceiling value of Electronic Commerce Custom Declarations (ECCD) on Turkey's export performance processed via ECCD during the COVID-19 period.

This paper examines the impact of the pandemic conditions on Cross-Border Electronic Commerce (ECCD) exports from Turkey to 47 countries over 42 months before and during the pandemic. An empirical analysis using the Pooled Mean Group (PMG) and Mean Group (MG), Panel Autoregressive Distributed Lag (ARDL) approach was conducted to identify the factors affecting export flows.

The findings suggest that raising the ceiling of the ECCD trade is a vital factor in increasing exports. and this result is robust after controlling for pandemic conditions. On the other hand, although the COVID-19 shock mitigates the export volume of ECCD in the short run, it changes by reversal and increases the export level in the long run. Additionally, the number of COVID-19 cases and deaths in Turkey have a significant and negative impact on export flows in the short run, while they have a positive and significant effect in the long run.

The results of this study have practical implications for policymakers, emphasizing the potential and significance of Cross-Border E-Commerce (CBEC) trade.

The study is a pioneering effort in the literature of CBEC to explore how changes in the upper limit on customs declarations can affect export flows, taking into account the impact of the COVID-19 pandemic.