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The purpose of this study is to investigate the relationship between organizational learning (OL) and business sustainability (BS) and to carry out its empirical examination.

Extensive literature research was carried out. Then, an empirical study was conducted in 694 Polish and Danish companies. Two phenomena related to OL were adopted: OL processes and organizational learning capability (OLC). BS was examined through the concept of sustainable performance (SP). Research models were tested using structural equation modeling.

The empirical studies have shown a positive, statistically significant relationship between OL and BS. The research supports the view that the intensification of the OL processes is substantial for BS, whereas the OLC concept is less relevant to the development of BS. The effect of OL on total SP was stronger than on any SP dimension. OL supports the synergies of the results obtained by the organization for the benefit of BS.

The model verification is based on the samples from two countries, and, therefore, the hypothesis requires further verification in different business contexts. In addition, there are different factors influencing BS, which have not been included in the research and should be analyzed in the future.

An in-depth, critical literature analysis shows that the theoretical foundation of the role of OL in shaping BS is fragmented and poorly empirically verified. The value of this paper is the presentation of large-scale empirical studies comparing the relationship between BS and two phenomena: OLC and the OL processes. The results obtained in the course of the research open up new research directions with respect to both the relationship between OL and BS as well as between OL and organizational performance.

This paper aims to analyze the influence of organizational learning (OL) and service innovation (SI) on organizational performance of knowledge-intensive business services (KIBS) and examine the mediating role of SI.

Hypotheses were tested using the theoretical OL model of knowledge acquisition, distribution, interpretation and organizational memory (Huber, 1991; Lopez, Peon, & Ordas, 2005; Jiménez-Jiménez & Sanz-Valle, 2011), using structural equation modeling partial least squares analysis of a survey data set of Brazilian architectural firms.

Findings suggest that OL is significantly linked to SI and so is SI to organizational performance. However, neither the direct relationship between OL and organizational performance could be verified, nor the mediating effect of SI.

These results can offer KIBS managers insights that suggest that OL alone does not guarantee a significant impact in organizational performance, but it is a starting point for achieving SIs, that lead to performance improvement and competitive advantages.

This paper contributes to the knowledge production in the following ways: to the understanding of the relationship between OL and SI and its effect on organizational performance, traditionally overlooked in the literature; to the study of SIs, considering the importance of the service sector; and to the study of innovation processes in architectural firms, a sector traditionally understudied, because of the focus on large construction firms.

Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro ridership at the station level.

This paper constructs two novel direct demand models based on geographically weighted regression (GWR) for modeling influencing factors on metro ridership from a local perspective. One is GWR with globally implemented LASSO for feature selection, and the other one is geographically weighted LASSO (GWL) model, which is GWR with locally implemented LASSO for feature selection.

The results of real-world case study of Shenzhen Metro show that the two local models presented perform better than the traditional global model (OLS) in terms of estimation error of ridership and goodness-of-fit. Additionally, the GWL model results in a better fit than GWR with global LASSO model, indicating that the locally implemented LASSO is more effective for the accurate estimation of Shenzhen metro ridership than global LASSO does. Moreover, the information provided by both two local models regarding the spatial varied elasticities demonstrates the strong spatial interpretability of models and potentials in transport planning.

The main contributions are threefold: the approach is based on spatial models considering spatial autocorrelation of variables, which outperform the traditional global regression model – OLS – in terms of model fitting and spatial explanatory power. GWR with global feature selection using LASSO and GWL is compared through a real-world case study on Shenzhen Metro, that is, the difference between global feature selection and local feature selection is discussed. Network structures as a type of factors are quantified with the measurements in the field of complex network.

The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems.

The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE).

The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models.

The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

Exchange rate uncertainty leads to an indecisive environment for imports and exports that would condense international trade, foreign direct investment, trade earnings, trade volumes, economic growth and welfare. This study aims to examine, empirically, the effect of exchange rate uncertainty on bilateral trade performance, focusing on eight SAARC member economies using the popular modified gravity model of trade.

The paper includes eight SAARC members – Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan and Sri Lanka panel data set over the period 2005–2018. The authors consider both standardized value (standard deviation) and conditional variance model to determine volatility of exchange rate. Primarily, ordinary least squares, random effects and fixed effects estimation techniques are employed to investigate the impact of exchange rate volatility. Endogeneity and robustness of the findings have been tested using the simultaneity-adjusted model and dynamic panel data two-step system GMM estimation techniques.

Empirical findings endorse the view that exchange rate volatility lowers trade flows in the SAARC regions. However, this adverse effect of exchange rate uncertainty on trade is pretty small. The negative correlation between exchange rate volatility and bilateral trade remains consistent and significant after controlling of simultaneous causality, autocorrelation, year effects, country-pair heterogeneity and endogeneity irrespective of panel data estimation techniques and different measures of volatility.

The present paper is original work.

Some progress have been made over time in improving health conditions in Sub-Saharan Africa (SSA). There are, however, contradicting reports on the relationship between health outcomes and economic growth in the region. The paper aimed at assessing the effect of health outcome on economic growth in SSA.

Data for 41 countries from 2000 to 2018 were obtained from WDI and WGI and analyzed using system generalized method of moment (sGMM) which is appropriate for the present scenario. AR(1) and AR(2) tests were used to assess the validity of the model while Sargan and Hansen tests were adopted to examine the validity of the instrumental variables. The robustness of the estimation was confirmed using the pooled OLS and fixed effect regression.

Health outcome (proxied by life expectancy), lagged GDP per capita, capital formation, labor force (LF), health expenditure (HE), foreign direct investment (FDI) and trade openness (TOP) significantly affected economic growth emphasizing the importance of health in the process of economic growth in the region. AR(1) and AR(2) tests for serial correlation and Sargan/Hansen tests confirmed the validity of the estimated model and the instrumental variables respectively. Robustness of the GMM results was established from the pooled OLS and the fixed effect model results.

Improvement in the national health system possibly through the widespread adoption of National Health Insurance, increase government spending on healthcare alongside increased beneficial trade and ease of doing business to facilitate investment were recommended to enhance.

The study used up-to-date data with appropriate methodology.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

The purpose is to analyze the spatially varying impacts of corruption and public debt as % of GDP (proxies of government failures) on non-performing loans (NPLs) in European countries; comparing two periods: one prior to the crisis of 2007 and another one after that. The authors first modeled the NPLs with an ordinary lest square (OLS) regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the authors utilized the geographically weighted regression (GWR) to explore regional variations in the relationship between NPLs and the proxies of “Government failures”.

The authors first modeled the NPL with an OLS regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the author utilized the Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) to explore regional variations in the relationship between NPLs and proxies of “Government failures” (corruption and public debt as % of GDP).

The results confirm that corruption and public debt as % of GDP, after the crisis of 2007, have affected significantly on NPLs of the EU countries and the following countries neighboring the EU: Switzerland, Iceland, Norway, Montenegro, and Turkey.

In a spatial prospective, unprecedented in the literature, this research focused on the impact of corruption and public debt as % of GDP on NPLs in European countries. The positive correlation, as expected, between public debt and NPLs highlights that fiscal problems in Eurozone countries have led to an important rise of problem loans. The impact of institutional corruption on NPLs reports that the higher the corruption, the higher is the level of NPLs.

Comprehensive studies examining how Korean e-commerce trade works are currently limited. This study seeks to explore whether Korea’s e-commerce trade is more applicable to traditional trade theory or to modern theories. According to our analysis, the share of intra-industry trade (IIT) in modern trade theory is less than that of general trade for Korean e-commerce. Therefore, trade based on comparative advantage can be more valid in explaining e-commerce trade. From results in analyzing the gravity model, it was found that Korea’s e-commerce exports are higher as IIT with its FTA partners. In contrast, it is found that the lower the proportion of e-commerce trade, the higher chance for the import growth. Lastly, this study looked at what kind of comparative advantage is realized through imports. While Korea has been mostly exporting goods of high quality, its major trading partners import products based on price and selection of goods. In order for Korea’s e-commerce to grow, a more strategic approach is necessary. A strategy of high price based on superior quality is not effective, and as e-commerce has radically reduced sales and marketing costs, so a price reduction needs to be reflected in the price of goods for consumers.