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For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The purpose of this study was to identify the explanatory power of the affective, cognitive and evaluative aspects of identity with work teams in predicting team effectiveness, represented by the variables: satisfaction with the team, manager-assessed team performance and objective indicators of performance.

Data were collected from 131 work teams of a Brazilian public organization with units in all state capitals of the country. Work team identity scale, the work team satisfaction scale, the team performance scale and objective performance indicators collected based on the achievement of the goals set for the units that make up the organization were used. To test the predictive model, three regressions were conducted using the stepwise method.

Regression analysis results showed that the evaluative dimension explains about 6% of the performance assessment given by managers, whereas the affective dimension explains 63% of the satisfaction with work teams. No significant results were found for the objective performance indicators.

The observed findings demonstrate the pertinence of understanding the work team identity as a collective and multidimensional phenomenon, as well as the contribution of its different components in explaining variables that represent effectiveness.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

Entrepreneurship is an important paradigm for enhancing the economic well-being of nations. However, despite heated debate about the significant role of entrepreneurial education (EE) in developing favourable entrepreneurial intention (EI), little is known about the role of individuals’ entrepreneurial self-efficacy (ES), entrepreneurial motivation (EM) and family support (FS), which the authors investigated in this study.

This study has used a quantitative research design to collect data from 334 young people from various Malaysian higher education institutes using a purposive sampling technique and a deductive approach based on the theory of planned behaviour (TPB).

The findings revealed interesting insights into the criticality of young people’s ES, EM and FS in learning methods, techniques and skills to start new enterprises. Moreover, EE was a significant mediator of the relationship between individual self-efficacy, FS, EM and EI.

This study is among the few to contribute to strategic management scholarship by designing a framework based on the idea that EE relies on diverse factors, particularly ES, EM and FS. These factors encourage Malaysian young people to seek the necessary education to develop favourable EI and launch successful businesses.

How to design jobs to support innovation is an issue that has received plenty of consideration over the past years. Building on the job characteristics model, the present study is set up to identify configurations of perceived job characteristics for innovation.

By adopting a fuzzy-set configurational approach (fsQCA), the research question is addressed through a two-wave self-report survey of 199 employees of an Italian manufacturing company.

Results reveal four compatible configurations of job characteristics leading to high levels of innovative work behavior and two for low levels.

The results offer guidance for managers and organizations that aim to strengthen employee-driven innovation by offering different recipes of job design to maximize the chance of boosting innovative behaviors among their workers.

This research is one of the first to empirically test the relation of job characteristics for innovative behavior using a configurational approach. By doing so it contributes to the literature by advancing the notion that innovative endeavors are determined by the holistic effects of different interdependent configurations of job characteristics.

The purpose of this paper is to use tenets of the complexity theory in order to study the effect of various determinants of firm’s performance, such as CEO’s compensation and age, for the case of 72 insurance companies.

The authors identify the asymmetries in the data set by creating quantiles and using contrarian analysis. Instead of ignoring this information and use a main effects approach, all the available information in the data set is taken into account. For this purpose, the authors use qualitative comparative analysis to find alternative equifinal routes toward high firm performance.

Five configurations are found which lead to high performance. Every one of the five configurations is found to be sufficient but not necessary for high firm performance.

The research findings contribute to a better understanding of the determinants of firm’s performance taking into account the asymmetries in the data set. The authors identify alternative paths toward high firm performance, which could be vital information for the decision maker inside a firm.

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field.

The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past.

Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure’s benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure’s key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building.

The authors examined a subset of journals in the field and did not include general management journals that publish international business and marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study.

The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with practical guidelines for making better choices concerning an appropriate SEM approach. Furthermore, based on a systematic review of current practices in the international business and marketing literature, the authors identify critical challenges in the selection and use of SEM procedures and offer concrete recommendations for better practice.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

Important research once thought unassailable has failed to replicate. Not just in economics, but in all science. The problem is therefore not in dispute nor are some of the causes, like low power, selective reporting, the file drawer effect, publicly unavailable data and so forth. Some partially worthy solutions have already been offered, like pre-registering hypotheses and data analysis plans.

This is a review paper on the replication crisis, which is by now very well known.

This study offers another partial solution, which is to remind researchers that correlation does not logically imply causation. The effect of this reminder is to eschew “significance” testing, whether in frequentist or Bayesian form (like Bayes factors) and to report models in predictive form, so that anybody can check the veracity of any model. In effect, all papers could undergo replication testing.

The author argues that this, or any solution, will never eliminate all errors.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?