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This study aims to explain the effect of entrepreneurial orientation (EO) dimensions on firms’ export performance. The study has considered three dimensions of EO: innovativeness, proactiveness and risk-taking. Export performance has been measured through multifaceted determinants: financial, strategic and satisfaction levels of exporting firms.

To explain the association between such determinants, qualitative face-to-face, in-depth interviews were conducted with the respondents from the apparel manufacturing companies of Bangladesh. The study has also applied a content analysis technique to explore and triangulate the descriptions of EO dimensions on performance.

The research results reveal that EO has a meaningful explanation of export performance from the lived experience of the respondents. The study has also found frequent explanations and indicators about the association of innovativeness and proactiveness on export performance, but the risk-taking dimension has revealed no effects.

The research has different implications on theoretical, managerial and policy-making aspects of EO and export performance. Nevertheless, the findings are based on the opinions of a small sample of entrepreneurs and high-level managers. Thus, it is important to test the generalizability of these findings in future research using a larger sample.

This study indicated product, process and market creation innovativeness. Risks related to product innovation, new market entry and politics are considered for risk-taking measures, while proactive innovation, technology and new market searching are used for measuring proactiveness. Additionally, multifaceted performance determinants have been applied to evaluate export performance. Hence, this study is a unique qualitative analysis with important theoretical, practical and methodological implications.

The purpose of this paper is to analyze the leadership decisions taken during the crisis and their influence on the goals recognized by managers as crucial for surviving.

During the survey, conducted in April 2020 (one month after the first economic lockdown in Poland), as part of a research project called Sur(VIR)val – Survival during the virus, data was collected from 178 leaders from randomly selected companies from Poland using the CAWI method. Ordered logistic regression modeling was used to examine the impact of the decisions taken by company leaders on the goals seen by leaders as most important for company survival.

The results obtained in the study show that during the first Covid-19 lockdown in 2020, leaders made decisions that can be seen as oriented toward survival and continuity. Changing to remote working, extending payment deadlines for customers, as well as selective employment reduction turned out to have the greatest influence on strategic support for maintaining current production levels and retaining competent employees in order to survive the crisis.

This study has certain limitations. First, the list of leadership decisions and company goals used as dependent variables is not exhaustive. Second, the selection of business goals oriented toward survival may not derive directly from the lockdown situation. Third, our study did not measure the actual accomplishment of the company goals, but the managerial perceptions as to which ones are crucial for company survival during crises, and which of them should be given strategic support respectively. Fourth, the research sample was randomly constructed and covered only business organizations in Poland. Fifth, the hypotheses were formulated in a way that treated leadership decisions as one construct. Finally, we used survey, with a scale measuring managerial perceptions.

Leaders should ensure that proper IT tools are developed within the organization, and that the skill level of employees is high enough for fast shifting employees on to remote working. At the same time, it is important to maintain IT infrastructure at a high level. In terms of general recommendations for leaders, they should make quick decisions, maintain the most valuable resources of the company (human resources and cash flow) and take actions aimed at taking advantage of opportunities (R&D) during and after the crisis.

Additionally, due to the key importance of human resources for the survival of the organization, leaders should respond quickly by making flexible decisions about sending employees on leave and downtime. As human resources are the most valuable assets of the company from the point of view of its survival, decisions concerning employment reduction should be taken carefully. Leaders who acted in panic after the first lockdown and made employees redundant, later on had problems recruiting skilled employees back and strived to return to full organizational capacity.

Although scholars have investigated leadership decisions and actions taken during economic crises, little is known about how leaders behave when taken by surprise, and what decisions they make when the duration of a crisis is difficult to predict. The results of this study show which leadership decisions during the first Covid-19 lockdown in 2020 influenced prioritizing critical company goals oriented toward survival.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

The alienation of megaproject environmental responsibility (MER) behavior is destructive, but its mechanism has not been clearly depicted. Based on fraud triangle theory and the fuzzy set qualitative comparative analysis (fsQCA) method, this study explored the combined effect of antecedent factors on alienation of MER behavior.

Based on the fraud triangle theory and literature review, eight influencing factors associated with the alienation of MER behavior were first identified. Subsequently, the fuzzy-set qualitative comparative analysis was used in this study to reveal configurations influencing alienation of MER behavior.

The study found nine configurations of MER behavioral alienation antecedent factors, integrated into three types of driving modes, i.e. “economic pressure + learning effect,” “institutional defect + moral rejection,” and “information asymmetry + economic pressure + expectation pressure.”

By analyzing the configuration effects of various induced conditions, this study puts forward a comprehensive analysis framework to solve the alienation of MER behavior in the megaprojects and a practical strategy to control alienation of MER behavior.

This study investigates whether corporate executives, who are university alumni, influence each other's firm corporate social responsibility (CSR) performance.

Drawing on social network theory, the authors hypothesise that a firm's CSR performance is positively associated with its peer firms' average CSR performance when the executives of the firm and its peer firms are university alumni. The study employs data from 1,685 listed firms and 4,906 executives who graduated from 585 different universities in China and runs multivariate regressions.

The results reveal a sizeable university peer influence on CSR performance. Such influence is even stronger for executives who graduated from elite universities (e.g. 985 or 211 universities), and universities or programmes that provide more opportunities for alumni reunions or networking (e.g. MBAs/EMBAs). Executives who are more influential in making firm decisions (e.g. CEOs/CFOs), as well as firms that are more likely to mimic the behaviour of others, also show higher degrees of university peer influence.

The results highlight the role of education in ethical decision-making.

This study documents evidence on a new determinant of firm CSR performance. The study sheds light on the impact of non-institutionalised personal ties, for example, university alumni networks, on CSR performance.