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This study aims to extend the understanding of entrepreneurial orientation (EO) by investigating the international EO (IEO) from the perspective of the international performance of born global firms. The born global firm's EOs toward global firms led to innovation and dynamic organizational capabilities and to improve the firms' international performance and development.

It is a hypothesis that IEO predicted that utilization of export promotion programs (EPPs) would be positively associated with international performance. The samples of 405 respondents were collected from born global firms to acquire higher international performance of firms in China. Structural equation modeling (SEM) and mediation analysis are processed to test the proposed hypotheses empirically.

The results from the SEM test revealed that the network relationships and utilization of EPPs fully mediate the effect of IEO on international performance. These results indicate that not only IEO is an important factor of network relationships but also the utilization of EPPs and international performance for born global firms. IEO can motivate managers to find the right network relationships and ultimately improve company performance. The IEO of the initiative has strong impact on the performance of international firms.

There is a comprehensive view among the relationships of IEO, entrepreneurial competencies and the network utilization of government EPPs, international performance in the context of born global firms. Theoretical and practical implications are discussed in detail.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.