(untitled) (International Journal of Engineering Sciences & Management Research, 2014)
The internationalisation of business has undergone tremendous change in the recent past. To compete in the global marketplace, multinational companies have recognised the importance of international human resource management that facilitates the success of expatriate assignments. But most of the companies functioning in multiple locations report that finding qualified and motivated candidates with a high potential for success is the greatest challenge. In the context of global assignments, managing expatriate talent is a hard-hitting task, because expatriates bring with them varying degrees of self efficacy, personality attributes, perceptions and relationship skills. Many organisations started using customised training programs for reducing the expatriat e failure. But very few succeeded in correcting the expatriate failure due to the costs associated with the preliminary HR functions such as recruitment, selection and training. Hence a problem identified in the context of expatriate training can be tackled to a remarkable extent using predictive analytics. In fact predictive analytics can generate a state of affairs based on how a set of variables interact and make them superior to static business rules evaluating single variables serially in certain applications. Nevertheless predictive analytics plays a pivotal role in equipping the firms to analyze past and present data and utilise the information to predict future conditions.