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This paper aims to explore how the ownership transfer from a highly industrialised country to less industrialised countries influences consumers’ brand perceptions.

Three acquisition cases of premium car brands (Jaguar, Land Rover and Volvo) are investigated using qualitative data from online brand communities.

When country of ownership (COOW) for brands changes, it leads to different effects on consumers’ brand perception. Consumers are disoriented as to which cue to apply when evaluating the brand. They also see that brand values, and how these are communicated, are in conflict, as are sustainability images.

This paper focuses on the perspective of brand community members in Europe and the USA and studies only the car industry and acquisitions by two countries (China and India) using data from the time of ownership transfers. The authors discuss theoretical implications and suggest further research to gain more insights and address limitations.

Following a transfer of ownership, communication campaigns are required for addressing the original brand’s heritage and promoting the new brand owner’s image. Managers need to take advantage of loyal brand fans by turning them into brand ambassadors, spreading information to convince consumers that are more sceptical.

This study fills the knowledge gap regarding change of COOW to developing countries as new owners, and its consequences for consumer perception. The authors also introduce an innovative type of data collection through brand communities, which is less commonly used in international marketing research.

Recently, deep reinforcement learning is developing rapidly and shows its power to solve difficult problems such as robotics and game of GO. Meanwhile, satellite attitude control systems are still using classical control technics such as proportional – integral – derivative and slide mode control as major solutions, facing problems with adaptability and automation.

In this paper, an approach based on deep reinforcement learning is proposed to increase adaptability and autonomy of satellite control system. It is a model-based algorithm which could find solutions with fewer episodes of learning than model-free algorithms.

Simulation experiment shows that when classical control crashed, this approach could find solution and reach the target with hundreds times of explorations and learning.

This approach is a non-gradient method using heuristic search to optimize policy to avoid local optima. Compared with classical control technics, this approach does not need prior knowledge of satellite or its orbit, has the ability to adapt different kinds of situations with data learning and has the ability to adapt different kinds of satellite and different tasks through transfer learning.