<p>Adjustment to a new culture is a slow and step-wise process, which can be facilitated by cross-cultural training. First, a comprehensive review of current theories about cross-cultural adjustment and different training techniques is presented. Then, the study looks at the preparation and training of expatriates at Ericsson in relation to these theories. In-depth interviews with two Area Managers at Ericsson have been carried out in order to establish how the company’s training program is structured. We found that Ericsson follows a sequential model with both pre-departure and post-arrival training, mainly didactic in nature. The pre-departure training is very culturegeneral, whereas the post-arrival training is more specific for the host culture. The post-arrival training is, however, not offered in all host countries. Language training is also offered, and expatriates are encouraged to visit the host country prior to the departure. Many components in Ericsson’s training program are supported by theory,but several other techniques described in the theory are not in use. A suggestion is made to further formalize the post-arrival training and make the overall training more culture-specific.</p>

Adjustment to a new culture is a slow and step-wise process, which can be facilitated by cross-cultural training. First, a comprehensive review of current theories about cross-cultural adjustment and different training techniques is presented. Then, the study looks at the preparation and training of expatriates at Ericsson in relation to these theories. In-depth interviews with two Area Managers at Ericsson have been carried out in order to establish how the company’s training program is structured. We found that Ericsson follows a sequential model with both pre-departure and post-arrival training, mainly didactic in nature. The pre-departure training is very culturegeneral, whereas the post-arrival training is more specific for the host culture. The post-arrival training is, however, not offered in all host countries. Language training is also offered, and expatriates are encouraged to visit the host country prior to the departure. Many components in Ericsson’s training program are supported by theory,but several other techniques described in the theory are not in use. A suggestion is made to further formalize the post-arrival training and make the overall training more culture-specific.

Expatriates’ adjustment to the host-country culture is a dynamic and gradual process. This process can be facilitated by cross-cultural training. The relationship between cross-cultural training and crosscultural adjustment has been explored to a great extent in the literature, however scholars have mainly put focus on how this relationship unfolds in the context of MNCs offering CCT in-house. Consequently, this leaves an interesting uninvestigated gap in terms of how fully integrated relocation service companies work with cross-cultural services and how their work can facilitate cross-cultural adjustment. This qualitative case-study therefore aims to examine the relationship between crosscultural training provided by a relocation service company and the expatriate adjustment process. An extensive review on existing theories concerning cross-cultural adjustment, cross-cultural training and cross-cultural learning are presented. After this, the case study examines how Nordic Relocation Group (NRG), a relocation service company in Sweden, operates in terms of providing cross-cultural services. The findings indicate that the relocation service company’s services are divided into different phases, whereby the timing and content of services offered varies as the expatriates’ international assignment develops over time. This form of tailoring the cross-cultural training and the content of their services supports the notion of sequential training, which within IHRM literature, is proposed as optimal in order to facilitate the expatriate’s adjustment process. Moreover, the results further indicate that the way in which NRG works with cross-cultural services, in terms of strategy, sequential order, and specific CCT activities offered, promotes effective cross-cultural learning. Consequently, the CCT strategies offered by the relocation service company can be viewed as being aligned with best practice.

The research of open-domain, knowledge-grounded dialogue systems has been advancing rapidly due to the paradigm shift introduced by large language models (LLMs). While the strides have improved the performance of the dialogue systems, the scope is mostly monolingual and English-centric. The lack of multilingual in-task dialogue data further discourages research in this direction. This thesis explores the use of transfer learning techniques to extend the English-centric dialogue systems to multiple languages. In particular, this work focuses on five typologically diverse languages, of which well-performing models could generalize to the languages that are part of the language family as the target languages, hence widening the accessibility of the systems to speakers of various languages. I propose two approaches: Multilingual Retrieval-Augmented Dialogue Model (xRAD) and Multilingual Generative Dialogue Model (xGenD). xRAD is adopted from a pre-trained multilingual question answering (QA) system and comprises a neural retriever and a multilingual generation model. Prior to the response generation, the retriever fetches relevant knowledge and conditions the retrievals to the generator as part of the dialogue context. This approach can incorporate knowledge into conversational agents, thus improving the factual accuracy of a dialogue model. In addition, xRAD has advantages over xGenD because of its modularity, which allows the fusion of QA and dialogue systems so long as appropriate pre-trained models are employed. On the other hand, xGenD takes advantage of an existing English dialogue model and performs a zero-shot cross-lingual transfer by training sequentially on English dialogue and multilingual QA datasets. Both automated and human evaluation were carried out to measure the models' performance against the machine translation baseline. The result showed that xRAD outperformed xGenD significantly and surpassed the baseline in most metrics, particularly in terms of relevance and engagingness. Whilst xRAD performance was promising to some extent, a detailed analysis revealed that the generated responses were not actually grounded in the retrieved paragraphs. Suggestions were offered to mitigate the issue, which hopefully could lead to significant progress of multilingual knowledge-grounded dialogue systems in the future.