Investment and wealth management are more important to customers than before. Bankers make the most of the market of wealth management. Relationship marketing plays an important role in this kind of situation. Relationship marketing is also the key factor of bankers¡¦ differentiation. According to literatures, this paper is to study the effect of ¡§the attributes of investor¡Bthe attributes of consultant¡Brelational selling behavior and corporate image¡¨ on the relationship quality. Refer to the paper of Morgan and Hunt (1994), the Key Mediating Variables Model describe that ¡§commitment and trust¡¨ are the key factors of success in the relationship marketing. So this paper adopts the concept of Key Mediating Variables Model, and uses ¡§commitment and trust¡¨ to discover the relationship quality between customers and consultants. This paper also discovers the effect of relationship quality on the customer satisfaction. Study Discovery: The customers¡¦ amount of investment and risk preference are not found to affect relationship quality significant effects. The expertise of the consultant has significant and positive effects on the relationship quality. Contact frequency and disclosure have significant and positive effects on the relationship quality. Corporate image is not found to affect relationship quality significant effects. Relationship quality has significant and positive effects on the customer satisfaction.

Background: The sales representatives in B2B companies are experiencing several changes in their environment, which have already altered their performed activities. In order to meet the new customer needs, Artificial Intelligence (AI) provides an effective usage of the large amount of complex data that is available, defined as Big Data. AI is developing intelligence that is human-like and is expected to impact occupational roles while threating to automate tasks typically performed by humans. Previous technologies have already impacted sales representatives in the performance of their sales activities; however, it is still uncertain how AI will impact and benefit them. Previous empirical findings and the lack of studies centered on the individual impact of AI confirm the need for more academic reports. Purpose: The aim of this research is to explore how the implementation of Artificial Intelligence and usage of Big Data in Business-to-Business selling processes are impacting sales representatives, in term of performed activities. Further, the aim is also to explore the management of individuals during the implementation of AI. Methodology: This qualitative study is based on a realistic perspective with an inductive research approach. The empirical data has been collected through semi structured interviews with six AI-providers and two consulting firms that have proven experiences in working with AI and sales in B2B companies. Conclusion: AI is characterized by its adapting capability as well as its ability to process and combine a large amount of real-time, online and historical data. As a result, the selling process is constantly provided with more accurate, faster and original insights. Through the analytical capacity of AI, the sales representatives are gaining extensive knowledge about the customer and the external world. Also, AI simplifies the creation and maintenance of long- lasting customer relationships by providing specific and valuable content. Administrative tasks and non-sales activities can also become automated through the usage of AI, which enables sales representatives to focus on their core tasks, for instance relationship building and value-adding activities. The threat of automation and elimination of jobs should be redefined into the possibility to augment human capabilities. By adopting this approach, the importance of the human-machine collaboration is strongly emphasized. In order to increase the willingness for changing working procedures at individual levels, the communication during the process of change should be centered on creating a positive perception and understanding of AI. It is also important to create trust for AI and promote a data-driven culture in order to ensure the systematic usage of the system.