Leveraging artificial intelligence to generate organizational learning: The case of B2B sales
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School of Business |
Master's thesis
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Authors
Date
2022
Department
Johtamisen laitos
Major/Subject
Mcode
Degree programme
Global Management
Language
en
Pages
146 + 9
Series
Abstract
The research revolves around the use of artificial intelligence (AI) in the analysis of social styles and emotions of B2B sales customers and their use in the generation of organizational learning. The study includes a literature review of organizational learning, social styles and AI. Additionally, empirical research was conducted including a group of seven salespeople with varying experience levels, a marketing expert bringing a perspective of the further use of the data and two AI experts to gain practical knowledge on its current use cases, capabilities and potential. With a thematic analysis of the interview data, we found that both social styles and emotions have an indirect effect on sales outcomes and processes through trust in creating new opportunities and moving processes forward faster. Nonetheless, sales representatives had some difficulties in the recognition of social styles except for specific characteristics and in the recognition of emotions in text-based communications and emotional patterns. The sales representatives tend to adjust their communications based on emotions with several methods such as mirroring and labelling. Social styles they mostly adjust based on characteristics. These identified characteristics are rarely shared on tools such as customer relationship management (CRM) systems, which are currently used mostly for tracking and monitoring opportunities, sharing basic customer insights and communications. The researcher offered two theoretical frameworks for the identification and use of social styles and emotions data with AI to increase organizational learning taking into account the above findings. The AIDLL model, allows for identification and use of social styles and emotions for the improvement of sales communications, taking into account the need for transparent AI, quality data and the improvement of data practices. The second, ESSOL model, offers a further use of emotions and social styles data to create opportunities for organizational learning using AI capabilities. With the models, the researcher finally suggests practical implications for organizations.Description
Thesis advisor
Schildt, HenriKeywords
artificial intelligence, social style, emotion, organizational learning, organizational memory, double-loop learning, B2B sales, machine learning