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Abstract The purpose of this study is to describe customer-related knowledge utilisation in the collaborative relationships of professional service organisations. Within this specific context, knowledge transfer capabilities are emphasised as an important prerequisite in the utilisation process. Effective organisation-level knowledge utilisation is crucial in collaborative relationships of professional service organisations. In order to formulate a coherent service offering across different areas of expertise, for instance, it is beneficial to transfer customer knowledge between professionals, business units and functions. Knowledge utilisation across different expertise areas may also be an important prerequisite for an organisation's innovativeness and proactiveness in customer cooperation. Customer-related knowledge utilisation and related knowledge transfer processes are in this study approached from a relationship management perspective, and literature from organisation research, resource-based view and knowledge management is used as a theoretical basis. Empirically this study is based on a descriptive case study of two professional service firms in the field of business-to-business education and consultancy services. In the first case, an in-depth analysis of an organisation developing a collaborative relationship in the outsourcing situation is described. In the second case, additional views are given on organisational practices potentially facilitating customer-related knowledge transfer. Empirical results show that internal fragmentation in the professional service organisation seems to be, to a large extent, inherent in this type of organisation, and may cause many problems in customer-related knowledge transfer and thus in effective utilisation of that knowledge. These knowledge transfer inhibitors rise from an organisation's characteristics; its dominant logic, culture, structure and systems. These organisational characteristics are bound to the characteristics of knowledge itself: its tacitness, non-observability and complexity, and can have an inhibiting influence on knowledge transfer. However, in spite of the inherent forces causing internal fragmentation and inhibiting knowledge transfer, moderating practices of a well-planned relationship coordination system, customer knowledge and expertise codification, and cooperative working practices among the experts seem to help to maintain customer knowledge transfer and utilisation, and thus also continuity and value creation in the long-term relationships. This value creation can be seen to be based on accessing and integrating a wide variety of knowledge resources in order to create innovative, flexible and multifaceted service offerings. Value creation can also be based on organisational ability for generative learning in order to change prevailing organisational assumptions and to develop the operations model needed in collaborative relationship.

TV distribution and stream content delivery of video over the Internet, since is made up of complex networks including Content Delivery Networks (CDNs), cables and end-point user devices, that is very prone to issues appearing in different levels of the network ending up affecting the final customer’s TV services. When a problem affects the customer, and this prevents from having a proper TV delivery service in devices used for stream purposes, the issue is reported through a call, a TV case is opened and the company’s customer handling agents start supervising it to solve the problem as soon as possible. The goal of this research work is to present an ML-based solution that predicts the Resolution Times (RTs) of the TV cases in each TV delivery service type, therefore how long the cases will take to be solved. The approach taken to provide meaningful results consisted in utilizing four Machine Learning (ML) algorithms to create 480 models for each of the two scenarios. The results revealed that Random Forest (RF) and, specially, Gradient Boosting Machine (GBM) performed exceptionally well. Surprisingly, hyperparameter tuning didn’t significantly improve the RT as expected. Some challenges included the initial data preprocessing and some uncertainty in hyperparameter tuning approaches. Thanks to these predicted times, the company is now able to better inform their costumers on how long the problem is expected to last until is resolved. This real case scenario also considers how the company processes the available data and manages the problem. The research work consists in, first, a literature review on the prediction of RT of Trouble Ticket (TT) and customer churn in telecommunication companies, as well as the study of the company’s available data for the problem. Later, the research focuses in analysing the provided dataset for the experimentation, the preprocessing of the this data according to the industry standards and, finally, the predictions and analysis of the obtained performance metrics. The proposed solution is designed to offer an improved resolution for the company’s specified task. Future work could involve increasing the number of TV cases per service for improving the results and exploring the link between resolution times and customer churn decisions. / TV-distribution och leverans av strömningsinnehåll via internet består av komplexa nätverk, inklusive CDNs, kablar och slutanvändarutrustning. Detta gör det känsligt för problem på olika nätverksnivåer som kan påverka slutkundens TV-tjänster. När ett problem påverkar kunden och hindrar en korrekt TV-leveranstjänst rapporteras det genom ett samtal. Ett ärende öppnas, och företagets kundhanteringsagenter övervakar det för att lösa problemet så snabbt som möjligt. Målet med detta forskningsarbete är att presentera en maskininlärningsbaserad lösning som förutsäger löstiderna (RTs) för TV-ärenden inom varje TV-leveranstjänsttyp, det vill säga hur lång tid ärendena kommer att ta att lösa. För att få meningsfulla resultat användes fyra maskininlärningsalgoritmer för att skapa 480 modeller för var och en av de två scenarierna. Resultaten visade att Random Forest (RF) och framför allt Gradient Boosting Machine (GBM) presterade exceptionellt bra. Överraskande nog förbättrade inte finjusteringen av hyperparametrar RT som förväntat. Vissa utmaningar inkluderade den initiala dataförbehandlingen och osäkerhet i metoder för hyperparametertuning. Tack vare dessa förutsagda tider kan företaget nu bättre informera sina kunder om hur länge problemet förväntas vara olöst. Denna verkliga fallstudie tar också hänsyn till hur företaget hanterar tillgängliga data och problemet. Forskningsarbetet börjar med en litteraturgenomgång om förutsägelse av RT för Trouble Ticket (TT) och kundavhopp inom telekommunikationsföretag samt studier av företagets tillgängliga data för problemet. Därefter fokuserar forskningen på att analysera den tillhandahållna datamängden för experiment, förbehandling av datan enligt branschstandarder och till sist förutsägelser och analys av de erhållna prestandamätvärdena. Den föreslagna lösningen är utformad för att erbjuda en förbättrad lösning för företagets angivna uppgift. Framtida arbete kan innebära att öka antalet TV-ärenden per tjänst för att förbättra resultaten och utforska sambandet mellan löstider och kundavhoppbeslut.