As an increasing number of companies operates in international markets characterized by global competition, many traditional manufacturers augment their product offerings with services to gain competitive advantage. As servitization needs change throughout the company, many companies struggle on the transition from a product – to a service centric business model. The dynamic capabilities view analyses capabilities in changing environments and could therefore be an interesting theoretical lens for servitization research. Building on existing case research of dynamic capabilities in a servitization context, we analyze the impact of dynamic capabilities and especially of sensing, seizing and reconfiguration capabilities on firm performance in a servitization context. Additionally, we analyze the moderating role of environmental turbulence. The results, which are based on 206 manufacturing companies, show that dynamic capabilities are an essential factor for the performance of a firm in the context of servitization. We find a significant impact of sensing and reconfiguration on firm performance, whereas seizing has no significant impact. We fail to confirm a significant moderating impact of environmental turbulence, which indicates that dynamic capabilities are important in a servitization context indifferent of environmental turbulence. However, we find indication that reconfiguration is more important in relatively stable contexts, whereas sensing is more important in turbulent environments. We contribute to the literature on servitization and dynamic capabilities by creating evidence that dynamic capabilities have an impact on firm performance in a servitization context. This has practical implications as well: Managers in servitizing companies should assess their dynamic capabilities and should especially focus on reconfiguration in relatively stable environments and on sensing on turbulent environments.

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This contribution deals with a comparison of one AI based data mining tool and two traditional approaches utilized to collect and interpret data for prospect generation. Traditional prospect generation methods, like manual web search or using purchased data from external providers may involve high costs and efforts and are subject to failures and waste through outdated and untargeted data. In contrast, AI based methods claim to provide better results at lower costs. Based on a real case, the authors compare effects of these three prospect generation methods. AI based data mining tools compensate for some weaknesses of other methods, especially because they do not need pre-defined selection criteria which might bias the results. In addition, they involve less effort from the researcher. However, the results in generating concrete prospects may be still weaker than with traditional methods if web crawling activities are influenced by underlying databases. For academic research in the field of prospect generation, this study provides a fact-based comparison of approaches. Implications for businesses include the advice to combine methods rather than to rely on a single approach. The time available for research and the complexity of the target market have an influence on the selection of the prospect generation approach.

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

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