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Machine learning is a popular way to find patterns and relationships in high complex datasets. With the nowadays advancements in storage and computational capabilities, some machine-learning techniques are becoming suitable for real-world applications. The aim of this work is to conduct a comparative analysis of machine learning algorithms and conventional statistical techniques. These methods have long been used for clustering large amounts of data and extracting knowledge in a wide variety of science fields. However, the central knowledge of the different methods and their specific requirements for the data set, as well as the limitations of the individual methods, are an obstacle for the correct use of these methods. New machine learning algorithms could be integrated even more strongly into the current evaluation if the right choice of methods were easier to make. In the present work, some different algorithms of machine learning are listed. Four methods (artificial neural network, regression method, self-organizing map, k-means al-algorithm) are compared in detail and possible selection criteria are pointed out. Finally, an estimation of the fields of work and application and possible limitations are provided, which should help to make choices for specific interdisciplinary analyses.

Although research and managerial practice have demonstrated great interest in the role of marketing departments within firms and have raised repeated concerns that their influence is in sharp decline, prior research has not analyzed whether marketing departments are truly losing ground. To do so, we build on the work of Homburg et al. (1999), which assessed the influence of the marketing department two decades ago. Drawing on structurally equivalent data, the results demonstrate that the marketing department has indeed lost significant influence. Additionally, we analyze which department has benefited from this loss of influence. Interestingly, it is the sales department that has gained influence, rather than the finance department, as one might assume. We also study the performance consequences of the intraorganizational distribution of influence among the marketing, sales, R&D, operations, and finance departments. Our results are alarming because an influential marketing department makes the greatest contribution to company performance.

This paper presents a comparative study of the views of British and German engineers on the relationship between engineers and marketers and the conflict between them. Data have been collected from 151 British and 84 German engineers in 25 companies. Engineers who work in a variety of functions and at a number of different levels within an organisation are included. It has been found that the relationship is seen as being reasonable, with teamwork and increased knowledge of marketing by engineers both needed to make it function effectively. The engineers in both countries do not seek to dominate marketing, but they do feel that they need to obtain an understanding of management and of marketing. Conflict is not seen as being high in either country but German engineers perceive there to be lower levels of conflict between them and their marketing colleagues. German engineers see education and training differences as being the main reasons for conflict, whereas British engineers cite a lack of understanding between the two functions as the main reason. Engineers in both countries agree that better communications, more teamwork and more training are needed to improve the interface between the two functions. From this study, modifications to current theoretical frameworks can be seen and these are discussed.