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The behavior of tourists strongly depends on the availability and quality of information. Too little information as well as too much can be disorienting and forces many tourists to join the majority visiting major sights. This causes a few crowded places in contrast to many which are under‐utilized. A Destination Management Organization has the goal to spread tourists more evenly, whereas the tourists would like to enjoy the destination to its full potential according to their personal interests. The paper aims to focus on the issues surrounding the creation of a mobile tourist guide.

A field trial was conducted in the summer of 2005 to study the following questions as a precondition for the development: Is it possible to seed generic interest profiles in the mobile context that allow the accurate prediction of actual rankings? Are the interest profiles sufficiently diverse to base personalized tours on individual interest profiles instead of interest prototypes? How do personalized tours affect the spatial behavior of tourist? Three methods to elicit the generic preferences of tourist in the mobile context are compared with actual rankings using Spearman's rank order coefficient.

The diversity of the interest profiles is analyzed in various ways leading to the conclusion that personalized interest profiles are necessary. For the gathered profiles tours are computed and simulated in order to gain a first insight into the effect on the spatial behavior of tourists.

The dynamic tour guide is supporting both goals by means of pervasive computing based on the actual context which is defined by personal interests, location and schedule of a tourist. It enables a personalized, spontaneous and guided tour.

Globalisation is generally defined as the “denationalisation of clusters of political, economic, and social activities” that destabilize the ability of the sovereign State to control activities on its territory, due to the rising need to find solutions for universal problems, like the pollution of the environment, on an international level. Globalisation is a complex, forceful legal and social process that take place within an integrated whole with out regard to geographical boundaries. Globalisation thus differs from international activities, which arise between and among States, and it differs from multinational activities that occur in more than one nation‐State. This does not mean that countries are not involved in the sociolegal dynamics that those transboundary process trigger. In a sense, the movements triggered by global processes promote greater economic interdependence among countries. Globalisation can be traced back to the depression preceding World War II and globalisation at that time included spreading of the capitalist economic system as a means of getting access to extended markets. The first step was to create sufficient export surplus to maintain full employment in the capitalist world and secondly establishing a globalized economy where the planet would be united in peace and wealth. The idea of interdependence among quite separate and distinct countries is a very important part of talks on globalisation and a significant side of today’s global political economy.

In 2006 the German-based electronics company Siemens faced widespread corruption and bribery allegations. Investigations of the German state attorney’s office disclosed an amount of more than 2.3 billion of suspicious payments to foreign governments (Schubert & Miller, 2008). It turned out that Siemens had bribed governmental officials in order to secure contracts and to obtain favorable conditions over more than three decades (Schmidt, 2009). Though Siemens had a clearly stated anticorruption policy this did not prevent the company from getting involved in one of the largest corporate scandals in German business history.

A deeper analysis of the scandal reveals at least four fundamental shortcomings which enabled the corrupt practices on all organizational levels. First, most of the managers saw no alternatives to secure their foreign business, especially in countries where bribery payment has been a widespread practice. Second, the managers had created misguided bonds of loyalty believing that personal engagement in the corruption scheme was part of their dedication to the company. Third, due to corporate routines and commonly accepted practices, most managers lacked a clear sense of reality seeing corruption as part of the regular business at Siemens. Fourth, poor governance structures and a lack of clear regulations for doing business in a corrupt environment made it easier for managers to bypass official regulations.

YOU WILL HAVE seen, no doubt, recent announcements that my wife and I have sold our book‐publishing business of Clive Bingley Ltd to the Munich‐based firm of international reference publishers, Verlag Dokumentation.

This column has always intended to provide indepth, comparative reviews of abstracting services, indexes, serial bibliographies, yearbooks, directories, almanacs and other serial tools which would normally be housed in reference departments. For the purposes of this column, reference serials are materials which must meet two rather flexible requirements: they must be useful as reference sources and they must be issued as serials or be titles which are superseded periodically by new editions.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.