The influence of formal and informal sales controls on customer-directed selling behaviors and sales unit effectiveness

Adaptive selling (AS) and customer-oriented selling (COS) constitute two key customer-directed selling behaviors for the success of the modern sales force. However, knowledge regarding the organizational factors that can induce salespeople to engage in those behaviors is strikingly limited. Against this background, we develop a comprehensive model that delineates the influences of formal and informal sales controls on AS and COS and, through them, on sales unit effectiveness. Based on a sample of sales managers in a major European Union country, we present new evidence that (a) formal and informal sales controls exert differential impact on salespeople's AS and COS behaviors; (b) AS directly and positively influences sales unit effectiveness; (c) COS affects sales unit effectiveness only indirectly, i.e. by fostering AS; and (d) outcome and cultural controls directly improve sales unit effectiveness. We conclude with a discussion of our findings for academics and practitioners.

Dangers of ‘Facebook Login’ for Mobile Apps: Is There a Price Tag for Social Information?

Social networks offer horizontal integration for any mobile platform providing app users with a convenient single sign-on point. Nonetheless, there are growing privacy concerns regarding its use. These vulnerabilities trigger alarm among app developers who fight for their user base: While they are happy to act on users’ information collected via social networks, they are not always willing to sacrifice their adoption rate for this goal. So far, understanding of this trade-off has remained ambiguous. To fill this gap, we employ a discrete choice experiment to explore the role of Facebook Login and investigate the impact of accompanying requests for different information items / actions in the mobile app adoption process. We quantify users’ concerns regarding these items in monetary terms. Beyond hands-on insights for providers, our study contributes to the theoretical discourse on the value of privacy in the growing world of Social Media and mobile web.

Forecasting Intraday Stock Price Trends with Text Mining Techniques

In this paper, we describe NewsCATS (news categorization and trading system), a system implemented to predict stock price trends for the time immediately after the publication of press releases. NewsCATS consists mainly of three components. The first component retrieves relevant information from press releases through the application of text preprocessing techniques. The second component sorts the press releases into predefined categories. Finally, appropriate trading strategies are derived by the third component by means of the earlier categorization. The findings indicate that a categorization of press releases is able to provide additional information that can be used to forecast stock price trends, but that an adequate trading strategy is essential for the results of the categorization to be fully exploited.