Beyond Mere Compliance — Delighting Customers by Implementing Data Privacy Measures?

The importance of customer data for business models is increasing, as is the relevance of customers’ concerns regarding privacy aspects. To prevent data privacy incidents and to mitigate the associated risks, companies need to implement appropriate measures. Furthermore, it is unclear whether their implementation – beyond mere compliance – has the potential to actually delight customers and yields competitive advantages. In this paper, we derive specific measures to deal with customers’ data privacy concerns based on the literature, legislative texts, and expert interviews. Next, we leverage the Kano model via an Internet-based survey to analyze the measures’ evaluation by customers. As a result, most measures are considered basic needs of must-be quality. Their implementation is obligatory and is not rewarded by customers. However, delighters of attractive quality do exist and have the potential to create a competitive advantage

Dead or alive? A formal decision model for deciding on customer recovery investments

As digitization makes customer migration easier and more attractive, managing customer recovery becomes increasingly important for companies. Thereby, the challenge for organizations is to avoid two error types that can occur with customer relation recovery. First, mistakenly investing in customer relations that are active anyway (alive customer relations), or second, mistakenly not investing in migrated customer relations (dead customer relations). Consequently, in order to support organizations with such recovery investment decisions, considering the probability if a customer relation is alive or dead is necessary. Based on this probability, an economically reasonable decision has to be made whether to invest in an individual customer relations recovery or not. However, existing literature often neglects the above mentioned probability. Accordingly, based on a comprehensive discussion of related work, we propose a formal decision model on whether to invest in a customer relations recovery considering the probability that the customer relation is still alive or dead. To demonstrate the decision models applicability, an illustrative case with a sample calculation is presented

The Business Alignment of Social Media Analytics

Many companies have realized the immense potential of Social Media (SM) insights and have started to use concepts of Social Media Analytics (SMA) to reveal them. However, despite the importance of SMA, executives frequently hand off the topic to IT departments, instead of being actively involved in the business alignment of SMA. While existing SMA literature frequently identifies this need, it only addresses individual steps of the business alignment rather than suggesting comprehensive approaches. In order to contribute to the scientific discussion, we adapt a framework from the requirements \ engineering field to structure the field of action and enable decision-makers to define the SM insights beneficial to their individual goals. To help practitioners with the application of our work, we show a practical example of how to use the framework, and present possible SMA concepts based on current literature that can be used to collect the required SM insights.

The disclosure of private data: measuring the privacy paradox in digital services

Privacy is a current topic in the context of digital services because such services demand mass volumes of consumer data. Although most consumers are aware of their personal privacy, they frequently do not behave rationally in terms of the risk-benefit trade-off. This phenomenon is known as the privacy paradox. It is a common limitation in research papers examining consumers’ privacy intentions. Using a design science approach, we develop a metric that determines the extent of consumers’ privacy paradox in digital services based on the theoretical construct of the privacy calculus. We demonstrate a practical application of the metric for mobile apps. With that, we contribute to validating respective research findings. Moreover, among others, consumers and companies can be prevented from unwanted consequences regarding data privacy issues and service market places can provide privacy-customized suggestions