Case Article—The RealPro Customer Benefits Program: Rekindling Shopper Loyalty Through a Subscription Service

In this case study, students combine data-based insights with strategic considerations to make fundamental business decisions at the German grocery retail chain Real. In response to dwindling numbers of customers and reduced revenues, Real developed the RealPro customer benefits program to achieve a quick turnaround. For a fixed annual fee, RealPro members receive substantial and permanent discounts of 20% on nonpromoted items from a broad range of food categories. Students employ data analytics methods to extract insights from the provided data set, which contains point-of-sale information from the actual market test of RealPro. Based on these insights, decisions concerning the rollout and design of the RealPro program must be made. We provide data analysis solutions in both Excel and R to analyze 75 thousand customer transactions. In the case extension, students can apply the difference-in-differences method and two covariate balancing algorithms for in-depth statistical analyses. For this purpose, we provide an additional unbalanced data set with 83 thousand transactions, on which the students can test and analyze propensity score matching and entropy balancing models.

Extremizing and Antiextremizing in Bayesian Ensembles of Binary-Event Forecasts

Many organizations combine forecasts of probabilities of binary events to support critical business decisions, such as the approval of credit or the recommendation of a drug. To aggregate individual probabilities, we offer a new method based on Bayesian principles that can help identify why and when combined probabilities need to be extremized. Extremizing is typically viewed as shifting the average probability farther from one half; we emphasize that it is more suitable to define extremizing as shifting it farther from the base rate. We introduce the notion of antiextremizing, cases in which it might be beneficial to make average probabilities less extreme. Analytically, we find that our Bayesian ensembles often extremize the average forecast but sometimes antiextremize instead. On several publicly available data sets, we demonstrate that our Bayesian ensemble performs well and antiextremizes anywhere from 18% to 73% of the cases. Antiextremizing is required more often when there is bracketing with respect to the base rate among the probabilities being aggregated than with no bracketing.

A Code of Digital Ethics: laying the foundation for digital ethics in a science and technology company

The rapid and dynamic nature of digital transformation challenges companies that wish to develop and deploy novel digital technologies. Like other actors faced with this transformation, companies need to find robust ways to ethically guide their innovations and business decisions. Digital ethics has recently featured in a plethora of both practical corporate guidelines and compilations of high-level principles, but there remains a gap concerning the development of sound ethical guidance in specific business contexts. As a multinational science and technology company faced with a broad range of digital ventures and associated ethical challenges, Merck KGaA has laid the foundations for bridging this gap by developing a Code of Digital Ethics (CoDE) tailored for this context. Following a comprehensive analysis of existing digital ethics guidelines, we used a reconstructive social research approach to identify 20 relevant principles and derive a code designed as a multi-purpose tool. Versatility was prioritised by defining non-prescriptive guidelines that are open to different perspectives and thus well-suited for operationalisation for varied business purposes. We also chose a clear nested structure that highlights the relationships between five core and fifteen subsidiary principles as well as the different levels of reference—data and algorithmic systems—to which they apply. The CoDE will serve Merck KGaA and its new Digital Ethics Advisory Panel to guide ethical reflection, evaluation and decision-making across the full spectrum of digital developments encountered and undertaken by the company whilst also offering an opportunity to increase transparency for external partners, and thus trust.

TinyLFU-Based Semi-Stream Cache Join for Near-Real-Time Data Warehousing

Semi-stream join is an emerging research problem in the domain of near-real-time data warehousing. A semi-stream join is basically a join between a fast stream (S) and a slow disk-based relation (R). In the modern era of technology, huge amounts of data are being generated swiftly on a daily basis which needs to be instantly analyzed for making successful business decisions. Keeping this in mind, a famous algorithm called CACHEJOIN (Cache Join) was proposed. The limitation of the CACHEJOIN algorithm is that it does not deal with the frequently changing trends in a stream data efficiently. To overcome this limitation, in this paper we propose a TinyLFU-CACHEJOIN algorithm, a modified version of the original CACHEJOIN algorithm, which is designed to enhance the performance of a CACHEJOIN algorithm. TinyLFU-CACHEJOIN employs an intelligent strategy which keeps only those records of $R$ in the cache that have a high hit rate in S. This mechanism of TinyLFU-CACHEJOIN allows it to deal with the sudden and abrupt trend changes in S. We developed a cost model for our TinyLFU-CACHEJOIN algorithm and proved it empirically. We also assessed the performance of our proposed TinyLFU-CACHEJOIN algorithm with the existing CACHEJOIN algorithm on a skewed synthetic dataset. The experiments proved that TinyLFU-CACHEJOIN algorithm significantly outperforms the CACHEJOIN algorithm.

Risk Management in Company Providing Services on the International Market

Effective and integrated risk management requires integrating the risk management process into the enterprise management process. Each enterprise takes risks to achieve the planned results. The market economy creates both opportunities to achieve the planned profits and the risk of losses as a result of unfavorable changes in the company's environment and errors within the organization. At the time of making a decision, it is never certain how the conditions for the implementation of the planned project will develop in the future. Accounting, which is the most important element of the system, plays an important role in the risk management process information business unit. Nowadays, all business decisions are burdened with risk, which is why organizations more and more often decide to implement a risk management system.

Künstliche Intelligenz im Rahmen unternehmerischer Entscheidungen des Vorstands der AG

The use of Artificial Intelligence (AI) in the context of business decisions of the AG's board of directors brings the company not only opportunities but also major challenges. The first question that arises is whether it is legally permissible to delegate business decisions to AI systems. It is then necessary to consider what skills and knowledge the board of directors should possess to fulfill the new AI-related tasks, and which obligations they should obey to ensure that AI systems will properly and successfully perform the tasks assigned to them. Last but not least, the board of directors must ensure the company’s IT-security when using AI.

Initial Exploration of Cross-Generational Attitudes Towards Piracy of Chinese Online Literature

This chapter seeks to provide initial evidence and provide a baseline for further exploration of Chinese cross-generational audiences' attitude differences towards online literature and digital piracy. Globalization has complicated the many disparate cultural, generational, and national perspectives on intellectual property (IP) protection. IP and IP protection continue to grow in importance in global commerce and international relations. How attitudes towards IP and online content, in particular, evolve generationally is an area of relative under exploration. Data was gathered through an online survey and indicates a trend towards increased awareness and acceptance of IP value and protection. This study provides insight into cross-generational audiences in the important market of China. It may be helpful to those interested in commerce in the areas of online publishing or related industries to help make business decisions in targeting and marketing, to those interested in global commerce and international relations, or those who are researchers in the areas of IP and IP protection.

Social Media in Business Decisions of MSMES

Social media has progressively grown in the last century and is now seen as a potential opportunity for various purposes, including the decision-making. Present work explores how social media platforms such as Facebook, Twitter, and Instagram etc. can be used to support the decision making process of MSMEs. The work is exploratory in nature and relevant literature has been reviewed to identify the decision making approaches at different managerial levels and how they have been integrated with the social media applications. Specific examples of Social media platforms have been discussed, considering the MSMEs’ business environment. Along with the practices, most important challenges to social media integration have also been presented.

Data-driven method for mobile game publishing revenue forecast

Games as a service is similar to software as a service, which provides players with game content on a continuous monetization model. Game revenue forecast is vital to game developers to make the right business decisions, such as determining the marketing budget, controlling the development cost, and setting up benchmarks for evaluating game publishing performance. How to make the revenue forecast and integrate it with the game publishing process is hard for small and medium-sized independent (indie) game developers. This includes all steps of the process, from forecasting to decision-making based on the results. This paper provides a data-driven method that uses the mobile game revenue forecast based on different time-series prediction models to drive the game publishing. We demonstrate how to use the data-driven method to guide an indie game studio to forecast revenue and then set the revenue forecast as the internal benchmark to drive game publishing. In practice, we involve a real game project from an indie game studio and provide guidance for one of their casual game projects. Then, based on the revenue forecast, we discuss how to set the revenue forecast as an internal benchmark and drive the actions for mobile game publishing. Finally, we make a conclusion on how our data-driven method can be used to drive mobile game publishing and also discuss future research work.