Research on business analytics (BA) has made various incongruent assumptions about how BA systems are applied in business processes and how they create value. Meanwhile, machine learning (ML) is changing how BA systems create value, but research streams on ML and BA have yet to come to a shared view on process-level value creation. In this theoretical literature review, we draw on the Information Value Chain (IVC) to synthesize 176 articles from top journals and present a model of process-level value creation from BA systems. We contextualize and extend the IVC with six distinct value creation paths, explain how the characteristics of ML-based BA systems change value creation paths, and propose a research agenda.

In today’s work and life, the use of video meeting systems is ubiquitous. As usage continues to rise, the negative effects of video meeting systems on users have become apparent. Consequently, scholars and public media have called for a better comprehension of the impact of video meeting systems on users with respect to psychological user states and ensuing outcomes. However, a synopsis of existing empirically grounded knowledge in this field is non-existent. To fill this gap, we review existing literature with a focus on psychological user states from a cognitive and affective perspective as well as downstream outcomes. Specifically, we review and conceptualize findings from 78 quantitative studies and describe the results in a morphological box. We identified a focus on examining the overall systems’ impact on the user states of attention, awareness, and negative emotions. Moreover, there has been a rise in literature examining recently developed features that concentrate on supporting attention and emotion understanding. Besides, video meeting systems have been predominantly explored in the context of generic conversations in work settings. By providing future research directions, this overview offers scholars the potential to design their studies more effectively and informs designers to facilitate system improvements based on empirical findings.

In the light of the constant battle for attention on digital media, animating digital content plays an increasing role in modern graphic design. In this study, we use artificial intelligence methods to create aesthetic animations along the case of brand logos. With scalable vector graphics as the standard format in modern graphic design, we develop an autonomous end-to-end method using complex machine learning techniques to create brand logo animations as scalable vector graphics from scratch. We acquire data and setup a comprehensive animation space to create novel animations and evaluate them based on their aesthetics. We propose and compare two alternative computational models for automated logo animation and carefully weigh up their idiosyncrasies: on the one hand, we set up an aesthetics evaluation model to train an animation generator and, on the other hand, we combine tree ensembles with global optimization. Indeed, our proposed methods are capable of creating aesthetic logo animations, receiving an average rating of ‘good’ from observers.

Achieving effective inter-team coordination is one of the most pressing challenges in large-scale software development. Hybrid approaches of traditional and agile development promise combining the overview and predictability of long-term planning on an inter-team level with the flexibility and adaptability of agile development on a team level. It is currently unclear, however, why such hybrids often fail. Our case study within a large software development unit of 13 teams at a global enterprise software company explores how and why a combination of traditional planning on an inter-team level and agile development on a team level can result in ineffective coordination. Based on a variety of data, including interviews with scrum masters, product owners, architects and senior management, and using Grounded Theory data analysis procedures, we identify a lack of dependency awareness across development teams as a key explanation of ineffective coordination. Our findings show how a lack of dependency awareness emerges from misaligned planning activities of specification, prioritization, estimation and allocation between agile team and traditional inter-team levels and ultimately prevents effective coordination. Knowing about these issues, large-scale hybrid projects in similar contexts can try to better align their planning activities across levels to improve dependency awareness and in turn achieve more effective coordination.

Software development is becoming more and more complex. Traditionally and to date, the software development process rather corresponds to job-shop manufacturing. Therefore, the ever growing demands for different kinds of software as well as the ongoing globalization require more efficient development processes. Both scientific literature and practical experience hence postulate a necessary industrialization of software development and design of novel forms of specialization, task distribution, and collaboration. Existing approaches to collaborative software development can be classified and analyzed according to multiple categories. By evaluating these, current deficiencies are identified and discussed for further investigation.

When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under- or over-specified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors. This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.