Contributions in Merlin

A big part of software developers’ time is spent finding answers to their coding-task-related questions. To answer their questions, developers usually perform web searches, ask questions on Q&A websites, or, more recently, in chat communities. Yet, many of these questions have frequently already been answered in previous chat conversations or other online communities. Automatically identifying and then suggesting these previous answers to the askers could, thus, save time and effort. In an empirical analysis, we first explored the frequency of repeating questions on the Discord chat platform and assessed our approach to identify them automatically. The approach was then evaluated with real-world developers in a field experiment, through which we received 142 ratings on the helpfulness of the suggestions we provided to help answer 277 questions that developers posted in four Discord communities. We further collected qualitative feedback through 53 surveys and 10 follow-up interviews. We found that the suggestions were considered helpful in 40% of the cases, that suggesting Stack Overflow posts is more often considered helpful than past Discord conversations, and that developers have difficulties describing their problems as search queries and, thus, prefer describing them as natural language questions in online communities.

We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models improve predictability, not only because of the combination of predictors with different frequencies but also due to the preservation of high-frequency features such as time-varying volatility. Temporally aggregated models misspecify the evolution frequency of the volatility dynamics, resulting in poor volatility timing and worse portfolio performance than the mixed-frequency specification. These results highlight the importance of preserving the potential mixed-frequency nature of predictors and volatility in predictive regressions.

We replicate the finding of Lefgren et al. (2015) showing that professional basketball coaches in the NBA discontinuously change their starting lineup more often after narrow losses than after narrow wins. This result is consistent with outcome bias because such narrow outcomes are conditionally uninformative. As our paper shows, this pattern is not restricted to the NBA; we also find evidence of outcome bias in the top women’s professional basketball league and college basketball. Finally, we show that outcome bias in coaching decisions generalizes to the National Football League (NFL). We conclude that outcome bias is credible and robust, although it has weakened over time in some instances.

In 5G and beyond, the figure of cross-operator/domain connections and relationships grows exponentially among stakeholders, resources, and services, being reputation-based trust models one of the capital technologies leveraged for trustworthy decision-making. This work studies novel 5G assets on which trust can be used to overcome unsuitable decision-making and address current requirements. First, it introduces a background and general architecture of reputation-based trust models. Afterward, it analyzes pivotal 5G assets on which trust can enhance their performance. Besides, this article performs a comprehensive review of the current reputation models applied to 5G assets and compares their properties, features, techniques, and results. Finally, it provides current trends and future challenges to conducting forthcoming research in the area.

We analyze how individual investors respond to inflation. We introduce a unique dataset containing information on local inflation and security portfolios of more than 2,000 clients of a German bank between 1920 and 1924, covering the German hyperinflation. We find that individual investors buy less (sell more) stocks when facing higher local inflation. This effect is more pronounced for less sophisticated investors. Moreover, we document a positive relation between local inflation and forgone returns following stock sales. Our findings are consistent with individual investors suffering from money illusion. Alternative explanations such as consumption needs are unlikely to drive our results.

Hippocampal activity linking past experiences and simulations of the future with current goals can play an important role in decision-making. The representation of information within the hippocampus may be especially critical in situations where one needs to overcome past rewarding experiences and exert self-control. Self-control success or failure may depend on how information is represented in the hippocampus and how effectively the representation process can be modified to achieve a specific goal. We test this hypothesis using representational similarity analyses of human (female/male) neuroimaging data during a dietary self-control task in which individuals must overcome taste temptations to choose healthy foods. We find that self-control is indeed associated with the way individuals represent taste information (valance) in the hippocampus and how taste representations there adapt to align with different goals/contexts. Importantly, individuals who were able to shift their hippocampal representations to a larger degree to align with the current motivation were better able to exert self-control when facing a dietary challenge. These results suggest an alternative or complementary neurobiological pathway leading to self-control success and indicate the need to update the classical view of self-control to continue to advance our understanding of its behavioral and neural underpinnings.

Die Hinterziehung von Steuern soll durch den international eingeführten automatischen Informationsaustausch (AIA) aufgedeckt und eingedämmt werden. Zudem besteht in der Schweiz seit 2010 die straflose Selbstanzeige. Im Zusammenhang mit der internationalen Einführung des AIA ist ein deutlicher Anstieg der straflosen Selbstanzeigen zu beobachten. Dabei werden erhebliche kantonale Unterschiede sichtbar. So ist die Anzahl strafloser Selbstanzeigen pro zehntausend Steuerpflichtige in Kantonen mit hohen Vermögensteuern, getrieben durch die bevölkerungsstarken Genferseekantone, nach der Einführung des AIA tendenziell höher. Da das Selbstanzeigeverhalten im Zuge des AIA vorwiegend ausländisches Vermögen und Einkommen betrifft, könnte auch der Anteil der zugewanderten Wohnbevölkerung eine Rolle spielen.

In the digital paradigm, collaborative value creation takes place across organisational boundaries while the consumer moves to the centre. Value will be created in the future through the combination of artificial intelligence, augmented reality, the internet of things, blockchain, cloud and quantum computing, and the customer journey will span different sectors. The golden triangle of digital ecosystems includes commerce, social media, and finance. Companies that understand the benefits of the platform economy can achieve exponential growth and become winners of the digital transformation. They can share knowledge and resources across organisational boundaries, react quickly to market changes and find the balance between existing businesses and new markets. The future of work and leadership in open and digital ecosystems requires rethinking and new dynamic capabilities such as openness, agility, and ambidexterity. The book is based on years of research. It expands traditional management models with pragmatic strategy and action concepts, which are explained with diverse practical examples of tech companies from the USA, cross-industry ecosystems from China and healthcare platforms from Switzerland.

To remain competitive, organizations tend to change their established ways of working, their strategy, the core values, and the organizational structure. Such thorough changes are referred to as transformational change. Unfortunately, transformational change is often unsuccessful because organizational members do not always welcome the change. Although organizations often expect their supervisors to be successful role-models and change-agents during the transformational change process, we argue that initiating transformational change could increase supervisors' hindrance stress levels, which may result in abusive behaviors towards employees. More specifically, in a multi-source survey and an experimental study, we find evidence that transformational change is associated with supervisors' experienced hindrance stress, which subsequently led to more abusive behaviors towards employees.