Welcome to 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 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 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.

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Although sustainable finance (SF) has become a leading trend in the financial industry, little is known about how attention to news on SF, trust in the industry, and recent accusations of greenwashing affect the likelihood to invest in SF products. Based on a survey of a representative sample of Swiss citizens, we find that more attention to news about SF and trust in SF are positively related to the likelihood of investing in SF, whereas greenwashing perceptions are negatively related. Furthermore, attention to SF and economic news are positive predictors of sustainable finance literacy (SFL), whereas attention to SF news is negatively associated with greenwashing perception of SF. Collectively, these findings imply that engaging citizens with SF investments, particularly information seeking on SF news and trust in SF rather than SFL, need to be fostered. The Implications of the communication practices of the SF industry and policy implications are discussed.

We analyze the relationship between social gender norms and adolescents' occupational choices by combining regional votes on constitutional amendments on gender equality with job application data from a large job board for apprenticeships. The results show that adolescent males in regions with stronger traditional social gender norms are more likely to apply for typically male occupations. This finding does not hold for females, suggesting that incentivizing men to break the norms and choose gender-atypical occupations (e.g., in healthcare) can be even more effective in accelerating advancement toward gender equality in the labor market than incentivizing women to choose STEM occupations.

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Government as a Platform (GaaP) is a promising approach to the digital transformation of the public sector. In practice, GaaP is realized by platform-oriented infrastructures. However, despite successful examples, the transformation toward platform-oriented infrastructures remains challenging. A potential remedy is the analysis of existing public infrastructure regarding its platform orientation. Such an analysis can identify the gaps to an ideal platform-oriented infrastructure and, thus, support the transformation toward it. We follow the design science research methodology to develop a four-dimensional analysis method. We do so in three iterations, and, after each iteration, evaluate the method by its application to infrastructures in practice. With regard to theory, our results suggest extending GaaP conceptualizations with a specific emphasis on platform principles. With regard to practice, we contribute an analysis method that creates proposals for the improvement of infrastructures and, thus, supports the transformation toward GaaP.

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Endogeneity is a common problem in any causal analysis. It arises when the independence assumption between an explanatory variable and the error in a statistical model is violated. The causes of endogeneity are manifold and include response bias in surveys, omission of important explanatory variables, or simultaneity between explanatory and response variables. Instrumental variable estimation provides a possible solution. However, valid and strong external instruments are difficult to find. Consequently, internal instrumental variable approaches have been proposed to correct for endogeneity without relying on external instruments. The R package REndo implements various internal instrumental variable approaches, i.e., latent instrumental variables estimation (Ebbes, Wedel, Boeckenholt, and Steerneman 2005), higher moments estimation (Lewbel 1997), heteroscedastic error estimation (Lewbel 2012), joint estimation using copula (Park and Gupta 2012) and multilevel generalized method of moments estimation (Kim and Frees 2007). Package usage is illustrated on simulated and real-world data.