The Good, the Bad, and the Dynamic: Changes to Retail Business Models During COVID-19

Crises, such as the COVID-19 pandemic, challenge the economy and require firms to become resilient to external change. During COVID-19, the retail industry faced doubleedged consequences. While brick and mortar business models (BMs) were discontinued, online retail thrived. Extant BM research has investigated several crises; however, it still lacks an explanation of how BM change increases resilience to cope with crises. We analyze the BMs of 45 European retailers and the BM changes implemented during the COVID-19 pandemic and their influence on the retailers\u27 revenue. We identify three types of retailers implementing different strategies to cope with the crises: the »good,« the »bad,« and the »dynamic.« These represent resilient BMs, un-resilient BMs, and BMs becoming resilient enabled by digital technology. We show how BM change creates resilience and performance benefits. For practice, we show how retailers adapted their BM to a crisis leveraging digital technology

Competing in the Clouds: A Strategic Challenge for ITSP Ltd.

By 2010, cloud computing had become established as a new model of IT provisioning for service providers. New market players and businesses emerged, threatening the business models of established market players. This teaching case explores the challenges arising through the impact of the new cloud computing technology on an established, multinational IT service provider called ITSP. Should the incumbent vendors adopt cloud computing offerings? And, if so, what form should those offerings take? The teaching case focuses on the strategic dimensions of technological developments, their threats and opportunities. It requires strategic decision making and forecasting under high uncertainty. The critical question is whether cloud computing is a disruptive technology or simply an alternative channel to supply computing resources over the Internet. The case challenges students to assess this new technology and plan ITSP’s responses

Value Drivers of Artificial Intelligence

Artificial intelligence (AI) holds great potential for firms to create new business models and gain competitive advantages. While some pioneers are effectively leveraging AI, most firms are struggling to capitalize on the opportunities for value creation. Previous research has highlighted the performance benefits, success factors, and challenges of adopting AI. However, the value drivers of AI, specifically regarding how AI creates value, remain unclear and need exploration so that firms can adapt their value creation to leverage the potential. To clarify how AI creates value, we conduct a case survey of 61 firms to identify six value drivers: efficiency, novelty, knowledge from data, ecosystem, personalization, and human resemblance. We discuss how these value drivers differ from other digital technologies. For practitioners, we provide valuable insights into the business value of AI and business model (BM) design opportunities to build on

The trough-system algorithm and its application to spatial modeling of Greenland subglacial topography

This is the published version. Copyright 2014 Herzfeld et al.Dynamic ice-sheet models are used to assess the contribution of mass loss from the Greenland ice sheet to sea-level rise. Mass transfer from ice sheet to ocean is in a large part through outlet glaciers. Bed topography plays an important role in ice dynamics, since the acceleration from the slow-moving inland ice to an ice stream is in many cases caused by the existence of a subglacial trough or trough system. Problems are that most subglacial troughs are features of a scale not resolved in most ice-sheet models and that radar measurements of subglacial topography do not always reach the bottoms of narrow troughs. The trough-system algorithm introduced here employs mathematical morphology and algebraic topology to correctly represent subscale features in a topographic generalization, so the effects of troughs on ice flow are retained in ice-dynamic models. The algorithm is applied to derive a spatial elevation model of Greenland subglacial topography, integrating recently collected radar measurements (CReSIS data) of the Jakobshavn Isbræ, Helheim, Kangerdlussuaq and Petermann glacier regions. The resultant JakHelKanPet digital elevation model has been applied in dynamic ice-sheet modeling and sea-level-rise assessment

Sharing data from molecular simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations