Jochen Andreas Wulf

Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three socio-technical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption.

Online firestorms pose severe threats to online brand communities. Any negative electronic word of mouth (eWOM) has the potential to become an online firestorm, yet not every post does, so finding ways to detect and respond to negative eWOM constitutes a critical managerial priority. The authors develop a comprehensive framework that integrates different drivers of negative eWOM and the response approaches that firms use to engage in and disengage from online conversations with complaining customers. A text-mining study of negative eWOM demonstrates distinct impacts of high and low arousal emotions, structural tie strength, and linguistic style match (between sender and brand community) on firestorm potential. The firm’s response must be tailored to the intensity of arousal in the negative eWOM to limit the virality of potential online firestorms. The impact of initiated firestorms can be mitigated by distinct firm responses over time, and the effectiveness of different disengagement approaches also varies with their timing. For managers, these insights provide guidance on how to detect and reduce the virality of online firestorms.

“Understanding the value and organizational implications of big data analytics: the case of AUDI AG” presents the case of AUDI AG and its attempts to implement big data analytics in its organization. The case highlights the situation of an original equipment manufacturer (OEM) in the automotive industry and the potentials and challenges the emerging technology big data analytics may entail for such organizations. The case tries to help students to grasp the technical characteristics, the value, and organizational implications of big data analytics as well as the distinct types of analytics services. The case is presented through the eyes of Hortensie, an aspiring manager at AUDI, who gained strong interest in the phenomenon of big data analytics and received the task to position it within AUDI. To ramp up the topic big data analytics, AUDI is engaging with industry and design experts as well as an external consultancy ITConsult.

Digital transformation, which often includes establishing big data analytics capabilities, poses considerable challenges for traditional manufacturing organizations, such as car companies. Successfully introducing big data analytics requires substantial organizational transformation and new organizational structures and business processes. Based on the three-stage evolution of big data analytics capabilities at AUDI, we provide recommendations for how traditional manufacturing organizations can successfully introduce big data analytics and master the related organizational transformations.

New digital services in consumer-facing organizations offer novel value propositions, closer consumer relationships and higher automation of consumer-facing processes. But transforming to fully digital services requires an organization to acquire specific capabilities. This article presents a digital services capability model that allows an organization to assess its current capabilities and identify gaps. Two in-depth case studies demonstrate the application of the model and show how it identifies the capabilities in urgent need of improvement. Our recommendations are built around four scenarios for using the model.