Christian Dremel

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.

Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems.

Digital and agile companies widely use chatbots in the form of integrations into enterprise messengers such as Slack and Microsoft Teams. However, there is a lack of empirical evidence about their action possibilities (i.e., affordances), for example, to link social interactions with third-party systems and processes. Therefore, we adopt a three-stage process. Grounded in a preliminary study and a qualitative study with 29 interviews from 17 organizations, we inductively derive rich contextual insights of 14 affordances and constraints, which serve as input for a Q-Methodology study that highlights five perceptional differences. We find that actualizing these affordances leads to higher-level affordances of chatbots that augment social information systems with affordances of traditional enterprise systems. Crossing the chasm between these, so far, detached systems contributes a novel perspective on how to balance novel digital with traditional systems, flexibility and malleability with stability and control, exploration with exploitation, and agility with discipline.

“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.

The pervasive infiltration of digital technology into physical products brings both tremendous challenges and opportunities to original equipment manufacturers. With the goal to support the initial stages of the product lifecycle, this article introduces a method compendium for the development of digitized products. More precisely, the compendium suggests (1) customer- and user-centric innovation methods, (2) agile and prototyping methods, (3) system and architecture modelling methods, (4) feedback- and data-driven methods, and (5) service and business modelling methods. Methodically, we draw on secondary data from a longitudinal single-case study scrutinizing the development of digitized trucks at a leading materials handling and intralogistics organization. Bounded to the business-to-business context of industrial equipment manufacturing, we enrich product lifecycle management with methodological contributions valuable for academia and practice alike.

Recent developments in the insurance industry embrace various “Insurance Technology” (InsurTech) innovations. To date, there is a lack of structured assessments of InsurTech. Prior research on FinTech fails (1) to clarify how InsurTech can be characterized and what capabilities are employed, and hence, (2) to reveal implications for value creation on firm and industry level. We address this by inductively building a model of InsurTech innovation adopting the grounded theory method. Our empirical data includes 208 InsurTech innovations from a market analysis based on Twitter data and a multiple-case study. The resulting model comprises 52 characteristics and 14 transformational capabilities and is integrated with extant value networks and intermediation literature. The former explains how InsurTech affects firm-level value creation and suggests that disruptive potentials emerge from aligning the transformational capabilities along three interdependent activities. The latter explains the entrance of digital intermediaries and their roles in the personal insurance market.

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.