Walter Brenner

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.