Leaving behind what we are not. Applying a systems thinking perspective to present unlearning as an enabler for finding the best version of the self

In response to criticism on the concept of "unlearning", we suggested that unlearning on an individual level should be defined as the reduction of the influence of old knowledge on cognitive and/or behavioural processes. In this article, we apply a systems thinking perspective on this definition to explore how far this kind of unlearning can possibly go and what happens if this process is inward-directed, i.e. affects the cognitive and behavioural patterns that define who we are. We take a knowledge perspective on the concept of the self and suggest that unlearning could trigger a disequilibrium, which in turn, enables a deep learning process and guides us to what is referred to as ideal or best version of the self. This does not only have implications for the individual level but it can initiate fundamental change processes in organizations

Dynamics of Human-AI Delegation in Organizational Routines

Human-AI delegation occurs when a human delegates work to an autonomous AI-based system. We report on a simulation study to examine how human-AI delegation dynamically changes in an organizational routine as it is enacted over and over again. We build on findings from previous research and examine the interaction of various human and AI-related factors. We compute the resulting dynamics in terms of complexity representing the degree of uncertainty as to whether delegation takes place. We find that online and offline learning capabilities interact with human willingness in various ways which leads to different, even non-linear changes in the dynamics of human-AI delegation over time. Our study yields implications for research on human-AI delegation, routine dynamics and business process management. We point to a number of practical implications and avenues for future research

Studying the Co-evolution of Individual Actions and Emergent Social Structures using Digital Trace Data

The information systems field has a long-standing interest in how individual actions co-evolve with social structures. Yet, studying the exact process of co-evolution turned out to be elusive. We propose a novel way to study this co-evolution using digital trace data. By analyzing the sequence of individual actions through digital trace data and the process of emergent social structuring expressed in collective action patterns, we can measure the recursive influence of individual actions and the process of emergent social structuring over time. We illustrate our approach using data from GitHub. We analyze the social structuring expressed through collective action patterns of a project and compare them with the idiosyncratic action patterns of individual developers. Our research has implications for studies that examine the connection between social structures and individual actions. Our approach particularly allows us to investigate the role of power and social influence in structuration processes, which has been typically neglected in existing research

Cascading Digital Options and the Evolution of Digital Infrastructures: The Case of IIoT

Digital infrastructures provide a space where possibilities for innovation continuously emerge. They are not stable entities but are evolving. Their boundaries are subject to constant negotiation among multiple organizational actors as well as changing connections of digital technologies, operations, and users. In this paper, we explore the evolution of an Industrial Internet of Things (IIoT) infrastructure in a leading manufacturing company. We find that the IIoT infrastructure provided actionable spaces upon which organizational actors discovered opportunities for improving process performance which, in turn, led to investment decisions. We explain this process through the lens of digital options theory and highlight how IIoT infrastructure provides the material foundation for the identification of digital options, how the realization of digital options leads to the emergence of more digital options, and how these “cascading” digital options are implicated in the evolution of IIoT infrastructure. We discuss theoretical and practical implications

Modular Change in Platform Ecosystems and Routine Mirroring in Organizations

Organizational routines involve modular digital technologies that are part of larger platform ecosystems that often transcend organizational boundaries. Change in organizational routines is thus interwoven with innovation and associated change in digital platforms. To get at this “embedded” routine change, we use the concept of modular operators to conceptualize how changes to digital technologies in platform ecosystems are mirrored in changes in the organizational routines in which these technologies are implicated. We distinguish between enabling and constraining impacts and develop a set of propositions to move towards a theory of “routine mirroring.” We use the Industrial Internet of Things (IIoT) as a base example

Unlearning before Creating new Knowledge: A Cognitive Process.

Recent research expresses serious doubts on the \ concept of unlearning. It is argued that knowledge \ cannot be discarded or eliminated in order to make \ space for the creation of new knowledge. Taking into \ account the recent scepticism, we focus on the \ cognitive dimension of unlearning and propose an \ alternative conceptualization. Considering how far \ unlearning can go from a psychological/cognitive \ scientific perspective, we propose that unlearning is \ about reducing the influence of old knowledge on our \ cognitive capacity. This study: (a) investigates the \ unlearning process within the cognitive domain and \ on an individual level and (b) proposes unlearning \ process triggers that detract or facilitate the \ knowledge change process, which could subsequently \ contribute to unlearning on an organizational level

STUDYING DYNAMICS AND CHANGE WITH DIGITAL TRACE DATA: A SYSTEMATIC LITERATURE REVIEW

Digital trace data offer promising opportunities to study dynamics and change of various socio-technical phenomena over time. While we see a surge of empirical and conceptual articles, we lack a systematic understanding of why, how, and when digital trace data are or can be used to study dynamics and change. In this article, we present the findings of a systematic literature review to uncover common approaches, motivations, findings, and general themes in the existing literature. We systematically reviewed 40 studies that were published in premium outlets in the information systems field. Our review sheds light on (1) underlying purposes of such studies, (2) utilized data sources, (3) research contexts, (4) socio-technical phenomena of interest, (5) applied analytical methods, and (6) measures that are being used. Building on our findings, we point to several implications for research and shed light on avenues to advance this field in the future

Towards a prioritization of needs to support decision making in organizational change processes

A focus on needs and the ability to generate knowledge about needs is highly valuable for organizations because it extends the range of possible solutions and therefore enables them to create more innovative and sustainable products and services. Our paper will explore how a framework based on an abductive reasoning process for the creation and discovery of knowledge about needs in organizations can look like and what the main steps of such a framework are, in order to integrate this approach into the model of the knowledge-based firm. Moreover we will present empirical findings from a project with Austrian companies where this framework has been used

Learning from an Envisioned Future - An empirical account

Innovation processes require organizations to transcend current boundaries. These include not only technological as well as social limitations but "above all" the way we address the future. We are used to face the future with our existing knowledge and experiences from the past. This strategy, however, can hardly lead to knowledge off the beaten path. We therefore suggest a new learning approach for organizations, which enables to literally envision a desired future scenario and thereby, allows for the creation of radical new knowledge. We argue that the created knowledge yields a higher degree of novelty and radicalness. Along with an enhanced theory of learning including learning from the future, we present our empirical findings from comparing the outputs of Learning from an Envisioned Future and learning from the past. For this purpose, we use data from two organizational learning projects; one, which was conducted with a high school in Austria and another one, which was conducted with members of the Austrian Economic Chamber. Our findings from both case studies suggest that Learning from an Envisioned Future does produce significantly more paradigm challenging knowledge compared to the output gained from conventional learning from past experiences. We conclude that the combination of both learning sources may lead to best learning outcomes in organizations