Organizational Learning in the Rise of Machine Learning

Organizational learning (OL) is associated with experience and knowledge in an organization. Information Technology (IT) enables the creation, dissemination, and use of knowledge, and as such, plays an important role in an organization’s learning process. This role has inspired a large body of literature studying the link between OL and IT and the relation between IT and knowledge exploration and exploitation. The recent rise of Machine Learning (ML) with its Deep Learning (DL) capabilities has nevertheless brought about new ways of creating, retaining, and transferring knowledge. I argue that the learning occurring within the machine plays a role in the learning occurring within the organization, calling for revisiting OL in light of this disruptive IT. In this paper, I focus on three different ways in which the machine achieves its learning, namely supervised, unsupervised, and reinforcement learning, and advance propositions on how each impacts OL differently

Digital Technologies for Digital Innovation: Unlocking Data and Knowledge to Drive Organizational Value Creation

The rise of digitization has radically transformed innovation processes of today's companies and is increasingly challenging existing theories and practices. Digital innovation can describe both the use of digital technologies during the innovation process and the outcome of innovation. This thesis aims to improve the understanding of digital innovation in today's digitized world by contributing to the theoretical and practical knowledge along the four organizational activities of the digital innovation process: initiation, development, implementation, and exploitation. In doing so, the thesis pays special attention to the use of digital technologies and tools (e.g., machine learning, online crowdsourcing platforms, etc.) that unlock knowledge and data to facilitate new products, services, and other value streams. When initiating digital innovations, organizations seek to identify, assimilate, and apply valuable knowledge from within and outside the organization. This activity is crucial for organizations as it determines how they address the increasing pressure to innovate in their industries and markets while innovation processes themselves are changing and becoming more distributed and open. Papers A and B of this thesis address this phase by examining how digital technologies are changing knowledge gathering, e.g., through new ways of crowdsourcing ideas and facilitating cooperation and collaboration among users and innovation collectives. Paper A focuses on organizational culture as a critical backdrop of digital innovations and explores whether it influences the implementation of idea platforms and, in this way, facilitates the discovery of innovations. The paper reveals that the implementation of idea platforms is facilitated by a culture that emphasizes policies, procedures, and information management. Additionally, the paper highlights the importance of taking organizational culture into account when introducing a new technology or process that may be incompatible with the existing culture. Paper B examines newly formed innovation collectives and initiatives for developing ventilators to address shortages during the rise of the COVID-19 pandemic. The paper focuses on digital technologies enabling a transformation in the way innovation collectives form, communicate, and collaborate - all during a period of shutdown and social distancing. The paper underlines the role of digital technologies and collaboration platforms through networking, communication, and decentralized development. The results show that through the effective use of digital technologies, even complex innovations are no longer developed only in large enterprises but also by innovation collectives that can involve dynamic sets of actors with diverse goals and capabilities. In addition, established organizations are increasingly confronted with community innovations that offer complex solutions based on a modular architecture characteristic of digital innovations. Such modular layered architectures are a critical concept in the development of digital innovations. This phase of the digital innovation process encompasses the design, development, and adoption of technological artifacts, which are explored in Sections C and D of this paper. Paper C focuses on the latter, the adoption of digital services artifacts in the plant and mechanical engineering industry. The paper presents an integrative model based on the Technology-Organization-Environment (TOE) framework that examines different contextual factors as important components of the introduction, adoption, and routinization of digital service innovations. The results provide a basis for studying the assimilation of digital service innovations and can serve as a reference model for informing managerial decisions. Paper D, in turn, focuses on the design and development of a technology artifact. The paper focuses on applying cloud-based machine learning services to implement a visual inspection system in the manufacturing industry. The results show, for one, the value of standardization and vendor-supplied IS architecture concepts in digital innovation and, for another, how such innovations can facilitate further innovations in manufacturing. The implementation of digital innovations marks the third phase of the digital innovation process, which is addressed in Paper E. It encompasses organizational changes that occur during digital innovation initiatives. This phase emphasizes change through digital innovation initiatives within the organization (e.g., strategy, structure, people, and technology) and across the organizational environment. Paper E investigates how digital service innovations impact industrial firms, relationships between firms and their customers, and product/service offerings. The paper uses work systems theory as a theoretical foundation to structure the results and analyze them through the lens of service systems. While this analysis helps to identify the organizational changes that result from the implementation of digital innovations, the paper also provides a basis for further research and supports practitioners with systematic analyses of organizational change. The last phase of the digital innovation process is about exploiting existing systems/data for new purposes and innovations. In this regard, it is important to better understand the improvements and effects in the domains beyond the sheer outcome of digital innovation, such as organizational learning or organizational change capabilities. Paper F of this thesis investigates the exploitation of digital innovations in the context of organizational learning. One aspect of this addresses how individuals within the organization leverage innovation to explore and exploit knowledge. Paper F utilizes the organizational learning perspective and examines the dynamics of human learning and machine learning to understand how organizations can benefit from their respective idiosyncrasies in enabling bilateral learning. The paper demonstrates how bilateral human-machine learning can improve the overall performance using a case study from the trading sector. Drawing on these findings, the paper offers new insights into the coordination of human learning and machine learning, and moreover, the collaboration between human and artificial intelligence in organizational routines

Artificial Intelligence, International Competition, and the Balance of Power (May 2018)

World leaders, CEOs, and academics have suggested that a revolution in artificial intelligence is upon us. Are they right, and what will advances in artificial intelligence mean for international competition and the balance of power? This article evaluates how developments in artificial intelligence (AI) — advanced, narrow applications in particular — are poised to influence military power and international politics. It describes how AI more closely resembles “enabling” technologies such as the combustion engine or electricity than a specific weapon. AI’s still-emerging developments make it harder to assess than many technological changes, especially since many of the organizational decisions about the adoption and uses of new technology that generally shape the impact of that technology are in their infancy. The article then explores the possibility that key drivers of AI development in the private sector could cause the rapid diffusion of military applications of AI, limiting first-mover advantages for innovators. Alternatively, given uncertainty about the technological trajectory of AI, it is also possible that military uses of AI will be harder to develop based on private-sector AI technologies than many expect, generating more potential first-mover advantages for existing powers such as China and the United States, as well as larger consequences for relative power if a country fails to adapt. Finally, the article discusses the extent to which U.S. military rhetoric about the importance of AI matches the reality of U.S. investments.LBJ School of Public Affair

Biases in human behavior

The paper shows that biases in individual’s decision-making may result from the process of mental editing by which subjects produce a “representation” of the decision problem. During this process, individuals make systematic use of default classifications in order to reduce the short-term memory load and the complexity of symbolic manipulation. The result is the construction of an imperfect mental representation of the problem that nevertheless has the advantage of being simple, and yielding “satisficing” decisions. The imperfection origins in a trade-off that exists between the simplicity of representation of a strategy and his efficiency. To obtain simplicity, the strategy’s rules have to be memorized and represented with some degree of abstraction, that allow to drastically reduce their number. Raising the level of abstraction with which a strategy’s rule is represented, means to extend the domain of validity of the rule beyond the field in which the rule has been experimented, and may therefore induce to include unintentionally domains in which the rule is inefficient. Therefore the rise of errors in the mental representation of a problem may be the "natural" effect of the categorization and the identification of the building blocks of a strategy. The biases may be persistent and give rise to lock-in effect, in which individuals remain trapped in sub-optimal strategies, as it is proved by experimental results on stability of sub-optimal strategies in games like Target The Two. To understand why sub-optimal strategies, that embody errors, are locally stable, i.e. cannot be improved by small changes in the rules, it is considered Kauffman’ NK model, because, among other properties, it shows that if there are interdependencies among the rules of a system, than the system admits many sub-optimal solutions that are locally stable, i.e. cannot be improved by simple mutations. But the fitness function in NK model is a random one, while in our context it is more reasonable to define the fitness of a strategy as efficiency of the program. If we introduce this kind of fitness, then the stability properties of the NK model do not hold any longer: the paper shows that while the elementary statements of a strategy are interdependent, it is possible to achieve an optimal configuration of the strategy via mutations and in consequence the sub-optimal solutions are not locally stable under mutations. The paper therefore provides a different explanation of the existence and stability of suboptimal strategies, based on the difficulty to redefine the sub-problems that constitute the building blocks of the problem’s representation

Policy, paradigms, and partnership potential: rethinking the governance of learning networks

**IP Unitl May 3**This paper engages with the idea of ‘joining-up’ as an increasingly common policy response by governments internationally in the face of so-called ‘wicked problems’ (Rittel and Webber 1973). In particular, the paper concerns the problem of young people in transition from a primary role in engaging with and progressing through the levels of formal education to a sustainable engagement in the increasingly fragmented labor markets that are the motor of individualized risk in the context of the risk society (Beck 1992). Drawing on seminal organizational theory, the paper takes up a a metaphorical lens to critique the governance arrangements that have evolved in concert with such policy responses. The paper proceeds in the following stages. Firstly, the problem of youth transition and its interface with socio-economic factors will be framed. Secondly, the policy response introduced in the research context ¬¬— the state of Victoria in Australia — will be sketched. Finally, the metaphorical underpinnings of the governance arrangements that were implemented as part of the policy will be critiqued. The paper closes with some thoughts for reflection