The metaverse is considered by some to be the next big thing in the global economy. Despite technological progress and high expectations, scholars agree that a broader metaverse platform does not yet exist. Instead, it has been suggested that the metaverse exists mainly as an imaginary or a representation in various discursive contexts. However, studies that examine such representations are few, and the present study aims to – through the lens of social representation theory – critically explore how VR technology and the metaverse are discursively represented in Swedish news media. The analysis identifies three underlying themata: (a) The inevitability of technology, (b) The commodification of experience, and (c) The world of male pioneers. It is concluded that the news media?s representation of VR and the metaverse contributes greatly to maintaining the structural status quo. For traditional journalism to fulfill its democratic mission, a pervasive update of media logic is needed.

Purpose

The purpose of this study is to explore the process of initial sensemaking that organizational members activate when they reflect on AI adoption in their work settings, and how the perceived features of AI technologies trigger sensemaking processes which in turn have the potential to influence workplace learning modes and trajectories.

Design/methodology/approach

We adopted an explorative qualitative and interactive approach to capture free fantasies and imaginative ideas of AI among people within the industry. We adopt a conceptual perspective that combines theories on initial sensemaking and workplace learning as a theoretical lens to analyze data collected during 23 focus groups held at four large Swedish manufacturing companies. The data were analyzed using the Gioia method.

Findings

Two aggregated dimensions were defined and led to the development of an integrated conceptualization of the initial sensemaking of AI technology adoption. Specifically, sensemaking triggered by abstract features of AI technology mainly pointed to an exploitative learning path. Sensemaking triggered by concrete features of the technology mainly pointed to explorative paths, where socio-technical processes appear to be crucial in the process of AI adoption.

Originality/value

This is one of the first studies that attempts to explore and conceptualize how organizations make sense of prospective workplace learning in the context of AI adoption.

This study includes the design and testing of a model of research inquiry with the purpose of exploring how agency as well as learning can be increased among industrial companies within the context of competence provision.  We apply the model to an interactive, collaborative research project including researchers, industrial firms and trade organizations and report on the design of the model as well as the evaluation of its ability to increase agency and generate learning. By applying and integrating theories of agency and effectuation logic within the framework of work-integrated learning, our study contributes to the call for developing the micro-practices of interactive research methods and deepen the knowledge on the role of agency, joint learning, and reflection for organizational learning as key prerequisites to sustainable changes in how competence provision challenges is approached by organizations.

Knowledge capability and innovation performance are essential drivers of organizational success, with managerial work playing a crucial role in fostering these factors. This paper examines planned and unplanned managerial work from a knowledge creation perspective, analyzing 21 short interviews and 21 days of shadowing seven leaders, alongside interactive analyses with management teams in four manufacturing SMEs. A four-step analysis of 2125 activities revealed four categories of managerial work, with unplanned activities accounting for 52% of work time, highlighting the complexity and ad-hoc nature of managerial work. Internally initiated unplanned managerial work was crucial for tacit knowledge conversion. If policymakers want to support lifelong learning initiatives and strengthen innovation capabilities in manufacturing SMEs, they need to encourage the unplanned aspect of managerial work, as it is crucial for knowledge creation, fostering the evolutionary aspect of organizational learning and enhancing innovation capabilities.

The interest in collaborative research projects between industry and academia is growing, but the understanding of knowledge creation and how work-integrated learning takes place between these arenas, requires further attention. A deeper understanding of collaborative research projects and the learning processes is needed since interactions between industry and academy show both complicated and complex patterns. The study is based on a qualitative, explorative approach. Seven company representatives were interviewed concerning their experiences of collaboration with the academy. The results shows that the company participants’ competence level is crucial concerning motivation and abilities to contribute to the collaborative results. The communication between the academics and the practitioners is mainly performed by top-down processes, which gives advantages in the learning process to practitioners with high academic knowledge. A model is presented showing possible ways to increase the learning process, from top-down processes to bottom-up, that will include all the participants regardless of competence. To attract academics and company partners in participating in collaborative projects, this paper shows that it is essential that the project management is knowledgeable about individual participants’ knowledge level and take this into account when jointly designing the project setup.

Artificial intelligence (AI) has for the last decade been expected to revolutionize the way we work. However, as many industries are facing more complex problems as an effect of technological development and globalizations, companies are experiencing challenges in finalizing AI-projects and fully integrating AI into their operations, not least in the manufacturing industry. To face these challenges, it is suggested that manufacturing industry could benefit from enhancing their work-integrated learning related to AI and AI transformation. In addition, one of the considered success factors in technological development and organizational change is the way that it is reflected on by the organization. This paper employs an action-based perspective, in which learning is generated by and in action, that is, in the reflection on experiences. And so, with a qualitative focus group study, we sought to explore how manufacturing industry organizations reflect on action in a potential AI-transformation. However, the results showed that the participating organizations instead displayed an inaction stance when discussing AI-transformation, demonstrating a general passivity towards the transformation as a concrete change process. We employ theories of psychological safety, maneuver space and sense of coherence to analyze our empirical results. Moreover, we discuss our findings based on the idea of action and inaction as contradictory forces that adverse each other in terms of learning as well as the theoretical and practical implications of our study.

Purpose The purpose of this paper is to shed light on different types of knowledge created and how this links to the project design, process, and content. Design/methodology/approach In this paper the authors investigate participants' experiences from a three-year interactive research project, designed to trigger reflection among the participants. They apply a knowledge creation perspective on experiences expressed by participants as a result of different research project activities. Findings The study resulted in five categories of insights with potential for sustainable influence on the participating organizations: an understanding of concepts and theories; an understanding of the impacts of collaborative, reflective work processes; an understanding of the meaning of one's own organizational context; an understanding of the importance of increased organizational self-awareness; and an understanding of the potential for human interaction and communication. Practical implications The author's findings suggest that it is possible to design a project to promote more profound and sustainable effects on a business beyond the explicit purpose of the project. They advise practitioners to make room for iterative reflection; be mindful to create a trustful and open environment in the team; challenge results with opposing views and theories; and make room for sharing experiences and giving feedback. Originality/value This study contributes to unraveling key practices which can nurture conditions for knowledge creation in interactive research projects and business projects alike.

The focus of this workshop is to employ a more systematic approach to leadership development and design a maturity model that is supported by validated and practical applicable theories, methods and practices to fit the organization’s needs and resources.  

Introduction

Moving from manual, to automated, to connected AI operations systems implies a significant transformation in the organisation of work (European Parliament, 2015:8) (Brock & von Wangenheim). To understand these “realistic AI” processes, to build competence for certain tasks. it is crucial to understand what organisational competencies that are needed and how to organize knowledge creation processes in practice (Ellström, 2001) Schön used the concept of “knowing–in-action” is nonreflective and solving most everyday practical problems, here understood as executional learning (Engström & Wikner, 2017). Thus, this knowing, according to Schön (1983), is not enough to meet more complex situations. To be aware of tacit knowledge, we need to distance ourselves and learn to reflect. More complex, uncertain and unclear tasks require “knowing-on-action” and collaboration between several competences to create new knowledge or to reach a new solution here understood as developmental learning (Engström&Wikner).. Anton et al. (2020) state that in many organisations there is a lack of AI-related competencies that prevent development of the full AI potential. For the development of the field, it is important to study the dynamic interplay between advanced technology and the social side of work from a learning and competence perspective. Therefore, this paper aims to explore how industrial organisations understand their competencies in relation to AI transformation from a knowledge creation perspective.

Research method

The study was part of a collaborative research project with an interdisciplinary research team and representatives from five industrial partners. In four-month cycles the industrial partners engaged in “homework” presented, analysed and discussed in common workshops. For this study, the homework was guided by the DIGITAL approach (Brock & von Wangenheim, 2019) and based on the explanatory model (Anton et al., 2020). The industrial partners studied how resources and competencies related to specific organisational tasks in their own organisations could be identified and defined. To aid the data collection (that was done by the industrial partners themselves) a framework capturing Anton et al.’s (2020) 13 dimensions of competencies (Leadership, Communication, Customer-focused decision making, Business development, Data science/STEM, Agile software development, Initiative and engagement, AI technology, Programming, Digital analysis tools, Data and network technology, Digital competencies, and Data management) was used. For each dimension the partners assesses the competence level: Competence central to the process; Competence exists internally; Competence partly exists internally; Competence does not exist internally; Competence can be gained by development internally; Competence needs to be sourced externally. These were in line with Brock & von Wangenheim’s (2019) logic that managers when starting AI project should do “internal resources check”. The data was analysed in four steps. First, focus group data was analysed by the facilitators at each industrial partner. Second, the competence mapping was analysed by the “working groups” at each industrial partner. Third, the transcribed data from the two industrial partners used in this paper were reviewed individually by t he authors. Fourth, the cross-disciplinary group of authors from both academia and industrial partners gathered for a common analysis session. This session primarily focused on the data from the competence mapping but also cross-checked with the input from the cross-functional focus groups to triangulate the outcome. During the common analysis the conceptual framework presented in the discussion section was developed through iterations between the theoretical framework based on the findings by Anton et al. (2020), and the data from the project.

Findings

The preliminary findings show differences among the industrial partners in how they view their own competencies. For some organisations organisational structures are in place, e.g., dedicated AI Labs, where the work with understanding the benefits and usage of the technology is ongoing on a rather advanced level. In other organisations the work has just been initiated. Overall, all representatives stress the importance of top management support and the need for dedicated forums. Among the organisations that have come the farthest in their AI transformation the structure given by the proposed framework is not enough. They emphasise the need to further frame it into also understanding what the competence is associated with and why it is needed. They view the leadership as almost having to have an evangelistic approach to it, where it does not seem to be enough with “only” technical experts. A conceptual framework, consisting of the relationship between the two dimensions: the managerial competencies and the technical competencies, is developed (Figure 1). The managerial competencies dimension concerns organisation and organising. The technical competencies dimension on the other hand captures the complexity level of the technology that is needed, the system of systems. The diagonal illustrates the relationship between these two dimensions, that is, the relation between technological complexity and organisational ability. The lower part of the diagonal captures isolated, simple processes (presumably internal) while the upper part of the diagonal captures integrated, complex processes (presumably primarily related to external parts and/or actors).. For high levels of technical complexity that requires high levels of technical competencies within the organisation the organisation also needs to advance the managerial competencies and the developmental learning processes. However, while in the long-term perspective we suggest that going off the diagonal will be inefficient and ineffective, hence, waste, it might be needed to do that temporarily, as the organisation develops. We believe that this developmentcan be either technology-driven or organisation-driven.

The proposed conceptual framework is intended to help organisations plot their own current position based on the two dimensions and identify what changes are needed to reach the diagonal. It can also be used to define where on the diagonal the organisation ultimately wants to end up. It is not relevant for all companies or even for all sectors overall to be at the top right side. We believe that AI transformation cannot be approached as either technologydriven or managerial-driven, but as an e interdependent process of both dimensions.

This paper explores how industrial organisations understand their competencies in relation to AI transformation. It does so by using data from a collaborative research project with five industrial partners. The paper complements current research on AI transformation by explicating how industrial organisations understand technical and managerial competencies related to AI. The paper proposes a new two-dimensional conceptual framework that attempts to capture the complexity industrial organisations face when trying to relate their managerial efforts to their technical ones in the AI transformation process.