Umeå University's logo

umu.sePublications
Change search
Link to record
Permanent link

Direct link
Holmström, Jonny
Alternative names
Publications (10 of 133) Show all publications
Sundberg, L. & Holmström, J. (2024). Citizen-centricity in digital government research: a literature review and integrative framework. Information Polity, 29(1), 55-72
Open this publication in new window or tab >>Citizen-centricity in digital government research: a literature review and integrative framework
2024 (English)In: Information Polity, ISSN 1570-1255, E-ISSN 1875-8754, Vol. 29, no 1, p. 55-72Article in journal (Refereed) Published
Abstract [en]

Citizen-centricity is a common concept in digital government research and policy. However, there is little clarity regarding the concept in previous literature. To address this shortcoming, and build theoretical foundations for addressing both citizen-centricity and associated phenomena, we have examined how citizen-centricity is characterized in digital government research. This study is based on literature review of 66 journal articles. A combination of narrative analysis and ideal-type methodology identified themes concerning four modes of government, designated traditionalist, service-dominant, participatory, and transformative. Further analysis of associated types and research streams provides an overview of the theoretical understandings of citizen-centricity and methodological approaches applied to explore it in the literature. The findings contribute to contemporary theory on citizens in digital government by outlining an integrative framework of citizen-centricity. The paper concludes with proposals for further research, including efforts to enhance conceptual clarity and develop more dynamic theories.

Place, publisher, year, edition, pages
IOS Press, 2024
Keywords
Citizen-centricity, digital government
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:umu:diva-214467 (URN)10.3233/ip-220047 (DOI)2-s2.0-85186088797 (Scopus ID)
Available from: 2023-09-17 Created: 2023-09-17 Last updated: 2024-03-12Bibliographically approved
Kostis, A., Sundberg, L. & Holmström, J. (2024). Data work as an organizing principle in developing AI. In: Ioanna Constantiou; Mayur P. Joshi; Marta Stelmaszak (Ed.), Research handbook on Artificial Intelligence and decision making in organizations: (pp. 38-57). Edward Elgar Publishing
Open this publication in new window or tab >>Data work as an organizing principle in developing AI
2024 (English)In: Research handbook on Artificial Intelligence and decision making in organizations / [ed] Ioanna Constantiou; Mayur P. Joshi; Marta Stelmaszak, Edward Elgar Publishing, 2024, p. 38-57Chapter in book (Refereed)
Abstract [en]

While data are often depicted as raw, neutral, and mere inputs to algorithms, we build on an emerging stream of research on data work viewing data as ambivalent, performative, and embedded entities, interwoven with organizing. We argue that in the process of developing AI, where epistemic uncertainty prevails as a key organizing challenge, data work serves as an organizing principle providing the logic through which behaviors are adopted, interpretations are made, and the collective efforts of domain experts and AI experts are coordinated. Prior research suggests that active involvement of both AI and domain experts is required for developing AI. Yet, domain experts and AI experts have distinct knowledge and understandings of domain specificities, meanings of data, and AI’s possibilities and limitations. Consequently, in AI initiatives, a key organizing challenge is epistemic uncertainty, i.e., ignorance of pertinent knowledge that is knowable in principle. We build a conceptual model deciphering three key mechanisms through which data work serves as an organizing principle supporting organizations to cope with epistemic uncertainty: cultivating knowledge interlace, triggering data-based effectuation, and facilitating multi-faceted delegations. These three mechanisms emerge when domain experts and AI experts work with and on data to define and shape trajectories of an AI initiative and make decisions about AI. This chapter contributes to the nascent body of research on data work by expounding the performative role of data as a relational entity, by providing a processual view on data’s interweaving with organizing, and by deciphering data work as a collectively accomplishment.

Place, publisher, year, edition, pages
Edward Elgar Publishing, 2024
Keywords
AI development, Epistemic uncertainty, Data work, Organizing principle, Data-based effectuation, Delegation
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:umu:diva-222395 (URN)10.4337/9781803926216.00010 (DOI)2-s2.0-85192619160 (Scopus ID)9781803926209 (ISBN)9781803926216 (ISBN)
Available from: 2024-03-15 Created: 2024-03-15 Last updated: 2024-07-02
Sundberg, L. & Holmström, J. (2024). Fusing domain knowledge with machine learning: A public sector perspective. Journal of strategic information systems
Open this publication in new window or tab >>Fusing domain knowledge with machine learning: A public sector perspective
2024 (English)In: Journal of strategic information systems, ISSN 0963-8687, E-ISSN 1873-1198Article in journal (Refereed) Published
Abstract [en]

Machine learning (ML) offers widely-recognized, but complex, opportunities for both public and private sector organizations to generate value from data. A key requirement is that organizations must find ways to develop new knowledge by merging crucial ‘domain knowledge’ of experts in relevant fields with ‘machine knowledge’, i.e., data that can be used to inform predictive models. In this paper, we argue that understanding the process of generating such knowledge is essential to strategically develop ML. In efforts to contribute to such understanding, we examine the generation of new knowledge from domain knowledge through ML via an exploratory study of two cases in the Swedish public sector. The findings reveal the roles of three mechanisms – dubbed consolidation, algorithmic mediation, and naturalization – in tying domain knowledge to machine knowledge. The study contributes a theory of knowledge production related to organizational use of ML, with important implications for its strategic governance, particularly in the public sector.

Keywords
Knowledge production, Artificial Intelligence, Machine Learning, Natural Language Processing, Public Sector
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:umu:diva-227858 (URN)10.1016/j.jsis.2024.101848 (DOI)
Available from: 2024-07-13 Created: 2024-07-13 Last updated: 2024-07-13
Sundberg, L. & Holmström, J. (2024). Innovating by prompting: How to facilitate innovation in the age of generative AI. Business Horizons
Open this publication in new window or tab >>Innovating by prompting: How to facilitate innovation in the age of generative AI
2024 (English)In: Business Horizons, ISSN 0007-6813, E-ISSN 1873-6068Article in journal (Refereed) In press
Abstract [en]

This article focuses on how recent advances in artificial intelligence (AI), particularly chatbots based on large language models (LLMs), such as ChatGPT, can be used for innovation purposes. The article begins with a brief overview of the development and characteristics of generative AI (gAI). Elaborating on the implications of gAI, we provide examples to demonstrate four mechanisms of LLMs: translation, summarization, classification, and amplification. These mechanisms inform a framework that highlights how LLMs enable the creation of innovative solutions for organizations through capacities in two dimensions: context awareness and content awareness. The strength of LLMs lies in the combination of capacities in both these dimensions, which enables them to 'comprehend' and amplify content. Four managerial suggestions are presented, ranging from starting out with small-scale projects and data exploration, to scaling through integration efforts and educating prompt engineers. By presenting the framework, recommendations, and examples of use cases in various contexts, the article contributes to the emerging literature on gAI and innovation.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
prompt engineering, generative AI, ChatGPT, large language models
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:umu:diva-223741 (URN)10.1016/j.bushor.2024.04.014 (DOI)2-s2.0-85192497786 (Scopus ID)
Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2024-07-09
Holmström, J., Kostis, A., Galariotis, E., Roubaud, D. & Zopounidis, C. (2024). Stalled data flows in digital innovation networks: Underlying mechanisms and the role of related variety. Industrial Marketing Management, 121, 16-26
Open this publication in new window or tab >>Stalled data flows in digital innovation networks: Underlying mechanisms and the role of related variety
Show others...
2024 (English)In: Industrial Marketing Management, ISSN 0019-8501, E-ISSN 1873-2062, Vol. 121, p. 16-26Article in journal (Refereed) Published
Abstract [en]

Data flows across organizational boundaries are vital for creating and capturing value from data-intensive digital technologies, such as Artificial Intelligence. To achieve this, organizations increasingly engage in digital innovation networks, i.e., constellations of relations among dispersed, loosely coupled actors, who seek to collaborate for combining heterogeneously distributed domain expertise to train and leverage emerging digital technologies that learn from data. Yet, data flows remain stalled within digital innovation networks, and organizations fail to achieve sought-after benefits from data-intensive digital technologies. To date, research has paid limited attention to what contributes to stalled data flows and what strategies are required to facilitate seamless data flows. Our in-depth qualitative study of a digital innovation network within the Swedish forestry identified four key mechanisms underlying stalled data flows and hampering firms in leveraging value from data-intensive digital technologies and revealed the key role of brokerage functions in digital innovation networks for establishing what we call related variety.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Artificial intelligence, Data flows, Digital innovation networks, Related variety
National Category
Information Systems Business Administration
Identifiers
urn:nbn:se:umu:diva-227557 (URN)10.1016/j.indmarman.2024.06.007 (DOI)2-s2.0-85196826218 (Scopus ID)
Funder
Jan Wallander and Tom Hedelius Foundation and Tore Browaldh Foundation, W21-0008Jan Wallander and Tom Hedelius Foundation and Tore Browaldh Foundation, Fv23-0047
Available from: 2024-07-03 Created: 2024-07-03 Last updated: 2024-07-03Bibliographically approved
Sundberg, L. & Holmström, J. (2024). Teaching tip: using no-code AI to teach machine learning in higher education. Journal of Information Systems Education, 35(1), 56-66
Open this publication in new window or tab >>Teaching tip: using no-code AI to teach machine learning in higher education
2024 (English)In: Journal of Information Systems Education, ISSN 1055-3096, Vol. 35, no 1, p. 56-66Article in journal (Refereed) Published
Abstract [en]

With recent advances in artificial intelligence, machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in use of ML into non-technical educational programs, such as social sciences courses, is challenging. Here, we present an approach to address this challenge by using no-code AI in a course for students with diverse educational backgrounds. The approach was tested in an empirical, case-based educational setting, in which students engaged in data collection and trained ML models using a no-code AI platform. In addition, a framework consisting of five principles of instruction (problem-centered learning, activation, demonstration, application, and integration) was applied. This paper contributes to the literature on IS education by providing information for instructors on how to incorporate no-code AI in their courses, and insights into the benefits and challenges of using no-code AI tools to support the ML workflow in educational settings.

Place, publisher, year, edition, pages
Information Systems and Computing Academic Professionals (ISCAP), 2024
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:umu:diva-207861 (URN)10.62273/CYPL2902 (DOI)2-s2.0-85186916600 (Scopus ID)
Available from: 2023-05-04 Created: 2023-05-04 Last updated: 2024-03-18Bibliographically approved
Muhic, M., Bengtsson, L. & Holmström, J. (2023). Barriers to continuance use of cloud computing: evidence from two case studies. Information & Management, 60(5), Article ID 103792.
Open this publication in new window or tab >>Barriers to continuance use of cloud computing: evidence from two case studies
2023 (English)In: Information & Management, ISSN 0378-7206, E-ISSN 1872-7530, Vol. 60, no 5, article id 103792Article in journal (Refereed) Published
Abstract [en]

Continuous use of cloud computing and cloud sourcing has received limited research attention compared to cloud adoption. There are indications that cloud sourcing benefits are not easy to reap in continuous use for companies, calling for more research on the continuance use of cloud computing. The current study is one of the first studies of the continuance use of cloud computing processes at the organizational level, contributing to the management and business research literature on cloud computing. In particular, the present study has contributed with two case studies verifying the existence of barriers and more importantly identifying an additional type of barrier: management process barriers (MP), i.e., lack of objectives and strategies for cloud sourcing and lack of organizing cloud vendor communication. Overcoming or reducing management process barriers guides the continuance use of cloud computing process in a strategic direction for the company and enables cloud-related innovation. As a contribution, our research builds on and extends extant research by providing a TOMPE framework of barriers to the continuance use of cloud computing (based and modified from the technology–organization–environment (TOE) framework, by complementing it with the identified management process barriers).

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Barriers, Cloud computing, Cloud sourcing, Continuance use of cloud computing, IT outsourcing, TOE
National Category
Information Systems, Social aspects Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:umu:diva-209112 (URN)10.1016/j.im.2023.103792 (DOI)001001981100001 ()2-s2.0-85159181324 (Scopus ID)
Available from: 2023-06-08 Created: 2023-06-08 Last updated: 2023-09-05Bibliographically approved
Sundberg, L. & Holmström, J. (2023). Democratizing artificial intelligence: how no-code AI can leverage machine learning operations. Business Horizons, 66(6), 777-788, Article ID 777-788.
Open this publication in new window or tab >>Democratizing artificial intelligence: how no-code AI can leverage machine learning operations
2023 (English)In: Business Horizons, ISSN 0007-6813, E-ISSN 1873-6068, Vol. 66, no 6, p. 777-788, article id 777-788Article in journal (Refereed) Published
Abstract [en]

Organizations are increasingly seeking to generate value and insights from their data by integrating advances in artificial intelligence (AI) such as machine learning (ML) systems into their operations. However, there are several managerial challenges associated with ML operations (MLOps). In this article we outline three key challenges and discuss how an emerging form of AI platforms – ‘no-code AI’ – may help organizations to address and overcome them. We outline how no-code AI can leverage MLOps by closing the gap between business and technology experts, enabling faster iterations between problems and solutions, and aiding infrastructure management. After outlining important remaining challenges associated with no-code AI and MLOps we propose three managerial recommendations. By doing so, we provide insights into an important novel, emerging phenomenon in AI software and set the stage for further research in the area.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Artificial intelligence, Machine learning, No-code AI, MLOps, Operational AI
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:umu:diva-206856 (URN)10.1016/j.bushor.2023.04.003 (DOI)2-s2.0-85170233128 (Scopus ID)
Available from: 2023-04-19 Created: 2023-04-19 Last updated: 2024-04-11
Page, A. & Holmström, J. (2023). Enablers and inhibitors of digital startup evolution: a multi-case study of Swedish business incubators. Journal of Innovation and Entrepreneurship, 12(1), Article ID 35.
Open this publication in new window or tab >>Enablers and inhibitors of digital startup evolution: a multi-case study of Swedish business incubators
2023 (English)In: Journal of Innovation and Entrepreneurship, E-ISSN 2192-5372, Vol. 12, no 1, article id 35Article in journal (Refereed) Published
Abstract [en]

Global advances in digital technology are facilitating corresponding rises in digital entrepreneurship and its startup manifestation. There are many uncertainties on the road to digital startup evolution, some of which may be successfully navigated with the assistance of business incubators. While these organisations provide valuable guidance and support to the startup community, their efforts are at least partly constrained by the lack of a consistent, coherent roadmap to guide both them and their incubatees. T0 help efforts to develop such a map, this paper seeks to identify factors that influence digital startup evolution within an incubator setting through a multiple-case study focusing on digital startups under the umbrella of three business incubators in the Swedish city Umeå. Sets of enabling and inhibitory factors are identified through literature searches and the case studies. The latter may include inertia and possibly attitudes towards failure. In addition, present the Ideation Dynamics Model as a guide for both incubators and digital startups is proposed.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Digital entrepreneurship, Digital startups, Incubators, Scaling
National Category
Business Administration
Identifiers
urn:nbn:se:umu:diva-209539 (URN)10.1186/s13731-023-00306-y (DOI)2-s2.0-85160859973 (Scopus ID)
Funder
Knut and Alice Wallenberg Foundation
Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2024-05-06Bibliographically approved
Koukouvinou, P., Ademaj, G., Sarker, S. & Holmström, J. (2023). Ghost in the machine: theorizing data knowledge in the age of intelligent technologies. In: Rising like a phoenix: emerging from the pandemic and reshaping human endeavors with digital technologies ICIS 2023. Paper presented at International Conference on Information Systems (ICIS) 2023, Hyderabad, India, December 10-13, 2023.. AIS eLibrary, 22, Article ID 1622.
Open this publication in new window or tab >>Ghost in the machine: theorizing data knowledge in the age of intelligent technologies
2023 (English)In: Rising like a phoenix: emerging from the pandemic and reshaping human endeavors with digital technologies ICIS 2023, AIS eLibrary , 2023, Vol. 22, article id 1622Conference paper, Published paper (Refereed)
Abstract [en]

AI technologies have led to new ways of thinking about data, knowledge, and organizations. Despite the arguments that data speak for themselves, the era of datafication demands revisiting data and knowledge and reflecting on new ways of theorizing. Considering that working with data is important for most employees, there is a need to investigate how the knowing of data can be achieved. In this paper, we move beyond the factual view of data and the hierarchical view of data and knowledge, to introduce data knowledge as a new type of knowledge. We present a first step towards a theory of explanation of what is data knowledge in today ́s organizations. To investigate this, we apply an etymological lens, and review systematically the IS literature. Our preliminary findings demonstrate unveiling data, balancing between intuition and data, acknowledging external and internal capabilities, and realizing data, as the four main concepts of data knowledge.

Place, publisher, year, edition, pages
AIS eLibrary, 2023
Keywords
data, knowledge, theorizing, etymology, systematic review
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:umu:diva-217530 (URN)2-s2.0-85192532950 (Scopus ID)9781713893622 (ISBN)
Conference
International Conference on Information Systems (ICIS) 2023, Hyderabad, India, December 10-13, 2023.
Note

Track: AI in Business and Society

Available from: 2023-12-07 Created: 2023-12-07 Last updated: 2024-06-04Bibliographically approved
Projects
Organizing for innovation [2009-01742_Vinnova]; Umeå UniversityOrganizational preconditions for innovation: Examining innovation networks in the creative industry [2013-02524_Vinnova]; Umeå University
Organisations

Search in DiVA

Show all publications