The abundance of digital technologies poses a challenge for Small and Medium-sized Enterprises (SMEs), which must identify, select, and implement appropriate solutions to innovate their business. Digital transformation requires SMEs to undertake a continuous digitalisation process, but a lack of guidance has led to many SMEs’ failures to embrace the potential of digital technologies. To meet this challenge, IS research has developed procedure models that structure the digital transformation process. The literature suggests that such models should have a reasonable level of flexibility and should integrate external digital innovation units to support SMEs. Research provides a promising basis for developing procedure models that meet these requirements, but extensive evaluations of these models in a naturalistic setting remain to be explored. The present study addresses this gap by developing a procedure model following the Design Science Research based on eight digitalisation projects with industry partners. The study concludes that the model provides the flexibility needed to adapt it to SMEs’ various circumstances and integrates the role of external digital innovation units. Thus, it helps SMEs initiate and advance their digital transformation. The results contribute to the body of knowledge concerning reference modelling, digital transformation, and innovation.
This research ascertained the characteristics of the work of Australia’s Health Information Managers (HIMs). It also explored the challenges (disorder) that they face and how they manage these (create order) to produce the official facts (‘the truth’) of the patient. A qualitative approach involved in-depth interviews of 55 HIMs. The HIM-participants, employed throughout the healthcare system, had spent between seven and 40 years in the profession and were confident of their expertise. The domains of their work and professional knowledge were identified as: health information science and management, including data privacy; health classification; health informatics, and information and communication technologies; and health data analysis and epidemiological research. The HIMs’ articulation work involves complex, system- and data-based governance, data privacy, and technology-related functions intended to purify and establish the source of ‘truth’ of health data for their ultimate construction of official facts. In curating, classifying and analysing the scientific facts of the patient they produce population health data and calculate hospital performance and revenues. Their ordering work necessitates constant problem-solving, intermediation, and interfacing between stakeholders, technologies and technicians amid systemic pressures and expectations of their outputs. Paradoxically, their data ordering and truth-making are invisible to patients and the public.
In the last decades, nursing homes in Denmark, have become increasingly data-oriented, with the expectation that more digital data enable more person-centred and effective care practices. On basis of ethnographic fieldwork at three nursing homes in the capital of Denmark, we explore how data about care is produced and travel within the nursing homes and become powerful claims of evidence. We illustrate how nursing home data can be considered as socio-material constructions and highlight the professional data work it takes to produce data. Drawing on recent work in Critical Data Studies, and an ‘Ethics in Practice approach’ to professional care work, we show how data work and processes of datafication might interfere with ideals and values associated with what is considered ‘good care’ and demonstrate that information about situated care practices do not always travel well as data. The paper concludes by addressing the risk, that the partial nature of nursing home data, might render aspects of care invisible and hereby undermine the situated and creative care practices, that are necessary if we are to enable true person- centred care.
As information systems (IS), including data-driven solutions, become increasingly prevalent across sectors, the need for ethical data-driven decision-making (DDDM) is gaining recognition. This scoping literature review explores how agency and ethics are addressed in scientific discussions on DDDM. It examines the ethical terminology used, its definitions, and the forms of agency identified in 79 peer-reviewed articles from disciplines such as computer science, education, and engineering. Agency is central to ethical judgment, decision-making, and action. The review reveals a wide range of ethics-related concepts, many of which are loosely defined, incoherent, and problem-oriented. Drawing on a sociomaterial perspective, the study identifies three forms of agency relevant to DDDM: human, technological, and distributed. These forms reflect the complex interplay between individuals, technologies, and institutional contexts. The review concludes that ethics and agency must be considered together to clarify responsibility at different levels of decision-making. Although ethical concerns are increasingly discussed, the debate remains fragmented. In the broader context of IS and the rapid development of generative AI, cross-disciplinary research is essential to address ethical challenges comprehensively. This study contributes both practical insights and theoretical understanding to the evolving discourse on ethical DDDM.
