Background: Adverse drug reactions pose a serious threat to health care, leading to patient harm and substantial economic burden. Dashboards for drug safety surveillance are a valuable tool to tackle it.

Objective: This qualitative study aims to develop a dashboard for drug safety tracking with active and iterative involvement of end users. To support dashboard development, we formulate and iteratively refine design principles (DPs) for drug safety dashboards using the affordance theory.

Methods: Following a design science research approach, we conducted 3 cycles of iterative design and evaluation involving the end users. Four professional end users (with expertise in drug screening, drug discovery, and data science) and 6 nonprofessional end users (drug consumers) were engaged in the requirements gathering sessions through co-design workshops, usability testing through think-aloud sessions, and heuristic evaluation.

Results: The analysis resulted in a set of 8 DPs refined using the prototype’s affordances (ie, actionable properties of the dashboard that guide user interaction and interpretation of data). Following themes emerged in the formulation and refinement of DPs: addressing the bootstrap problem through designing for immediate use (DP1a), allowing identification of patterns through visualizing causality while signalizing uncertainty (DP1b), tracking trends for relevant variables (DP1c), implementing user-controlled views (DP2a) and customizable levels of data granularity (DP2b), guiding user’s visual attention through spatial layouts (DP2c), designing for higher public value (DP3a) and providing features to support decision-making for varied stakeholders’ groups (DP3b). A high-fidelity dashboard prototype for drug safety surveillance was proposed as a result of applying the final set of DPs. Heuristic evaluation of the prototype revealed an overall usability score of 84%.

Conclusions: Applying DPs rooted in affordance theory led to a purposeful and user-relevant artifact that can improve understanding of drug safety data and potentially guide decision-making processes for professional users. Our theoretical contribution lies in providing refined DPs, while also demonstrating how affordances can aid dashboard development in pharmacovigilance. Our findings may be applicable to similar health information systems in related domains.

Organisations increasingly invest in Big Data Analytics (BDA) to enhance decision-making, yet the relationship between analytical approaches and organisational outcomes remains unclear. This systematic literature review synthesises 62 peer-reviewed studies from 2020 to 2025 to examine how descriptive, predictive, and prescriptive analytics mediate decision processes across industries. Through inductive thematic synthesis, we identify three distinct decision domains influenced by BDA: operative decision enablement, transformative decision support, and adaptive decision capability. Our findings reveal that descriptive analytics primarily supports operational monitoring and real-time responsiveness, predictive analytics drives strategic learning and organisational resilience, while prescriptive analytics enables continuous organisational recalibration. The study identifies persistent tensions between automation and intuition, algorithmic autonomy and human control, and analytical precision and interpretive transparency that organisations must navigate to realise BDA’s full potential.

Due to the advent of digitalization, the retail business has seen major changes in recent decades, including e-commerce and multichannel retailing. Customers have witnessed the development of new and digitized channels, as well as solutions such as digital marketplaces, social media touchpoints, and augmented reality applications. During the COVID-19 pandemic, as most of the physical brick-and-mortar retailers were forced to close, more consumers turned to digital channels such as apps, websites, social media platforms, etc. Small retail businesses, which account for a sizable share of the retail industry, have also faced challenge of seamless retailing in the era of digitization. This concept of seamless retailing is called omnichannel retailing, and it aims to integrate all the retail channels and touchpoints. The goal for small retailers is to make the transformation to omnichannel. The purpose of this study is to investigate how small retail businesses are changing their business models in the age of omnichannel transformation. Through a qualitative study of small retailers, this research provides empirical insights on business model changes and adaptive skills. The findings show that small businesses can implement complex changes in their business models through adaptive behaviors. This study also identifies areas in their business models and adaptive capabilities that require additional consideration and improvement to support the ongoing omnichannel transformation.

The exogenous shock of COVID-19 strongly challenged Greek retail, especially in small and micro-sized enterprises (SMiEs). In particular, the decline of customer visits in physical retail stores caused by lockdowns and other restrictive measures led to a business threatening reduction of sales. As a result, adjusting business model elements became one of the first priorities for Greek business owners. This paper focuses on the effects of the COVID-19 pandemic on business model elements of Greek SMiE retailers. To investigate this impact, eight expert interviews with Greek SMiE retailers were conducted. Results show that the pandemic situation directly and indirectly affected most of the business model elements of Greek SMiE retailers. Naturally, each studied company displayed unique characteristics as well as different needs and priorities, which facilitated the specific changes in their business model in order to become profitable. However, especially opportunities offered by digital technologies, such as web shops, online advertising, and online customer engagement, were most frequently mentioned to influence business model elements. Thus, this research contributes to the existing body of knowledge in the field of business models and information systems, by providing a business owner perspective on how business model elements were adjusted to the circumstances of the COVID-19 pandemic.

Despite rapid growth projections in the smart home technology market, the adoption of smart home appliances has notably slowed down. One contributing factor is the lack of acceptance testing, particularly in private settings, leading to under-researched areas. This research explores how consumers perceive the value of smart home appliances in private settings using the theory of consumption values. Through interviews with six individuals focusing on smart refrigerators and freezers, the study identifies that functional and emotional values significantly influence consumer perception and adoption intentions. It highlights cost and negative emotional sentiments as major deterrents to adoption while suggesting a close relationship between functional and emotional values. Additionally, social and epistemic values show a modest positive impact on adoption intentions, while the effects of conditional values remain unclear.

The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in data analytics requires employees to make sense of several elements which collectively contribute to the generation of required insights. Building on sense-making, self-service business analytics, and institutions literature, this paper explores the relationship between sense-making and self-service business analytics and how institutions influence and shape such relationship. By adopting a qualitative perspective and using 22 interviews, we have empirically investigated a model developed through our literature review and provided more understanding of the sense-making concept in a self-service business analytics context.

Purpose – The purpose of this paper is to investigate the omnichannel practices to propose a conceptualoverview to offer guidance on how to handle their inherent complexities.

Design/methodology/approach – This study is based on a literature review of more than 100academics papers about the multichannel practices and omnichannel practices in the global market.

Findings – To this end, this paper identifies and addresses three limitations of the contemporaryomnichannel literature: the failure to articulate the sources of value creation generated by omnichannelpractices, the conception of omnichannel as targeting a single customer actor only and the static conception ofomnichannel practices. In response to these limitations, this study offers the following: four sources of valuecreation based on the business model concept, a multi-actor customer conception, where several actorspartake in the overall purchase decision and an evolutionary notion of omnichannel practices in terms of theirconstitution and use as part of the overall evolution of a marketplace.

Originality/value – The framework presented in this paper provides a map to take new research beyondits current boundaries and an audit tool to help managers identify their firm’s current omnichannel situation,including limitations and opportunities for further development.

The 2nd Linnaeus Student Conference on Information Technology (LSCIT) was held for two days between September 24 and September 25, 2020 in the Växjö campus of Linnaeus University. There were also online participants from different countries as well as different parts of Sweden.

With the growing number of applications of artificial intelligence such as autonomous cars or smart industrial equipment, the inaccuracy of utilized machine learning algorithms could lead to catastrophic outcomes. Human-in-the-loop computing combines human and machine intelligence resulting in a hybrid intelligence of complementary strengths. Whereas machines are unbeatable in logic and computation speed, humans are contributing with their creative and dynamic minds. Hybrid intelligent systems are necessary to achieve high accuracy and reliability of machine learning algorithms. In a design science research project with a Swedish manufacturing company, this paper presents an application of human-in-the-loop computing to make operational processes more efficient. While conceptualizing a Smart Power Distribution for electric industrial equipment, this research presents a set of principles to design machine-learning algorithms for hybrid intelligence. From being AI-ready as an organization to clearly focusing on the customer benefits of a hybrid intelligent system, designers need to build and strengthen the trust in the human-AI relationship to make future applications successful and reliable. With the growing trends of technological advancements and incorporation of artificial intelligence in more and more applications, the alliance of humans and machines have become even more crucial.

The digitalization of products stimulates service innovation and new business opportunities for industrial manufacturing firms. These opportunities are driven by generativity, which denotes digital technology's capacity in enabling organizations to innovate. While generativity on the one hand provides opportunities for digital service innovation, on the other hand it challenges the traditional business logic of industrial manufacturing firms. The traditional stable product-centric businesses where resources of a product platform are controlled by industrial manufacturing firms become destabilized and driven towards a flexible service innovation. The aim of this research is to explore the implications of generativity for service innovation in digitalized product platforms. We show the paradoxical relationship between generativity and control by following a bus manufacturer's endeavour in service innovation with digitalized public transport buses. We contribute with empirical illustrations of the implications for service innovation with three different degrees of control in digitalized product platforms.