Blockchain platforms have revolutionized the food industry by establishing a more trustworthy supply chain. By providing a secure and open record of all transactions, blockchain technology helps improve food safety. This reassures consumers who may wish to ensure that products are guaranteed to meet certain dietary or nutritional standards. However, the adaptability of existing supply chains and consumers' full adoption of such systems remains a gap in scholarly research. This field study employs Q-methodology to explore the concourse of requirements and priorities of food consumers in blockchain-based agri-food supply chains. Abductive reasoning is utilized to elaborate on the subjective perspective of consumers when specifying the system design requirements of blockchain technology. Although the study is exploratory, testable hypotheses for future research are proposed using abductive reasoning pertaining to enhancing consumer trust and improving blockchain adoption in the agri-food supply chains (ASCs). This paper concludes that consumer-driven food production supported by blockchain-enabled ASCs is an effective pathway to understanding how blockchain features may be harnessed to reshape discerning consumers’ informed purchasing decisions in the evolving landscape of ASCs.

We aimed to systematically review Blockchain affordances in digital health and synthesize a framework and research agenda for personalized health-care. Following PRISMA guidance, we conducted a qualitative thematic synthesis of peer-reviewed studies (January 2020 – October 2025 search window; no meta-analysis). Blockchain technology is an emerging solution that can meet these needs. However, a nuanced application of Blockchain affordances in digital health is necessary to harness its full potential. This paper comprehensively reviews Blockchain affordances in digital health, aiming to identify perceived affordances and explore recent research in the field. We applied Preferred Reporting Items for Systematic Reviews, following the PRISMA guidelines and the lens of affordance theory to analyze about 5300 relevant papers, with 194 selected for deeper analysis. Our analysis identified 14 Blockchain affordances (access control, decentralization, interoperability, security, tamper-resistance, traceability, anonymity, data provenance, identity, immutability, integrity, privacy, transparency, and trust) that are perceived and realized in personalized health-care. Our study also discovered several constraints in Blockchain implementation, such as security and privacy, interoperability, scalability, and infrastructural support, that require further research attention. This study presents Blockchain research in the digital health domain and informs the design and development of computational medicine for personalized health-care.

The COVID-19 pandemic has exposed the flaws of the traditional physically co-located office, forcing many organizations to work remotely. Many knowledge workers worked from home on a regular basis during COVID-19, and as a result, the power gap between remote e-workers and their previously on-site colleagues has vanished. To answer our research questions, how does the involuntary working from home requirement due to COVID-19 affect team collaboration and performance, what factors enable the design and implementation of a hybrid way of working in knowledge organizations? and, in volatile and uncertain situations, how does organizational culture influence IT governance performance in global virtual teams in a large organization? we conducted an in-depth organization-wide case study. Using a game-theoretic lens, this study explores the sudden and enforced issues that COVID-19 has presented, and the technological means knowledge workers use to achieve their team collaboration goals. We interviewed 221 knowledge workers and,15 c-suite executives and senior leadership group (SLG) members about their experiences of being required to work from home and its' various effects on team dynamics and collaborative problem-solving. This paper contributes to the IT Governance theory and IS Resilience theory by providing an understanding of team collaboration during uncertain, volatile situations (COVID-19). This broad-scope overview provides an integrative approach for considering the implications of COVID-19 for work, workers, and organizations while also identifying issues for future research and insights to inform solutions.

With increasing sophistication in wearable devices, Generative AI, and Quantum Computing, this paper reimagines the future of Precision Healthcare as a Service (PHCaaS). Specifically, how might we, for example, monitor patient wellness with wearable devices, AI interactions, and analytics on a trustworthy platform? As a first step, we establish and validate design rules using an emphatic, participatory approach called Soft Systems Modeling (SSM), prioritising patient-centricity. Drawing on prior research, we utilised Unified Modeling Language (UML) techniques to model and refine these rules, validated through a Delphi panel of potential PHC stakeholders. The procedural steps of SSM and UML methodologies utilised in the study are already detailed in prior work. This paper emphasises PHCaaS's link to patient-centricity, highlighting a commonly overlooked challenge for healthcare and technology providers. With the rise of generative AI and Large Language Models, ensuring data security, addressing bias, and building trust in PHCaaS become increasingly critical.

The outbreak of the COVID-19 pandemic has highlighted the vulnerabilities of the traditional physically co-located office, forcing many organizations to work remotely. During COVID-19, many knowledge workers work from home regularly, and as a result, the power distance between remote e-workers and their previously on-site colleagues has disappeared. An in-depth organization-wide case study was conducted to answer our research questions, how does the involuntary working from home requirement due to COVID-19 affect team collaboration and performance? What are the enabling factors to design and implement a hybrid way of working in knowledge organizations? And how does organizational culture influence IT governance performance in global virtual teams in a large organization during volatile and uncertain situations? The main conclusion of this research is that organizational culture does influence the performance outcomes of IT governance.

The application of blockchain beyond cryptocurrencies has received increasing attention from industry and scholars alike. Given predicted looming food crises, some of the most impactful deployments of blockchains are likely to concern food supply chains. This study outlined how blockchain adoption can result in positive affordances in the food supply chain. Using Q- methodology, this study explored the current status of the agri-food supply chain and how blockchain technology could be useful in addressing existing challenges. This theorization leads to the proposition of the 3TIC value-driver framework for determining the enabling affordances of blockchain that would increase shared value for stakeholders. First, we propose a framework based on the most promising features of blockchain technology to overcome current challenges in the agri-food industry. Our value-driver framework is driven by the Q-study findings of respondents closely associated with the agri-food supply chain. This framework can provide supply chain stakeholders with a clear perception of blockchain affordances and serve as a guideline for utilizing appropriate features of technology that match organizations’ capabilities, core competencies, goals, and limitations. Therefore, it could assist top-level decision-makers in systematically evaluating parts of the organization to focus on and improve the infrastructure for successful blockchain implementation along the agri-food supply chain. We conclude by noting certain significant challenges that must be carefully addressed to successfully adopt blockchain technology.

The application of Blockchain and augmented technologies such as IoT, AI, and Big Data platforms present a feasible approach for resolving the implementation challenges of trusted, decentralized platforms. This article proposes a DevOps framework for the specification of Blockchain use-cases that enables evaluation, replication, and benchmarking. Specifically, it could be applied to specify the requirements and design characteristics of Blockchain applications in terms of key attributes such as: (i) transparency; (ii) traceability; (iii) tamper-resistance; (iv) immutability; and (v) compliance. The article first introduces the design characteristics of Blockchain as a Platform and then examines successful use-cases for its implementation using the above attributes. It may be conjectured that the 3TIC framework would serve as the basis of a cross industry process for Blockchain. The intended contribution is that such a standard process will support industry-academia collaboration in the development of Blockchain platforms and services of relevance and utility as it can be applied by firms to structure their requirements and design specifications.

In this article, we introduce qpair as a new command written in Stata for the analysis of paired Q-sorts in Q-methodology, which is used for studying subjective issues and is a combination of qualitative and quantitative techniques. The quantitative component of Q-methodology employs a by-person factor analysis technique. However, currently there is no systematic approach for analyzing paired Q-sorts or longitudinal data in Q-methodology. We introduce the only statistical command available for the analysis of paired Q-sorts. The qpair command employs the factor extraction and factor rotation techniques in Stata. The command is illustrated using a dataset representing perceptions of 50 information technology professionals on person–organization fit regarding their training and development priorities.

Fairtrade-certified products have successfully entered the mainstream distribution channels, mostly in developed countries, and these products are now sold in famous supermarket chains. Nonetheless, the packaging and labeling of products as “Fairtrade” command premium pricing in the marketplace. How much of this, however, is valid and justified? Despite the reputable certification mechanisms for quality assurance, mass media reports suggest that much of the “surplus value” goes to the accreditation agencies themselves instead of the producers. This article proposes an agenda to set this right with a blockchain platform that provides “trust-free” assurances of verifiable labeling. Using an Action Design Research methodology, we have specified a research prototype of a Blockchain-enabled Fair-Trade platform Unified Modelling Language artifacts. We believe this will set the direction for social inclusion as part of information systems scholars’ aspiration to promote “tech for good.”

Although there have been some previous attempts on analyzing changes in perceptions in Q-methodology, a systematic approach is lacking. In this article we introduce two new methods for analyzing change in perceptions in Q-methodology using paired Q-sorts. We also demonstrate these methods using an actual dataset. Method I: This approach is appropriate for assessing the changes in perceptions between two different conditions of instruction applied to the same subjects. The changes are assessed using a factor analysis on the differences between the Q-sorts from the two conditions of instruction. Method II: This method examines the changes in perception from a baseline Q-analysis. This is usually appropriate when data are collected at two time-points, e.g., before-after situations, where the first assessment is considered as the baseline. In this approach, a by-person factor analysis is conducted on the baseline Q-sorts (condition 1) and factors are identified. Then, the changes in perceptions are assessed for the subjects loaded on any factor from baseline using the Q-sorts from condition 2. In conclusion, these two methods are easy to apply, the results are more objective, and are less prone to investigator bias.

Precision Healthcare (PHC) is a disruptive innovation in digital health that can support mass customisation. However, despite the potential, recent studies show that PHC is ineffectual due to the lower patient adoption into the system. This paper presents a Blockchain-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in rates. Soft Systems Methodology was adopted to create and validate UML design artefacts. Research findings report that there is a need for data-driven, secure, transparent, scalable, individualised and precise medicine for the sustainability of healthcare and suggests further research and industry application of explainable AI, data standards for biosensor devices,

Big Data's 5 V complexities are making it increasingly difficult to develop an understanding of the end to end process. Big Data platforms play a crucial role in many critical systems, combining with Internet-of-Things, Artificial Intelligence and Business Analytics. It is both relevant and important to understand Big Data systems to identify the best tools that fit the requirements of heterogeneous platforms. The objective of this paper is to "discover" a set of design principles and rules for Cloud-based Big Data platforms for complex, heterogeneous environments. The design scope comprises Big Data's significance, challenges and architectural impacts. Using a methodology Reverse Engineered Design Science Research (REDSR), artifacts from leading vendors are used to elicit the design principles and rules with relevant details of Big Data components. We conclude that the findings are relevant and useful for DevOps architects and practitioners in operating complex, heterogeneous Cloud-based Big Data platforms.

Chatbot technology is increasingly emerging as a virtual assistant. Chatbots could allow individuals and organizations to accomplish objectives that are currently not fully optimized for collaboration across an intergenerational context. This paper explores the preferences of chatbots as a companion in intergenerational innovation. The Q methodology was used to investigate different types of collaborators and determine how different choices occur between collaborators that merge the problem and solution domains of chatbots’ design within intergenerational settings. The study’s findings reveal that various chatbot design priorities are more diverse among younger adults than senior adults. Additionally, our research further outlines the principles of chatbot design and how chatbots will support both generations. This research is the first step towards cultivating a deeper understanding of different age groups’ subjective design preferences for chatbots functioning as a companion in the workplace. Moreover, this study demonstrates how the Q methodology can guide technological development by shifting the approach from an age-focused design to a common goal-oriented design within a multigenerational context.

Maintaining professional competency in an environment with rapid technological innovation may seem to be an insurmountable task since new technologies often become obsolete before technology professionals can master them. Although research has established that challenging work assignments affect professional motivation, research has also established that overly challenging work assignments can demotivate people. In particular, research has called for work that examines the relationship between the technical updating climate (TUC) and learning motivation in the professional development activity context. To fill this gap, we collected data from 174 IT professionals who exemplify professionals working in such environments. We found evidence that showed locus of control, self-efficacy, and technical updating climate could predict 43 percent of the variation in the IT professional’s motivation to participate in professional development. The research model that we propose demonstrates a strong ability to explain motivation to engage in professional development in technologically intensive work contexts. Furthermore, we successfully operationalized and validated the technical updating climate as both an instance of positive climate and as an organizational climate.

BACKGROUND The current digital health context is incapable of supporting the future need for data security and storage in digital health services. It requires implementing a robust, interoperable, and scalable data storage and security solution to address this future need. Blockchain is an emerging information technology that can support this industry's timely needs. Therefore, a clear foundational understanding of Blockchain affordances for digital health is significant to harness its full potential. OBJECTIVE Objective: This paper presents a comprehensive review of Blockchain affordances for digital health. The review aims to: 1) identify the perceived Blockchain affordances and 2) explore the recent Blockchain research in digital health (actualized). METHODS We applied the Systematic Literature Review (SLR) methodology to review the literature extant. Furthermore, we applied the affordance theory lens to define and defend our findings on Blockchain affordances. RESULTS A total of 3627 relevant papers have been identified and analysed in this review study. Of these, 90 were probed deeply. Our analysis identified 14 Blockchain affordances (Access control, Interoperability, Security, Tamper-resistance, Traceability, Anonymity, Data Provenance, Identity, Immutability, Integrity, Privacy, Transparency, and Trust) which are perceived and actualized in digital health. Our study also discovered several constraints in Blockchain implementation such as security and privacy, interoperability, scalability, and infrastructural support that requires further research attention. CONCLUSIONS We believe this study will guide further Blockchain research in the digital health domain and informatively contribute to eliminating (decreasing) the dark side of digital health and improving (increasing) the bright side for the future.

Background: Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs. Objective: This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC. Findings: A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation. Conclusion: Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030.

In this paper, we adopt Agency Theory and Weill and Ross’s IT Governance framework to examine the decision priorities of senior executives and board of directors in the context of IS resilience planning, which falls under the broader umbrella of IT governance. As identified in our earlier research, although research was conducted on the topics of organizational resilience, and IT governance, there is a gap in the extant literature on IS resilience. In this study we also expand the basic assumptions of Agency theory. We present a case study of the Jade Software Corporation, in which we use Q-methodology to develop a typology of decision priorities for IS resilience planning. Our analysis revealed two types of decision-makers, each representing a unique perspective of IS resilience. These types are discussed, along with implications of findings, a theoretical framework for IS resilience, and suggestions for future research.

Ethical usage of artificial intelligence and data science is a rapidly evolving topic of discussion among individuals, organizations, and society. More attention has been paid to moral rules and regulations during such discussions than these stakeholders’ moral character development. This study examines how individuals deploy their moral decision-making skills under conditions of uncertainty. What are the most important or most unimportant virtues in their decision to develop trust in artificial intelligence-based emerging technologies in the presence of personal information privacy threats? Using Q-methodology, the Concourse theory, and virtue ethics, four viewpoints (i.e., virtues-based decision-making structures) of individuals are extracted from a group of 39 participants for developing trust in emerging technologies. The findings of this study are of interest to philosophers, ethicists, and other stakeholders who work in the areas of moral decision-making under uncertainty, artificial intelligence, and data ethics.

Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs. This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC. A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation. Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030.