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With the conceptualization of the blockchain as a socio-technical assemblage, this study aims to critically examine the blockchain initiatives in Korea in terms of the opportunities, risks and challenges embedded in their development.

This paper analyzes blockchain design and development from socioecological views: social, technological and cultural phenomena that represent the strategic interaction among people, technology and society. The qualitative data were collected from a variety of sources and diverse means.

The results imply that blockchain needs a close socio-technical examination to avoid simplistic assumptions of its promises and pitfalls. The development of blockchains in Korea will need to consider a range of socio-technical issues to facilitate the best outcomes for blockchain in society.

Despite proactive drives, new risks, such as security, privacy and transparency, emerge as critical concerns of the social implications of the blockchain and their impact on the new digital environment. Questions are raised as to how to govern blockchains, and how to address the unexpected outcomes that such a policy choice may have on society and industry.

The sociopolitical implications of Korean blockchains are examined to identify key concerns and issues as the country progresses rapidly toward a blockchain-driven society.

The purpose of this paper is to conduct a dual-level (organizational and individual user) analysis of development related to the Internet of Medical Things (IoMT). It examines the organizational dynamics of IoMT and develops a conceptual model for quality of experience (QoE) in user acceptance.

This study uses the information systems success model of quality factor as an analytical framework and extends it beyond the individual user experience (UX) to include an organization-level perspective. Interviews are conducted with relevant stakeholders for sociotechnical inquiries; a survey identifies user factors in IoMT.

The sociotechnical analysis sheds light on how IoMT has been accepted and stabilized in the healthcare sector. It shows the complex interaction between the social and technical aspects of IoMT by highlighting the co-evolution, interaction, and interface of devices that constitute the next-generation network environment. The UX model conceptualizes QoE specific to medical informatics.

Given the sociotechnical nature of this investigation, another approach to adoption of IoMT innovations was worth investigating to determine effective integration.

IoMT needs to be meaningful if they are to be sustainable and they need to offer quality of services and QoE no matter the location or demographic in which they are used.

With a dual-level analysis, the study provides a comprehensive view of the IoMT development process by investigating the organizational dynamics, in addition to the UX, of IoMT. The results provide a basis for developing future IoMT services with QoE requirements, as well as for clarifying sociotechnical dynamics.

This study takes an affordance approach to explain how users perceive the affordance of user action within blockchain and examines how it influences the subsequent user experience. Focusing on the effect of trust on cognitive processes, the authors analyze how affordances in blockchains affect the user experience.

The blockchain affordances are examined through a two-stage process. The authors employ a qualitative analysis based on insights gained from the current literature and interviews. The authors then apply a quantitative survey to examine the role of trust in interactions with blockchain services. A structural user model was tested in which their appreciation of affordances of blockchain predicted the trust and satisfaction.

Users' appreciation for transparency and reliability explained to what extent they trust and are satisfied, thereby suggesting the heuristic roles of trust in blockchains. The study findings indicate a heuristic role for trust regarding underlying links to technological and affective affordances. A user's cognitive heuristics affect their attitudes toward blockchain, in which technological features are processed through users' perceptions and experience.

The model contributes to the conceptualization of security, privacy and traceability along with trust, which is then linked to transparency and reliability. The findings show how the frame of affordances gains explanatory power by being linked to the concepts of affect and emotion. The heuristics of direct perception of security–traceability–privacy (STP) can be used to understand the trajectory of heuristics and ongoing choices of blockchain.

The study results offer a lens through which to address the technology's most common problems by pairing user experience principles and heuristics to blockchain technologies. This study offers insights into the understanding of user actions related to blockchains and into practical implications for developing trust-based services. The results guide the application and tailoring of motivational affordances in blockchain.

While blockchain technology has gained popularity and momentum, there has been little research on how specific features of blockchain technology create value. This study contributes to the research gap by highlighting the role and dimension of trust in relation to STP in blockchains and provides meaningful implications for theory and practice.

As algorithms permeate nearly every aspect of digital life, artificial intelligence (AI) systems exert a growing influence on human behavior in the digital milieu. Despite its popularity, little is known about the roles and effects of algorithmic literacy (AL) on user acceptance. The purpose of this study is to contextualize AL in the AI environment by empirically examining the role of AL in developing users' information processing in algorithms. The authors analyze how users engage with over-the-top (OTT) platforms, what awareness the user has of the algorithmic platform and how awareness of AL may impact their interaction with these systems.

This study employed multiple-group equivalence methods to compare two group invariance and the hypotheses concerning differences in the effects of AL. The method examined how AL helps users to envisage, understand and work with algorithms, depending on their understanding of the control of the information flow embedded within them.

Our findings clarify what functions AL plays in the adoption of OTT platforms and how users experience algorithms, particularly in contexts where AI is used in OTT algorithms to provide personalized recommendations. The results point to the heuristic functions of AL in connection with its ties in trust and ensuing attitude and behavior. Heuristic processes using AL strongly affect the credibility of recommendations and the way users understand the accuracy and personalization of results. The authors argue that critical assessment of AL must be understood not just about how it is used to evaluate the trust of service, but also regarding how it is performatively related in the modeling of algorithmic personalization.

The relation of AL and trust in an algorithm lends strategic direction in developing user-centered algorithms in OTT contexts. As the AI industry has faced decreasing credibility, the role of user trust will surely give insights on credibility and trust in algorithms. To better understand how to cultivate a sense of literacy regarding algorithm consumption, the AI industry could provide examples of what positive engagement with algorithm platforms looks like.

User cognitive processes of AL provide conceptual frameworks for algorithm services and a practical guideline for the design of OTT services. Framing the cognitive process of AL in reference to trust has made relevant contributions to the ongoing debate surrounding algorithms and literacy. While the topic of AL is widely recognized, empirical evidence on the effects of AL is relatively rare, particularly from the user's behavioral perspective. No formal theoretical model of algorithmic decision-making based on the dual processing model has been researched.

The purpose of this study is to offer a roadmap for work on the ethical and societal implications of algorithms and AI. Based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems in Korea, this work conducts socioecological evaluations of the governance of algorithmic transparency and accountability.

This paper analyzes algorithm design and development from critical socioecological angles: social, technological, cultural and industrial phenomena that represent the strategic interaction among people, technology and society, touching on sensitive issues of a legal, a cultural and an ethical nature.

Algorithm technologies are a part of a social ecosystem, and its development should be based on user interests and rights within a social and cultural milieu. An algorithm represents an interrelated, multilayered ecosystem of networks, protocols, applications, services, practices and users.

Value-sensitive algorithm design is proposed as a novel approach for designing algorithms. As algorithms have become a constitutive technology that shapes human life, it is essential to be aware of the value-ladenness of algorithm development. Human values and social issues can be reflected in an algorithm design.

The arguments in this study help ensure the legitimacy and effectiveness of algorithms. This study provides insight into the challenges and opportunities of algorithms through the lens of a socioecological analysis: political discourse, social dynamics and technological choices inherent in the development of algorithm-based ecology.

This study examined how people assess health information from AI and improve their diagnostic ability to identify health misinformation. The proposed model was designed to test a cognitive heuristic theory in misinformation discernment.

We proposed the heuristic-systematic model to assess health misinformation processing in the algorithmic context. Using the Analysis of Moment Structure (AMOS) 26 software, we tested fairness/transparency/accountability (FAccT) as constructs that influence the heuristic evaluation and systematic discernment of misinformation by users. To test moderating and mediating effects, PROCESS Macro Model 4 was used.

The effect of AI-generated misinformation on people’s perceptions of the veracity of health information may differ according to whether they process misinformation heuristically or systematically. Heuristic processing is significantly associated with the diagnosticity of misinformation. There is a greater chance that misinformation will be correctly diagnosed and checked, if misinformation aligns with users’ heuristics or is validated by the diagnosticity they perceive.

When exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation.

Perceived diagnosticity exerts a key role in fostering misinformation literacy, implying that improving people’s perceptions of misinformation and AI features is an efficient way to change their misinformation behavior.

Although there is broad agreement on the need to control and combat health misinformation, the magnitude of this problem remains unknown. It is essential to understand both users’ cognitive processes when it comes to identifying health misinformation and the diffusion mechanism from which such misinformation is framed and subsequently spread.

The mechanisms through which users process and spread misinformation have remained open-ended questions. This study provides theoretical insights and relevant recommendations that can make users and firms/institutions alike more resilient in protecting themselves from the detrimental impact of misinformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0167

The purpose of this paper is to examine the effects of interaction techniques (e.g. swiping and tapping) and the range of thumb movement on interactivity, engagement, attitude, and behavioral intention in single-handed interaction with smartphones.

A 2×2 between-participant experiment (technological features: swiping and tapping×range of thumb movement: wide and narrow) was conducted to study the effects of interaction techniques and thumb movement ranges.

The results showed that the range of thumb movement had significant effects on perceived interactivity, engagement, attitude, and behavioral intention, whereas no effects were observed for interaction techniques. A narrow range of thumb movement had more influence on the interactivity outcomes in comparison to a wide range of thumb movement.

While the subject of actual and perceived interactivity has been discussed, the issue has not been applied to smartphone. Based on the research results, the mobile industry may come up with a design strategy that balances feature- and perception-based interactivity.

This study adopted the perspective of the hybrid definition of interactivity, which includes both actual and perceived interactivity. Interactivity effect outcomes mediated by perceived interactivity.

How does algorithmic information processing affect the thoughts and behavior of artificial intelligence (AI) users? In this study, the authors address this question by focusing on algorithm-based chatbots and examine the influence of culture on algorithms as a form of digital intermediation.

The authors conducted a study comparing the United States (US) and Japan to examine how users in the two countries perceive the features of chatbot services and how the perceived features affect user trust and emotion.

Clear differences emerged after comparing algorithmic information processes involved in using and interacting with chatbots. Major attitudes toward chatbots are similar between the two cultures, although the weights placed on qualities differ. Japanese users put more weight on the functional qualities of chatbots, and US users place greater emphasis on non-functional qualities of algorithms in chatbots. US users appear more likely to anthropomorphize and accept explanations of algorithmic features than Japanese users.

Different patterns of chatbot news adoption reveal that the acceptance of chatbots involves a cultural dimension as the algorithms reflect the values and interests of their constituencies. How users perceive chatbots and how they consume and interact with the chatbots depends on the cultural context in which the experience is situated.

A comparative juxtaposition of cultural-algorithmic interactions offers a useful way to examine how cultural values influence user behaviors and identify factors that influence attitude and user acceptance. Results imply that chatbots can be a cultural artifact, and chatbot journalism (CJ) can be a socially contextualized practice that is driven by the user's input and behavior, which are reflections of cultural values and practices.

Although Leader-Member Exchange (LMX) has been studied within the domain of organizational psychology, it has not received major attention from information systems literature and researchers. Drawing on LMX theory and the theory of reasoned interaction, this study investigates the roles of LMX, individual member's attitude toward system adoption, supervisor influence and goal commitment on mandatory system implementation. Specifically, our model investigates the moderating role of LMX and how it can be interpreted for an individual member's system adoption in an organization.

Partial least square (PLS) technique with the field survey of 148 participants was used for this analysis.

The empirical test results in the field setting show that attitude and supervisor influence positively affect goal commitment in the system implementation. Furthermore, LMX is a significant moderator between attitude and goal commitment within organizations, but this moderating effect is not valid in the relationship between supervisor influence and goal commitment since it is a more complex process influenced by variables other than supervisor influence.

The results showed that the model in this research has high explanatory and predictive power and is valuable in offering insights and guidance for implementers initiating technology-related changes within organizations.

This study aims to analyze how the mobile virtual network operator (MVNO) market in Korea will evolve, the opportunities and/or threats of network operators and how the MVNO market will be forged in the future.

This study provides competitive analysis, market evaluation and current regulatory assessment of the Korean MVNO marketplace.

While there are many positive prospects of MVNO in Korea, a series of complicated techno‐economic issues arise.

The current development of MVNO sheds light on the growing challenges of the dynamics of industry, regulation and technology.

While MVNOs continue to flourish in some markets, their diffusion in other regions is often very limited and continues to drastically decrease.

The results suggest a sustainable ecosystem of MVNO and propose an appropriate policy to promote MVNO diffusion.