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Responsible AI is concerned with the design, implementation and use of ethical, transparent, and accountable AI technology in order to reduce biases, promote fairness, equality, and to help facilitate interpretability and explainability of outcomes, which are particularly pertinent in a healthcare context. However, the extant literature on health AI reveals significant issues regarding each of the areas of responsible AI, posing moral and ethical consequences. This is particularly concerning in a health context where lives are at stake and where there are significant sensitivities that are not as pertinent in other domains outside of health. This calls for a comprehensive analysis of health AI using responsible AI concepts as a structural lens. A systematic literature review supported our data collection and sampling procedure, the corresponding analysis, and extraction of research themes helped us provide an evidence-based foundation. We contribute with a systematic description and explanation of the intellectual structure of Responsible AI in digital health and develop an agenda for future research.

PurposeThe aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.Design/methodology/approachThis article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.FindingsThe authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.Originality/valueWhile there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.

The digitalization process and its outcomes in the 21st century accelerate transformation and the creation of sustainable societies. Our decisions, actions and even existence in the digital world generate data, which offer tremendous opportunities for revising current business methods and practices, thus there is a critical need for novel theories embracing big data analytics ecosystems. Building upon the rapidly developing research on digital technologies and the strengths that information systems discipline brings in the area, we conceptualize big data and business analytics ecosystems and propose a model that portraits how big data and business analytics ecosystems can pave the way towards digital transformation and sustainable societies, that is the Digital Transformation and Sustainability (DTS) model. This editorial discusses that in order to reach digital transformation and the creation of sustainable societies, first, none of the actors in the society can be seen in isolation, instead we need to improve our understanding of their interactions and interrelations that lead to knowledge, innovation, and value creation. Second, we gain deeper insight on which capabilities need to be developed to harness the potential of big data analytics. Our suggestions in this paper, coupled with the five research contributions included in the special issue, seek to offer a broader foundation for paving the way towards digital transformation and sustainable societies

Following the growing popularity of social commerce sites, there is an increased interest in understanding how consumers decide what products to purchase based on the available information. Consumers nowadays are confronted with the task of assessing marketer-generated (MGC) as well as user-generated information (UGC) in a range of different forms to make informed purchase-related decisions. This study examines the information types and forms that influence consumers in their decision-making process on social commerce. Building on uses and gratifications and dual-process theories, we distinguish between marketer and user generated content, and differentiate formats into informational and normative. Using a mixed methods approach that builds on an eye-tracking study, followed by semi-structured interviews with 23 participants, our results indicate significant differences in the types and format of information consumed for selected versus eliminated products. Specifically, we looked at engagement, cognitive processing, and observation of consumers, since they reveal information about the mental and processing mechanisms during decision making. We find that consumers present a number of differences in terms of these measures among the different types of content, and with respect to selected versus eliminated products. The outcomes of the interviews also serve to complement these findings, providing more detailed information about the processes and emotional states of consumers during the selection process.

Purpose In the complex ecosystem of mobile applications multiple factors have been used to explain users’ behavior, without though focusing on how different combinations of variables may affect user behavior. The purpose of this paper is to show how price value, game content quality, positive and negative emotions, gender and gameplay time interact with each other to predict high intention to download mobile games. Design/methodology/approach Building on complexity theory, the authors present a conceptual model followed by research propositions. The propositions are empirically validated through configurational analysis, employing fuzzy-set qualitative comparative analysis (fsQCA) on 531 active users of mobile games. Findings Findings identify ten solutions that explain high intention to download mobile games. Alternative paths are identified depending on the gender and the time users spend playing mobiles games. The authors highlight the role of price value and game content quality, as well as that of positive emotions, which are always core factors when present. Originality/value To identify complex interactions among the variables of interest, fsQCA is employed, differentiating from traditional studies using variance-based methods, leading to multiple solutions explaining the same outcome. None of the variables explains the intention to download on its own, but only when they combine with each other. The authors extend existing knowledge on how price value, game content quality, emotions, gender and gameplay time combine to lead to high intention to download mobile games; and present a methodology for how to bridge complexity theory with fsQCA, improving our understanding of intention to adopt mobile applications.

Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizations are increasingly turning to AI in order to gain business value following a deluge of data and a strong increase in computational capacity. Nevertheless, organizations are still struggling to adopt and leverage AI in their operations. The lack of a coherent understanding of how AI technologies create business value, and what type of business value is expected, therefore necessitates a holistic understanding. This study provides a systematic literature review that attempts to explain how organizations can leverage AI technologies in their operations and elucidate the value-generating mechanisms. Our analysis synthesizes the current literature and highlights: (1) the key enablers and inhibitors of AI adoption and use; (2) the typologies of AI use in the organizational setting; and (3) the first- and second-order effects of AI. The paper concludes with an identification of the gaps in the literature and develops a research agenda that identifies areas that need to be addressed by future studies.

This study conceptually explores the relationship between a nation’s culture and the success of utilizing various digital technologies to mitigate the spread of a pandemic, such as novel coronavirus (COVID-19). In the absence of a cure or vaccine of COVID-19, the national governments and public health authorities have been aggressively utilizing digital technologies to mitigate the pandemic spread. Given the urgency caused by COVID-19, this study highlights the importance of considering a country’s national culture in evaluating the efficacy of a given digital technology, despite how promising or groundbreaking it may sound, in combating the spread of an infectious disease. Relying on the two critical dimensions of national culture, power distance and individualism/collectivism, this study proposes a framework that describes how people from different countries, depending on their prevalent national cultural values, would be receptive (or intolerant) to using government-run technology solutions meant for curbing the pandemic spread.

Customer relationship management (CRM) is a strategic approach to manage an organization’s interaction with current and potential customers. Artificial Intelligence (AI) can analyze huge volume of data without human intervention. The integration of AI with existing legacy CRM system in the business to customer (B2C) relationship makes sense given the massive potential for growth of AI integrated CRM system. Failure to plan AI-CRM technology implementation in an organization could lead some to success and others to failure. The Contingency theory states that it is not possible for organizations to take decisions without a contingency plan and the optimal course of action depends on the internal and external circumstances. The Dynamic Capability View theory emphasizes the organizational ability to react adequately in a timely manner to any external changes and combines multiple capabilities of the organization, including organizational CRM and AI capabilities. Against this background, the purpose of this study is to examine the success and failure of implementation of AI integrated CRM system in an organization from B2C perspective using Contingency theory and Dynamic Capability View theory. The study finds that information quality, system fit, and organizational fit significantly and positively impact the implementation of AI-CRM for B2C relationship management. Also, there is a moderating impact of technology turbulence on both acceptance and failure of AI-CRM capability in the organization.

This paper tests the meta-analysis based unified theory of acceptance and use of technology (meta-UTAUT) model to predict the behavioral intentions of organizational users and their use behavior to artificial intelligence (AI) integrated customer relationship management (CRM) systems. Data was collected from 315 organizational users in India. The hypotheses draw on the theoretical underpinnings which have been statistically validated. Results show that CRM quality and satisfaction significantly influences an organization’s employees attitudes and intentions to use AI integrated CRM systems. The compatibility of CRM systems has, however, a limited impact on employees attitudes. The findings, which are aligned with the extended UTAUT model, provide useful insights into organizations and decision-makers for designing AI integrated CRM systems.