AI-Based Digital Assistants: Opportunities, Threats, and Research Perspectives

Fluid teams in the metaverse: exploring the (un)familiar

The metaverse is a new and evolving environment for fluid teams and their coordination in organizations. Fluid teams may have no prior familiarity with each other or working together. Yet fluid teams are known to benefit from a degree of familiarity-knowledge about teams, members, and working together-in team coordination and performance. The metaverse is unfamiliar territory that promises fluidity in contexts-seamless traversal between physical and virtual worlds. This fluidity in contexts has implications for familiarity in interaction, identity, and potentially time. We explore the opportunities and challenges that the metaverse presents in terms of (un)familiarity. Improved understandings of (un)familiarity may pave the way for new forms of fluid team experiences and uses.

Artificial Intelligence for Cancer Detection-A Bibliometric Analysis and Avenues for Future Research

After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer.

The extended reality technology (ERT) framework for designing customer and service experiences in phygital settings: a service research agenda

Purpose

Because new-age technologies are gaining a broader interest among service scholars and practitioners, it is critical to identify these technologies and examine the roles they play. The examination needs to be conducted to design engaging customer and service experiences in new phygital settings that connect physical and digital environments. This review article aims to provide researchers with a new comprehensive and integrative extended reality technology (ERT) framework. The framework serves as the basis for an all-inclusive view of ERT types in order to explore the different types of technology used to design phygital customer and service experiences.

Design/methodology/approach

This article reviews prior works on the role technology plays in terms of customer experiences across various fields of research, including consumer, marketing and service literature. Adopting an experiential and phygital perspective as well as considering a consumer standpoint, this article defines the scope of the ERT framework by identifying categories of new-age technologies and their effects related to the design of phygital customer and service experiences.

Findings

The ERT framework proposed in this article offers directions for future research by adopting an experiential approach to technologies in order to categorize additional technological devices, platforms and tools that can be considered in the design of phygital experiences following several extension processes. These processes can enhance the cognitive, social, sensory and contextual dimensions of the phygital experience and thus create a continuum in terms of customer value from physical to digital settings and vice versa.

Research limitations/implications

Companies and service providers may benefit from a new, comprehensive, focused framework that assembles different types of technology. The technologies can be utilized to design engaging customer and service experiences that deliver customer value from physical to digital spaces and inversely.

Originality/value

No prior works have proposed a comprehensive ERT framework for service research following an experiential perspective and a consumer view of the experience occurring in a new setting: phygital. By embracing the ERT framework provided in this article, future service scholars can examine the dynamics and types of technologies that can positively or negatively affect the design of consumption and service experiences in phygital settings.

Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges

This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.

A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives

Intelligent mobile robots that can move independently were laid out in the real world around 100 years ago during the second world war after advancements in computer science. Since then, mobile robot research has transformed robotics and information engineering. For example, robots were crucial in military applications, especially in teleoperations, when they emerged during the second world war era. Furthermore, after the implementation of artificial intelligence (AI) in robotics, they became autonomous or more intelligent. Currently, mobile robots have been implemented in many applications like defense, security, freight, pattern recognition, medical treatment, mail delivery, infrastructure inspection and developments, passenger travel, and many more because they are more intelligent nowadays with artificial intelligence technology. To study the developments of mobile robots, we have studied an extensive literature survey of the last 50 years. In this article, we discuss a complete century of mobile robotics research, major sensors used in robotics, some major applications of mobile robots, and their impact on our lives and in applied engineering.

When Self-Humanization Leads to Algorithm Aversion

Decision support systems are increasingly being adopted by various digital platforms. However, prior research has shown that certain contexts can induce algorithm aversion, leading people to reject their decision support. This paper investigates how and why the context in which users are making decisions (for-profit versus prosocial microlending decisions) affects their degree of algorithm aversion and ultimately their preference for more human-like (versus computer-like) decision support systems. The study proposes that contexts vary in their affordances for self-humanization. Specifically, people perceive prosocial decisions as more relevant to self-humanization than for-profit contexts, and, in consequence, they ascribe more importance to empathy and autonomy while making decisions in prosocial contexts. This increased importance of empathy and autonomy leads to a higher degree of algorithm aversion. At the same time, it also leads to a stronger preference for human-like decision support, which could therefore serve as a remedy for an algorithm aversion induced by the need for self-humanization. The results from an online experiment support the theorizing. The paper discusses both theoretical and design implications, especially for the potential of anthropomorphized conversational agents on platforms for prosocial decision-making.

Do Men Have No Need for “Feminist” Artificial Intelligence? Agentic and Gendered Voice Assistants in the Light of Basic Psychological Needs

Artificial Intelligence (AI) is supposed to perform tasks autonomously, make competent decisions, and interact socially with people. From a psychological perspective, AI can thus be expected to impact users’ three Basic Psychological Needs (BPNs), namely (i) autonomy, (ii) competence, and (iii) relatedness to others. While research highlights the fulfillment of these needs as central to human motivation and well-being, their role in the acceptance of AI applications has hitherto received little consideration. Addressing this research gap, our study examined the influence of BPN Satisfaction on Intention to Use (ITU) an AI assistant for personal banking. In a 2×2 factorial online experiment, 282 participants (154 males, 126 females, two non-binary participants) watched a video of an AI finance coach with a female or male synthetic voice that exhibited either high or low agency (i.e., capacity for self-control). In combination, these factors resulted either in AI assistants conforming to traditional gender stereotypes (e.g., low-agency female) or in non-conforming conditions (e.g., high-agency female). Although the experimental manipulations had no significant influence on participants’ relatedness and competence satisfaction, a strong effect on autonomy satisfaction was found. As further analyses revealed, this effect was attributable only to male participants, who felt their autonomy need significantly more satisfied by the low-agency female assistant, consistent with stereotypical images of women, than by the high-agency female assistant. A significant indirect effects model showed that the greater autonomy satisfaction that men, unlike women, experienced from the low-agency female assistant led to higher ITU. The findings are discussed in terms of their practical relevance and the risk of reproducing traditional gender stereotypes through technology design.

The Cost of Fairness in AI: Evidence from E-Commerce

Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness” have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness.

Optimizing human hand gestures for AI-systems

Humans interact more and more with systems containing AI components. In this work, we focus on hand gestures such as handwriting and sketches serving as inputs to such systems. They are represented as a trajectory, i.e. sequence of points, that is altered to improve interaction with an AI model while keeping the model fixed. Optimized inputs are accompanied by instructions on how to create them. We aim to cut on effort for humans and recognition errors while limiting changes to original inputs. We derive multiple objectives and measures and propose continuous and discrete optimization methods embracing the AI model to improve samples in an iterative fashion by removing, shifting and reordering points of the gesture trajectory. Our quantitative and qualitative evaluation shows that mimicking generated proposals that differ only modestly from the original ones leads to lower error rates and requires less effort. Furthermore, our work can be easily adjusted for sketch abstraction improving on prior work.

Hope, tolerance and empathy: employees' emotions when using an AI-enabled chatbot in a digitalised workplace

Purpose

Information Systems research on emotions in relation to using technology largely holds essentialist assumptions about emotions, focuses on negative emotions and treats technology as a token or as a black box, which hinders an in-depth understanding of distinctions in the emotional experience of using artificial intelligence (AI) technology in context. This research focuses on understanding employees' emotional experiences of using an AI chatbot as a specific type of AI system that learns from how it is used and is conversational, displaying a social presence to users. The research questions how and why employees experience emotions when using an AI chatbot, and how these emotions impact its use.

Design/methodology/approach

An interpretive case study approach and an inductive analysis were adopted for this study. Data were collected through interviews, documents review and observation of use.

Findings

The study found that employee appraisals of chatbots were influenced by the form and functional design of the AI chatbot technology and its organisational and social context, resulting in a wider repertoire of appraisals and multiple emotions. In addition to positive and negative emotions, users experienced connection emotions. The findings show that the existence of multiple emotions can encourage continued use of an AI chatbot.

Originality/value

This research extends information systems literature on emotions by focusing on the lived experiences of employees in their actual use of an AI chatbot, while considering its characteristics and its organisational and social context. The findings inform the emerging literature on AI.

Design and Implementation of Advanced Machine Learning Management and Its Impact on Better Healthcare Services: A Multiple Regression Analysis Approach (MRAA)

In the current information and technology era, business enterprises are focusing in performing the process effectively by reducing the waiting time in completing the work, reduce latency and deploy the resources effectively so as to service the patient, medical practitioners, societies, and other stakeholders in an efficient manner. Hence, several organisations are using the emerging technologies so as to obtain high performance and enhance competitive edge. The advancement in machine learning, deep learning, business analytics, etc. enables the health care industry to identify the patterns based on the data collected and create a pivotal position and enhance revenues and profits in a sustainable manner. Machine learning models are considered as computational algorithms which will enable in collected the data, analyze them, and provide the necessary reports to the experts and management in order to make informed decision making. The application of advanced machine learning enables the organisation to process the image effectively, recognize the voice and enable in servicing the customers, process the available data, and identify the patterns so as to make informed decision making. The basic purpose of the study is to analyze the overall implementation of advanced machine learning approaches towards health care services for providing enhanced services, better patient engagement, and support in creating better life for them, the researchers intend to collect the closed-ended questionnaire from employees in different medical centers covering: apprehend the nature of designing and implementation of machine learning approaches in the organisation and understand the effectiveness of these tools in enhancing the sustainable growth and development of the organisation.

Intelligent Assistants

Intelligent assistants are an increasingly commonplace class of information systems spanning a broad range of form and complexity. But what characterizes an intelligent assistant, and how do we design better assistants? In the paper, the authors contribute to scientific research in the domain of intelligent assistants in three steps, each building on the previous. First, they investigate the historical context of assistance as human work. By examining qualitative studies regarding the work of human assistants, the authors inductively derive concepts crucial to modeling the context of assistance. This analysis informs the second step, in which they develop a conceptual typology of intelligent assistants using 111 published articles. This typology explicates the characteristics (what or how) of intelligent assistants and their use context (who or which). In the third and final step, the authors utilize this typology to shed light on historical trends and patterns in design and evaluation of intelligent assistants, reflect on missed opportunities, and discuss avenues for further exploration.

"Compoundless Anaesthesia", Controlled Administration, and Post-Operative Recovery Acceleration: Musings on Theoretical Nanomedicine Applications

Much research has been conducted on how patients may be served through new advances in perioperative anaesthetic care. However, adaptations of standardised care methodologies can only provide so many novel solutions for patients and caregivers alike. Similarly, unique methods such as nanoscopic liposomal package delivery for analgesics and affective numbing agents pose a similar issue-specifically that we are still left with the dilemma of patients for whom analgesics and numbing agents are ineffective or harmful. An examination of the potential gains that may result from the targeted development of nanorobotics for anaesthesia in perioperative care will be presented in this essay to help resolve this pending conflict for the research community. This examination should therefore serve as a "call to action" for such research and a "primer" for those for whom the method's implementation would most directly impact.

Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

The association between digitalization and mental health: The mediating role of wellbeing at work

The study aims to measure the mediating relationship of wellbeing at work between digitalization (IT infrastructure, IT business spanning, and IT proactive stance) and their effect on mental health. The study uses primary data collection techniques to gather data and used purposive sampling to analyze the data. The sample size of the study is 349 respondents. The research uses Smart PLS software to measure the relationship through bootstrapping and algorithms. The study finds a significant positive mediating role of wellbeing between digitalization (IT infrastructure, IT business spanning, and IT proactive stance) and their effect on mental health. The study outcomes are helpful for managers and policymakers.

Immunizing with information - Inoculation messages against conversational agents' response failures

Conversational agents (CAs) are often unable to provide meaningful responses to user requests, thereby triggering user resistance and impairing the successful diffusion of CAs. Literature mostly focuses on improving CA responses but fails to address user resistance in the event of further response failures. Drawing on inoculation theory and the elaboration likelihood model, we examine how inoculation messages, as communication that seeks to prepare users for a possible response failure, can be used as an alleviation mechanism. We conducted a randomized experiment with 558 users, investigating how the performance level (high or low) and the linguistic form of the performance information (qualitative or quantitative) affected users' decision to discontinue CA usage after a response failure. We found that inoculation messages indicating a low performance level alleviate the negative effects of CA response failures on discontinuance. However, quantitative performance level information exhibits this moderating effect on users' central processing, while qualitative performance level information affected users' peripheral processing. Extending studies that primarily discuss ex-post strategies, our results provide meaningful insights for practitioners.

Collaborating with Virtual Assistants in Organizations: Analyzing Social Loafing Tendencies and Responsibility Attribution

Organizations increasingly introduce collaborative technologies in form of virtual assistants (VAs) to save valuable resources, especially when employees are assisted with work-related tasks. However, the effect of VAs on virtual teams and collaboration remains uncertain, particularly whether employees show social loafing (SL) tendencies, i.e., applying less effort for collective tasks compared to working alone. While extant research indicates that VAs collaboratively working in teams exert greater results, less is known about SL in virtual collaboration and how responsibility attribution alters. An online experiment with N = 102 was conducted in which participants were assisted by a VA in solving a task. The results indicate SL tendencies in virtual collaboration with VAs and that participants tend to cede responsibility to the VA. This study makes a first foray and extends the information systems (IS) literature by analyzing SL and responsibility attribution thus updates our knowledge on virtual collaboration with VAs.

Emerging Digital Technologies to Combat Future Crises: Learnings From COVID-19 to be Prepared for the Future

In 2020, the world has witnessed an unprecedented global pandemic with COVID-19. It has led nations to take measures that have an enormous impact on individuals, society, and the economy. Researchers and practitioners responded rapidly, evaluating the opportunities to capitalize on technology for tackling the associated challenges. We investigate the innovative potentials of three emerging digital technologies — namely, the Internet of Things, artificial intelligence, and distributed ledgers — to tackle pandemic-related challenges. We present our findings on the most effective means of leveraging each technology’s potential, the implications for use in crises, and the convergence of the three technologies.

Project management: openings for disruption from AI and advanced analytics

Purpose

The purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution of project management capabilities and practices.

Design/methodology/approach

This paper is written as a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures.

Findings

Neither AI nor sophisticated analytics is likely to elicit hands on attention from project managers, other than those producing AI or analytics-based artifacts or using these tools to create their products and services. However, through the conduit of packaged software support for project management, new tools and approaches can be expected to more effectively support current activities, to streamline or eliminate activities that can be automated, to extend current capabilities with the availability of increased data, computing capacity and mathematically based algorithms and to suggest ways to reconceive how projects are done and whether they are needed.

Research limitations/implications

This essay includes projections of possible, some likely and some unlikely, events and states that have not yet occurred. Although the hope and purpose are to alert readers to the possibilities of what may occur as logical extensions of current states, it is improbable that all such projections will come to pass at all or in the way described. Nonetheless, consideration of the future ranging from current trends, the interplay among intersecting trends and scenarios of future states can sharpen awareness of the effects of current choices regarding actions, decisions and plans improving the probability that the authors can move toward desired rather than undesired future states.

Practical implications

Project managers not involved personally with creating AI or analytics products can avoid mastering detailed skill sets in AI and analytics, but should scan for new software features and affordances that they can use enable new levels of productivity, net benefit creation and ability to sleep well at night.

Originality/value

This essay brings together AI, analytics and project management to imagine and anticipate possible directions for the evolution of the project management domain.

ParlTech: Transformation Framework for the Digital Parliament

Societies are entering the age of technological disruption, which also impacts governance institutions such as parliamentary organizations. Thus, parliaments need to adjust swiftly by incorporating innovative methods into their organizational culture and novel technologies into their working procedures. Inter-Parliamentary Union World e-Parliament Reports capture digital transformation trends towards open data production, standardized and knowledge-driven business processes, and the implementation of inclusive and participatory schemes. Nevertheless, there is still a limited consensus on how these trends will materialize into specific tools, products, and services, with added value for parliamentary and societal stakeholders. This article outlines the rapid evolution of the digital parliament from the user perspective. In doing so, it describes a transformational framework based on the evaluation of empirical data by an expert survey of parliamentarians and parliamentary administrators. Basic sets of tools and technologies that are perceived as vital for future parliamentary use by intra-parliamentary stakeholders, such as systems and processes for information and knowledge sharing, are analyzed. Moreover, boundary conditions for development and implementation of parliamentary technologies are set and highlighted. Concluding recommendations regarding the expected investments, interdisciplinary research, and cross-sector collaboration within the defined framework are presented.

Computer Says I Don’t Know: An Empirical Approach to Capture Moral Uncertainty in Artificial Intelligence

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of classes with distinct moral preferences and that this codification can be used to express moral uncertainty of an AI. Choice analysis allows for the identification of classes and their moral preferences based on observed choice data. Our reformulation of the metanormative framework is theory-rooted and practical in the sense that it avoids runtime issues in real time applications. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. While one of the systems uses a baseline morally certain model, the other uses a morally uncertain model. We highlight cases in which the AI Systems disagree about the policy to be chosen, thus illustrating the need to capture moral uncertainty in AI systems.

Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn

Owing to advancements in artificial intelligence (AI) and specifically in machine learning, information technology (IT) systems can support humans in an increasing number of tasks. Yet, previous research indicates that people often prefer human support to support by an IT system, even if the latter provides superior performance – a phenomenon called algorithm aversion. A possible cause of algorithm aversion put forward in literature is that users lose trust in IT systems they become familiar with and perceive to err, for example, making forecasts that turn out to deviate from the actual value. Therefore, this paper evaluates the effectiveness of demonstrating an AI-based system’s ability to learn as a potential countermeasure against algorithm aversion in an incentive-compatible online experiment. The experiment reveals how the nature of an erring advisor (i.e., human vs. algorithmic), its familiarity to the user (i.e., unfamiliar vs. familiar), and its ability to learn (i.e., non-learning vs. learning) influence a decision maker’s reliance on the advisor’s judgement for an objective and non-personal decision task. The results reveal no difference in the reliance on unfamiliar human and algorithmic advisors, but differences in the reliance on familiar human and algorithmic advisors that err. Demonstrating an advisor’s ability to learn, however, offsets the effect of familiarity. Therefore, this study contributes to an enhanced understanding of algorithm aversion and is one of the first to examine how users perceive whether an IT system is able to learn. The findings provide theoretical and practical implications for the employment and design of AI-based systems.

Understanding Collaboration with Virtual Assistants – The Role of Social Identity and the Extended Self

Organizations introduce virtual assistants (VAs) to support employees with work-related tasks. VAs can increase the success of teamwork and thus become an integral part of the daily work life. However, the effect of VAs on virtual teams remains unclear. While social identity theory describes the identification of employees with team members and the continued existence of a group identity, the concept of the extended self refers to the incorporation of possessions into one’s sense of self. This raises the question of which approach applies to VAs as teammates. The article extends the IS literature by examining the impact of VAs on individuals and teams and updates the knowledge on social identity and the extended self by deploying VAs in a collaborative setting. Using a laboratory experiment with N = 50, two groups were compared in solving a task, where one group was assisted by a VA, while the other was supported by a person. Results highlight that employees who identify VAs as part of their extended self are more likely to identify with team members and vice versa. The two aspects are thus combined into the proposed construct of virtually extended identification explaining the relationships of collaboration with VAs. This study contributes to the understanding on the influence of the extended self and social identity on collaboration with VAs. Practitioners are able to assess how VAs improve collaboration and teamwork in mixed teams in organizations.

How and What Can Humans Learn from Being in the Loop?

This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.