Machines as teammates: A research agenda on AI in team collaboration

Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

The human-centered AI approach posits a future in which the work done by humans and machines will become ever more interactive and integrated. This article takes human-centered AI one step further. It argues that the integration of human and machine intelligence is achievable only if human organizations—not just individual human workers—are kept “in the loop.” We support this argument with evidence of two case studies in the area of predictive maintenance, by which we show how organizational practices are needed and shape the use of AI/ML. Specifically, organizational processes and outputs such as decision-making workflows, etc. directly influence how AI/ML affects the workplace, and they are crucial for answering our first and second research questions, which address the pre-conditions for keeping humans in the loop and for supporting continuous and reliable functioning of AI-based socio-technical processes. From the empirical cases, we extrapolate a concept of “keeping the organization in the loop” that integrates four different kinds of loops: AI use, AI customization, AI-supported original tasks, and taking contextual changes into account. The analysis culminates in a systematic framework of keeping the organization in the loop look based on interacting organizational practices.

State of science: models and methods for understanding and enhancing teams and teamwork in complex sociotechnical systems

This state of the science review brings together the disparate literature of effective strategies for enhancing and accelerating team performance. The review evaluates and synthesises models and proposes recommended avenues for future research. The two major models of the Input-Mediator-Output-Input (IMOI) framework and the Big Five dimensions of teamwork were reviewed and both will need significant development for application to future teams comprising non-human agents. Research suggests that a multi-method approach is appropriate for team measurements, such as the integration of methods from self-report, observer ratings, event-based measurement and automated recordings. Simulations are recommended as the most effective team-based training interventions. The impact of new technology and autonomous agents is discussed with respect to the changing nature of teamwork. In particular, whether existing teamwork models and measures are suitable to support the design, operation and evaluation of human-nonhuman teams of the future. Practitioner summary: This review recommends a multi-method approach to the measurement and evaluation of teamwork. Team models will need to be adapted to describe interaction with non-human agents, which is what the future is most likely to hold. The most effective team training interventions use simulation-based approaches.

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.

Artificial intelligence and the conduct of literature reviews

Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines. To familiarize researchers with some of the recent trends in this area, we outline how AI can expedite individual steps of the literature review process. Considering that the use of AI in this context is in an early stage of development, we propose a comprehensive research agenda for AI-based literature reviews (AILRs) in our field. With this agenda, we would like to encourage design science research and a broader constructive discourse on shaping the future of AILRs in research.

Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research

The application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00146-021-01239-4.

Improving Teamwork Competencies in Human-Machine Teams: Perspectives From Team Science

In response to calls for research to improve human-machine teaming (HMT), we present a “perspective” paper that explores techniques from computer science that can enhance machine agents for human-machine teams. As part of this paper, we (1) summarize the state of the science on critical team competencies identified for effective HMT, (2) discuss technological gaps preventing machines from fully realizing these competencies, and (3) identify ways that emerging artificial intelligence (AI) capabilities may address these gaps and enhance performance in HMT. We extend beyond extant literature by incorporating recent technologies and techniques and describing their potential for contributing to the advancement of HMT.

Das LEaD-Kompetenzmodell – wirksam Führen im Kontext der digitalen Transformation

Dieser Beitrag der Zeitschrift Gruppe. Interaktion. Organisation. (GIO) stellt die Entwicklung, empirische Validierung und Anwendung des LEaD-Modells©1 (Leadership Effectiveness and Development) der Führung im Rahmen der digitalen Transformation vor. Die Auswahl und Entwicklung von Führungskräften und die dazu erforderliche evidenzbasierte Diagnostik von Führungskompetenzen stellen zentrale Herausforderungen einer modernen HR-Strategie dar, wie z. B. beidhändige Führung im Kontext der digitalen Transformation und entwicklungsorientierte Diagnostik. Im Kontext des digitalen Wandels ist „LEaD“ ein Instrument für Führungskräfte und HR-Spezialist*innen, um Transformationsprozesse durch gezieltes Kompetenzmanagement erfolgreich zu bewältigen und eine neue Führungskultur zu etablieren. Praktische Anwendungsbereiche des LEaD-Modells in Organisationen sind sowohl auf individueller (z. B. als Führungsfeedback mit Selbst- und Fremdeinschätzung) als auch organisationaler Ebene (z. B. Messung der Führungseffektivität) möglich.

Dieser Beitrag beschreibt das LEaD-Modell der Führung als einen evidenzbasierten Handlungsrahmen für Führungskräfte, der Diagnose- und Interventionsmaßnahmen bietet und damit einen wichtigen Beitrag zu einer nachhaltigen und lohnenswerten Investition in Führung leistet.

The Role and Meaning of the Digital Transformation As a Disruptive Innovation on Small and Medium Manufacturing Enterprises

The research reported in this paper explores the impact of digital transformation as a disruptive innovation on manufacturing SMEs. The research is based on a qualitative Delphi study encompassing 49 experts from eleven EU countries. The paper aims to demonstrate how disruptive innovations affect organizational changes and determine critical factors in organizations that impact the initiating and promoting R&D of disruptive innovation. We discovered that disruptive innovations impact product/process development methods, new production concepts, new materials for products, and new organization plans. Additionally, we identified organizational changes related to the development and use of disruptive innovations in the future. We also indicate how disruptive innovations influence social and technological changes in the organizational environment. The analysis also disclosed three main groups of disruptive innovations and their impact on future smart factory development, namely the following: technological changes, the emergence of innovative products, business models and solutions and organizational culture as one of the crucial key success factors. The analysis also examined the enablers of the successful development/introduction of disruptive innovations, wherein internal and external factors were determined. Additionally, we presented obstacles and the approaches necessary to mitigate them. We can conclude from the findings that in the timeframe of 5-10 years, only the SME that uses/develops disruptive innovations will survive in the market. However, the companies do not always have a clear idea of the meaning of disruptive innovations. Therefore, it is important to set clear goals regarding the achievement of disruptive innovations in companies. It is also necessary to creatively apply presented instruments enabling improvement of organizational changes and apply some additional concepts, which we have suggested.

Einsatz eines virtuellen Kollaborators in analogen & digitalen Workshops im organisationalen Kontext

In dieser Studie stellen wir Unterschiede und Gemeinsamkeiten in der analogen sowie digitalen Zusammenarbeit hinsichtlich eines virtuellen Kollaborators (VK) dar. Konkret beobachten wir eine Kreativeinheit in einem Industrieunternehmen sowohl in der analogen als auch in der digitalen Durchführung kollaborativer Workshops. Aus den daraus resultierenden Herausforderungen und Anforderungen, die wir anhand von Interviews erheben, leiten wir Designprinzipien an einen VK ab und ziehen einen Vergleich. Gemeinsamkeiten bestehen darin, den Teilnehmenden zusätzliche Informationen und kreativen Input aus internetbasierten Quellen zu liefern. Unterschiede bestehen in der administrativen Vor- und Nachbereitung der jeweiligen Workshops sowie in der Art der Beeinflussung kollaborativer Arbeit. Während bei der digitalen Durchführung eher die Perspektiverweiterung im Vordergrund steht, ist es bei der analogen Durchführung die Ausbalancierung der Redebeiträge. Spezifika stellen sich darüber hinaus für die digitale Durchführung bei der Vernetzung der Teilnehmenden sowie beim Umgang mit digitalen Werkzeugen.

Studying human-AI collaboration protocols: the case of the Kasparov's law in radiological double reading

Purpose

The integration of Artificial Intelligence into medical practices has recently been advocated for the promise to bring increased efficiency and effectiveness to these practices. Nonetheless, little research has so far been aimed at understanding the best human-AI interaction protocols in collaborative tasks, even in currently more viable settings, like independent double-reading screening tasks.

Methods

To this aim, we report about a retrospective case–control study, involving 12 board-certified radiologists, in the detection of knee lesions by means of Magnetic Resonance Imaging, in which we simulated the serial combination of two Deep Learning models with humans in eight double-reading protocols. Inspired by the so-called Kasparov’s Laws, we investigate whether the combination of humans and AI models could achieve better performance than AI models alone, and whether weak reader, when supported by fit-for-use interaction protocols, could out-perform stronger readers.

Results

We discuss two main findings: groups of humans who perform significantly worse than a state-of-the-art AI can significantly outperform it if their judgements are aggregated by majority voting (in concordance with the first part of the Kasparov’s law); small ensembles of significantly weaker readers can significantly outperform teams of stronger readers, supported by the same computational tool, when the judgments of the former ones are combined within “fit-for-use” protocols (in concordance with the second part of the Kasparov’s law).

Conclusion

Our study shows that good interaction protocols can guarantee improved decision performance that easily surpasses the performance of individual agents, even of realistic super-human AI systems. This finding highlights the importance of focusing on how to guarantee better co-operation within human-AI teams, so to enable safer and more human sustainable care practices.

Organizational science and cybersecurity: abundant opportunities for research at the interface

Cybersecurity is an ever-present problem for organizations, but organizational science has barely begun to enter the arena of cybersecurity research. As a result, the "human factor" in cybersecurity research is much less studied than its technological counterpart. The current manuscript serves as an introduction and invitation to cybersecurity research by organizational scientists. We define cybersecurity, provide definitions of key cybersecurity constructs relevant to employee behavior, illuminate the unique opportunities available to organizational scientists in the cybersecurity arena (e.g., publication venues that reach new audiences, novel sources of external funding), and provide overall conceptual frameworks of the antecedents of employees' cybersecurity behavior. In so doing, we emphasize both end-users of cybersecurity in organizations and employees focused specifically on cybersecurity work. We provide an expansive agenda for future organizational science research on cybersecurity-and we describe the benefits such research can provide not only to cybersecurity but also to basic research in organizational science itself. We end by providing a list of potential objections to the proposed research along with our responses to these objections. It is our hope that the current manuscript will catalyze research at the interface of organizational science and cybersecurity.

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.

Challenges of upgrading craft workforce into Construction 4.0: framework and agreements

Craft workforce is the main productive factor in traditional construction. Construction 4.0 visions are based on automation and digitalisation, meaning that human site activities will require/stipulate changes. The extent to which manual tasks done by humans in construction will be replaced is uncertain. This might vary considering the context or type of work. Construction 4.0 includes craft workforce activities, as these can benefit from technology, fostering digital transformation in the short/medium term. The research scope is workforce–innovation–management using data from job sites. A framework is developed based on data future use involving electronic performance monitoring, building information modelling, smart contracts and artificial intelligence. A systematic scoping review is developed to identify legal/ethical issues in connection to technological aspects. The discussion and findings focus on General Data Protection Regulation compliance to apply the proposed framework. Optimised human–machine-controlled environments must be ethically managed by pre-established collective agreements and must rely on each worker’s awareness and consent. The findings suggest that the human aspects if improperly addressed could result in a bottleneck of digital transformation advances. Along with the framework, the paper provides a step-by-step, streamlined review of the regulations and requirements that need to be considered when implementing electronic monitoring of workers.

Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective

The implementation of Industry 4.0 and smart factory concepts changes the ways of manufacturing and production and requires the combination and interaction of different technologies and systems. The need for rapid implementation is steadily increasing as customers demand individualized products which are only possible if the production unit is smart and flexible. However, an existing factory cannot be transformed easily into a smart factory, especially not during operational mode. Therefore, designers and engineers require solutions which help to simulate the aspired change beforehand, thus running realistic pre-tests without disturbing operations and production. New product lines may also be tested beforehand. Data and the deduced knowledge are key factors of the said transformation. One idea for simulation is applying artificial intelligence, in this case the method of multi-agent-systems (MAS), to simulate the inter-dependencies of different production units based on individually configured orders. Once the smart factory is running additional machine learning methods for feedback data of the different machine units may be applied for generating knowledge for improvement of processes and decision making. This paper describes the necessary interaction of manufacturing and knowledge-based solutions before showing an MAS use case implementation of a production line using Anylogic.

Artificial intelligence, real-time feedback and workplace learning analytics to support in situ complex problem-solving: a commentary

Purpose

To help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).

Design/methodology/approach

Complex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).

Findings

In this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.

Originality/value

The commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.