Collaboration Systems and Technologies

Collaboration systems have long been used to support individuals to work together to attain their goals. With the widespread popularity of social media technologies, the pervasive expansion of immediate many-to-many connections has introduced new dynamics amongst and between users, companies, and advocates that pertain to all forms of political, economic, and personal life. While these dynamics imply various positive consequences for the civil society (e.g., greatly unrestrained access to sharing and collecting information), they also introduce severe issues of coordinated adversary behaviors (e.g., coordinated inauthentic behavior, cybermobbing, distortion of the public discourse, proliferation of unreliable information, relinquished on- and offline privacy).

The rise of coordinated adversarial behaviors in collaboration and social media systems deserves more scholarly and public attention, because of their devastating consequences to individuals and society. Furthermore, system providers and platform regulators struggle to develop effective means to counteract these undesirable and destructive issues. There is also a need to conduct theory-guided studies to investigate prevention and intervention strategies for coordinated adversary behaviors in collaboration and social media systems. Thus, the aim of this minitrack is to identify, conceptualize, and assess these emergent cutting-edge issues as well as their actual or potential counter mechanisms.

The minitrack intends to inform the respective decision making of regulators and tech companies from an Information Systems standpoint. Researchers are invited to adopt a pessimistic perspective (i.e., the extent to which collaboration and social media systems cause or prolong problems) or an optimistic view (i.e., ways to prevent and mitigate these problems) when addressing coordinated adversarial behaviors in collaboration and social media systems. We also welcome papers examining the synergies of collaboration and social media systems and exploring the potential, social and other impacts of integrated systems. This minitrack is open to all types of research, conceptual, theoretical, and/or empirical. Examples of possible topics of interest include (but are not limited to):

• Cybermobbing, cyberbullying, cyberharassment, online disinhibition, hate speech, trolling, deceptive communication
• Digital vigilantism, shaming, doxing, hacktivism, human flesh search engine
• Violent online extremism and cyberterrorism
• Fake news, deception, participatory or bot-driven disinformation campaigns
• Coordinated inauthentic behavior, astroturfing, sockpuppeting
• Network polarization, echo-chambers, filter bubbles
• Prevention and intervention mechanisms, (algorithmic) content moderation, balancing the protection of civil liberties and national security, censorship, deplatforming, content monitoring
• Policy, governance, platform (self) regulation
• Privacy protection and violations, dark pattern design

High quality and relevant papers from this minitrack will be selected for fast-tracked development towards Internet Research. Selected papers will need to expand in content and length in line with the requirements for standard research articles published in the journal. Although the minitrack co-chairs are committed to guiding the selected papers towards final publication, further reviews may be needed before a final publication decision can be made. Internet Research (IntR) is an international and refereed journal that is indexed and abstracted in major databases (e.g., SSCI, SCI, ABI/INFORM Global), with an impact factor 6.353 in 2021.

Minitrack Co-Chairs:

Christy M.K. Cheung (Primary Contact)
Hong Kong Baptist University
ccheung@hkbu.edu.hk

Marten Risius
University of Queensland
m.risius@business.uq.edu.au

Matthew K.O. Lee
City University of Hong Kong
cbmatlee@cityu.edu.hk

Christian Wagner
City University of Hong Kong
c.wagner@cityu.edu.hk

This minitrack focuses on the impact of AI on the various aspects of the workplace as it exists currently as well as how it may evolve in the future. We seek papers that address the social, technical, behavioral, attitudinal, emotional, or managerial aspects of AI in the workplace. The unit of analysis can be individuals, teams, or organizations. All kinds of research are welcome including but not limited to quantitative, qualitative, conceptual, or design-oriented research.

The focus of these papers can range from the impact of AI on work and its related aspects to the design considerations of AI in the workplace. In short, this minitrack seeks to highlight research that may influence the future of work and act as a springboard for new ideas and innovations in AI that will be disruptive to the workplace.

The “AI and the Future of Work” minitrack is especially interested in the following topics:

• Power shifts between humans and AI
• AI and employees’ mental and physical wellbeing
• Shift in social/role identities with the introduction of AI
• Required skill set for human employees in an era of AI
• AI and the changing face of leadership
• Social relationships and AI at the workplace
• Integration of AI in work practices (knowledge sharing, decision making, etc.)
• Responsible and Explainable AI
• Ethical considerations of AI at the workplace
• Financial and economic implications of AI implementation in the workplace
• The changing meaning of work or work–life balance in an era of AI
• AI task appropriateness
• Designing AI for the workplace
• AI and changes in work settings
• Workplace Analytics and AI
• AI and creativity in the workplace
• Collaboration with AI
• AI mimicking human labor (ChatGPT, Next Rembrandt etc.)

Minitrack Co-Chairs:

Triparna de Vreede (Primary Contact)
University of South Florida
tdevreede@usf.edu

Xusen Cheng
Renmin University of China
xusen.cheng@ruc.edu.cn

Dominik Siemon
LUT University
Dominik.Siemon@lut.fi

Online communities consist of individuals who share a common interest and who use the internet to communicate with each other and work together in pursuit of shared interests. Individuals seek out information online for both utilitarian and hedonic reasons. Online forums are one example of a pervasive platform where individuals can submit and receive answers to questions as well as browse the experiences of others. Individuals with questions often turn to these forums, either directly or indirectly (through search engine results), to find answers to problems they face. While research has begun to address utilitarian and hedonic seeking and consumption of information, there is still much left unknown.

This minitrack focuses on research related to understanding information processing and decision making in the context of online communities. The following is a list of sample topics (non-exhaustive) that would be a good fit for this minitrack:

• How individuals search for, filter, or adopt online information
• Online decision-making processes
• Cognitive processing related to consumption of online information
• Validation of online content
• AI-generated content
• Community based cues
• Evaluation of different cue types (e.g., upvotes, star ratings)
• Design elements of tools to support online communities
• Crowdsourced knowledge
• Approaches to increase contributions/engagement
• Novel approaches to support online communities

Minitrack Co-Chairs:

Matt Jensen (Primary Contact)
University of Oklahoma
mjensen@ou.edu

Tom Meservy
Brigham Young University
tmeservy@byu.edu

Kelly Fadel
Utah State University
kelly.fadel@usu.edu

This minitrack is dedicated to the employment of collaborative robots (cobots) in emergency situations. Cobots are robots that are built for direct human-robot collaboration (HRC) in a shared environment. It focuses on the idea that collaboration between people and robots must go much farther – rather than viewing robots as tools or mobile sensors, they must be viewed as team members especially in predictable environments such as production lines, towards disaster zones, and in emergency circumstances. In the aftermath of earthquakes, accidents, avalanches, or explosions, cobots can work with human counterparts to reduce the risk to human life and increase the possibility of rescuing people.

Topics of interest include, but are not limited to:

• Search and Rescue (SAR) collaborative robots (cobots)
• Human-robot cooperation (HRC) in emergency scenarios
• Haptics for emergency scenario
• Robot control algorithms for emergency scenarios
• Robotic sensors and actuators for emergency scenarios
• Ethical issues for human-robot cooperation (HRC) in emergency scenarios
• Machine Learning (ML) and Artificial Intelligence for robotics in emergency scenarios
• Robotic simulation for emergency scenarios
• Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (ER) for robotics in emergency scenarios

Minitrack Co-Chairs:

Filippo Sanfilippo (Primary Contact)
University of Agder
filippo.sanfilippo@uia.no

Gionata Salvietti
Università degli Studi di Siena
salviettigio@diism.unisi.it

Conversational AI (CA) is becoming an important means for human-computer collaboration. CA uses massive datasets and Artificial Intelligence (AI) techniques, such as Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning (DL), to mimic human interaction. By translating the meanings of voice and text input, CA performs a wide range of functions, from assisting users in searching web resources, summarizing and translating text, to answering challenging questions based on knowledge obtained from large volumes of data. Such CA not only increases the usability of computer-based services but also brings a radical change to human-computer collaboration. However, despite its exponentially increasing adoption across industries, CA suffers from a variety of technical limitations, calling for more scholarly attention to address them. Further, with its rapid adoption, our society is facing numerous ethical issues which need to be addressed to ensure that CA is safe, secure, trustworthy, and ethically appropriate in various contexts.

This minitrack welcomes theoretical and empirical research addressing a variety of technical, social, and ethical issues relevant to a complex and multifaceted challenge of Conversational AI systems. The topics relevant to this minitrack include, but not limited to:

• Generative AI: Generative AI has been used to create various types of new content, including text, images, audio, video and synthetic data. The release of powerful generative models, such as ChatGPT, has accelerated the adoption of generative AI, calling for more research to address its various issues, including design methods, development challenges, innovative use cases, and ethical concerns.
• Natural Language Processing and Text Analytics: A wide variety of ML/DL methods along with NLP have been used to analyze voice and text in conversation. Nonetheless, existing approaches suffers from technical limitations, calling for more research to advance the state-of-the art in voice/text analytics.
• Transparency and Explainability: Conversational AI systems can be difficult to understand or explain, making it harder for people to trust them. This has led to a growing need for research on methods to increase the transparency and explainability of AI systems.
• Bias and Fairness: Conversational AI systems can perpetuate and amplify biases present in the data used to train them, which can lead to unfair and discriminatory outcomes. This has led to a growing need for research on methods to increase fairness and reduce bias in Conversational AI systems.
• Autonomy: As Conversational AI systems become more advanced, there are concerns about their potential to make decisions without human oversight or control. This has led to a growing need for research on methods to ensure the safety and accountability of autonomous AI systems.
• Privacy and Data Breaches: The use of Conversational AI can raise concerns about the collection, storage, and process of large amounts of personal sensitive data that makes them a target for data breaches and other forms of cybercrime. Methods and implications for protecting individuals’ privacy and data breaches as a result of misuse of AI systems need to be studied.
• Security and Vulnerabilities: Conversational AI systems can be vulnerable to adversarial attacks (e.g., hacking, malware, and other forms of cyber attack), which can compromise their security and the security of the systems and networks they are connected to. Attackers also manipulate input data or use other techniques to trick the system into making incorrect decisions. This has led to a growing concern about the confidentiality and integrity of Conversational AI systems, which needs research on methods to increase the robustness and security of Conversational AI systems against adversarial attacks.
• Infringement of Copyrights and Intellectual Property Rights: Conversational AI systems can be used to create and distribute unauthorized copies of copyrighted and trademarked material, making it difficult to enforce and protect such rights. In addition, the models of Conversational AI systems are trained on a large amount of data, are valuable assets, and can be stolen or replicated.
• Weaponization: AI systems are increasingly being used in weapon systems, which raises ethical questions about the use of Conversational AI in warfare and the possibility of Conversational AI being used to create autonomous weapons.

Minitrack Co-Chairs:

Dan J. Kim (Primary Contact)
University of North Texas
dan.kim@unt.edu

Victoria Yoon
Virginia Commonwealth University
vyyoon@vcu.edu

Kiseol Yang
University of North Texas
kiseol.yang@unt.edu

Manoj Thomas
University of Sydney
manoj.thomas@sydney.edu.au

Effective Cross-organizational collaboration makes firms to achieve advance innovation through timesaving and cost reduction in the Post-Corona era. ESG management initiates new forms of collaboration across industries and countries while the carbon neutrality goal motivates companies to implement digital cross-border collaboration. New missions and roles of cross-organizational and cross-border collaboration will be defined for 2050 Zero Carbon society. New methods and success stories on eco-friendly and socio-friendly collaboration will contribute to the realization of carbon neutrality.

Information Systems and Information Technology have for a long time played a major role in supporting sustainability goals and a substantial research strand on “green IS/IT” has existed for many years. However, motivated by observing positive side effects caused by the COVID-19 pandemic, such as positive ecolocical effects through less travel and more virtualized collaboration, we believe that there are many more and not yet understood effects IT and IS can have on ecological and social sustainability goals, particularly through the virtualization of communication and collaboration. While there are obvious advantages, there are also downsides and challenges which require research activities to detect, understand, and mitigate those.

Integration of people, processes, and information systems across organizational and national borders enables productive teamwork towards achieving mutual goals. People involved should be motivated to enter into collaborative projects and interactions, which lead to satisfaction and performance. Perception of value, trust, and commitment among participants and stakeholders fosters quality in collaboration. With progress in globalization and AI technology, many of these collaborations are increasing across widely dispersed organizations and national borders. Cross-system integration and intelligent collaboration technologies play crucial roles and often decide about IS project success or failure. Additional complexity and dynamics evolve through digital innovation activities in which collaborators, across organizations, countries, and cultures, work on new digital products, services, and business models – changing the way to shape innovation success. Digital collaboration has been increasing by organizations seeking to gain competitive advantage by lowering costs, increasing knowledge, and reaching new customers. Perceived value serves as a stimulus for further growth.

Possible contributions regarding the cross-organizational and cross-border collaboration may include, but are not limited to the following:

• Eco-friendly or Socio-friendly collaboration for ESG management
• Cross-organizational collaboration cases for carbon neutrality
• Processes of international and inter-organizational collaboration in IS/IT projects and operations
• Effects of collaboration on IS/IT performance
• Conceptual frameworks of IS/IT collaboration across organizations and borders
• Motivating factors for IS/IT collaboration
• Methodologies for studies of IS/IT collaboration
• Development and validation of IS/IT collaboration instrument
• IS/IT collaboration studies at country, industry, firm, project, team or individual level
• Comparative cross-country research on IT/IS collaboration
• Industry-specific and organization-specific case studies on IT/IS collaboration
• Managing IS/IT outsourcing and offshoring/nearshoring relationships
• Out-tasking and crowdsourcing in IT/IS contexts
• Cross-organizational and international IS/IT project management
• Multinational teams and their cultural and leadership factors
• IT/IS-enabled Open Innovation approaches
• Cross-organizational innovation approaches and processes
• Collaboration cases on Big-Data analytics for creating customer value
• Social collaboration using online social networks across countries
• Sustainability enabled and facilitated through digital collaboration
• The role of digital collaboration for impact sourcing and other forms of sustainability-focused outsourcing engagements
• SME case studies on social innovations and sustainability

Minitrack Co-Chairs:

Ilsang Ko (Primary Contact)
Chonnam National University
isko@chonnam.ac.kr

Daniel Beimborn
University of Bamberg
daniel.beimborn@uni-bamberg.de

Cybersecurity and Artificial Intelligence (AI) are key domains whose intersection gives great promises and poses significant threats. The nature of AI and Cybersecurity encompasses many domains. While some perspectives are narrowly focused (e.g., point solutions inside an organization identifying threats in a network stream), many are very sweeping and are either collaborative or tackle collaborative domains (e.g., identifying intentional or unintentional cybersecurity threats propagating across collaboration platforms). Indeed, enhancing our capabilities in AI for cybersecurity has been noted as a key national priority by significant entities such as the National Science Foundation, the National Science Technology Council, and the National Academies of Science.

Implementing AI and Cybersecurity can also be internal to an organization or broadly collaborative (e.g., organizations working and competing together in adversarial AI research). Conversely, cybersecurity for AI has point solutions internal to organizations and broadly collaborative domains (e.g., collaboratively protecting from adversarial examples in shared data sets or shared models with multi-organizational transfer learning). However, the range and scope of how AI could be used for cybersecurity and how to improve the cybersecurity of AI remain relatively understudied yet critically important areas.

This minitrack seeks to focus on AI and Cybersecurity that works in broader domains, collaborative inter- organizational realms, shared collaborative domains, or with collaborative technologies. The threats being addressed with and/or to AI are intended to be sweeping in nature and of significant societal impact. Broadly, the topics and research areas include, but are not limited to:

• Novel applications of Artificial Intelligence, Machine Learning, and Deep Learning in Cybersecurity as it pertains to multi-user/multi-organizational collaborative domains and/or systems
• Adversarial AI/Machine Learning Applications in Cybersecurity that collaboratively span organizations or apply to collaborative systems (i.e., malware, phishing, or any applicable threat/identification domain)
• Protecting AI that is used collaboratively (i.e., shared data sets, shared models, shared applications) or spans collaborative domains from cybersecurity threats (i.e., adversarial examples, trojans, model inversion)
• Using AI to protect AI in any appropriate wide-reaching setting
• Novel Collaboration approaches to leveraging and protecting AI in the cybersecurity domain
• Sharing/disseminating tools, techniques, and applications of AI in Cybersecurity and Cybersecurity for AI that applies to the overarching theme of this minitrack

Minitrack Co-Chairs:

Mark Patton (Primary Contact)
University of Arizona
mpatton@email.arizona.edu

Hsinchun Chen
University of Arizona
hsinchun@email.arizona.edu

Sagar Samtani
Indiana University
ssamtani@iu.edu

Hongyi Zhu
University of Texas at San Antonio
hongyi.zhu@utsa.edu

Collaboration technologies are a fundamental cornerstone of modern work environments and hold many promising avenues for further development. The pace of change looks only to accelerate as the metaverse, and extended reality technology promise new possibilities for both in person and remote collaboration. However, successful collaboration is often difficult. Groups and teams often need to overcome both physical and metaphorical distance. Other challenges include groupthink, dominance, lack of efficiency, lack of focus, overwhelming information, differing motivations, and uncertainty.

The challenge for researchers and practitioners alike is to design sustainable processes and systems within and between organizations that allow people, groups, teams and machines to collaborate successfully. This challenge has many dimensions that cross technical, behavioral, social, emotional, economic, and political boundaries. This mini-track invites papers that address the theory, design, development, deployment, and evaluation of collaboration processes and systems within and between organizations, groups, teams, and machines.

Topics to be discussed in this minitrack include, but are not limited to:

• Evaluations and empirical studies involving extended reality technologies (augmented reality, virtual reality, mixed reality) and their use for collaboration
• Theoretical foundations and design methodologies for collaborative work practices and technologies
• Processes and tools for establishing and maintaining shared focus and shared mental models over time and when working in remote environments
• Processes, technologies, and theoretical breakthroughs to improve and speed up shared sense-making
• Facilitation methods, techniques, patterns, and procedures to improve (a)synchronous collaboration between co-located and distributed people, teams, or groups
• Assessment models and methods for team collaboration and performance
• Design, codification and reuse of work practices and pattern languages for group collaboration
• Design and building of automated virtual agents to participate in online collaborations (e.g., ChatOps)
• Exemplary use cases in various fields of application that refer to collaborative work practices
• Approaches for a new division of labor (including hand-offs) in reference to the task design and capabilities of AI and humans
• Approaches for increasing user acceptance of collaboration systems with AI components
• Usage of Low-code approaches for developing collaboration technologies
• Approaches for detecting and using social signals in different modalities (e.g., voice, language, vison) for the design of technologies to support collaboration
• Usage of generative AI collaboration technologies

Minitrack Co-Chairs:

Philipp Alexander Ebel (Primary Contact)
University of St. Gallen
philipp.ebel@unisg.ch

Eva Bittner
University of Hamburg
eva.bittner@uni-hamburg.de

Sarah Oeste-Reiß
University of Kassel
oeste-reiss@uni-kassel.de

Edona Elshan
University of St.Gallen
edona.elshan@unisg.ch

Diffusion (adoption, implementation, and utilization) of collaboration technologies has been investigated in many countries and regions around the globe. While many of the research initiatives have been undertaken in Western Europe and North America, they have been scarce in developing regions like Eastern Europe, Asia, Africa, and Latin America. The diffusion of collaboration technologies in these regions is on the rise as globalization drives inter and intra-country collaboration intensity within and across organizations. In many developing countries, synchronous and asynchronous computer support for team members (co-located or virtual) is being enhanced by handheld mobile devices in communities of practice and social media environments. Moreover, the use of digital technology enabled tools for collaboration and communication, has become more popular in the age of new technologies. Emerging trends like Metaverse, provide opportunities to further enhance IT enabled collaboration for development.

We would like to invite the authors to submit their research from theoretical, technological, social, psychological, behavioral, and design science perspectives. Research deploying different theoretical lenses could focus on process and system design, methods, modeling, and techniques in addressing various aspects of IT enabled collaboration for development.

The minitrack focuses on a wide range of topics including but not limited to:

• IT enabled collaboration in emerging context (e.g. Metaverse) in developing regions
• Collaboration technology diffusion case studies in education, business, government, and healthcare organizations in developing regions
• IT enabled cross-cultural and intra and/or inter-organizational collaboration in developing regions
• Global, virtual, distributed, blended, and face-to-face IT enabled collaboration for development at the team and organizational level
• Emerging issues in collaboration technology diffusion for development
• Deployment of mobile technologies for collaboration in developing regions
• Group decision making, negotiation, facilitation, and communication technologies for development
• Trust, privacy, security issues in IT enabled collaboration for development
• Social, behavioral, psychological, and technical factors influencing IT enabled collaboration for development
• Information systems, technologies, theories, processes, methods, or models that could be transferred and applied from developed regions to developing regions

Minitrack Co-Chairs:

Xusen Cheng (Primary Contact)
Renmin University of China
xusen.cheng@ruc.edu.cn

Xiangbin Yan
University of Science and Technology Beijing
xbyan@ustb.edu.cn

Deepinder Bajwa
Western Washington University
Deepinder.bajwa@wwu.edu

Scientists have started to adopt more open approaches to their research projects, which has seen open science (OS) growing in prominence in recent years, and is challenging this traditional scientific process used in information systems (IS) research. OS refers to making scientific knowledge freely available to the public, where open practices are becoming a requirement to be eligible for funding from most competitive funding schemes.

OS consists of different concepts such as open access (making published research articles freely available under an open access licence); open data (requiring researchers to make research data publicly available with their submitted papers); open methodology (sharing methodological details, such as data collection and analysis processes); open artefact(s) (making artefacts accessible online for free, with an open license to use,modify and reuse); and open peer review (where researchers and reviewers know each others identities). The impact of OS can be seen through the use of registered reports (RR), which facilitate a form of peer review that breaks studies into two stages: (i) study pre-registration – authors identify a relevant problem and detail their research design, which is submitted for (open) peer review and feedback, (ii) improved research execution – the study is completed with the improved design.

The objective of this minitrack is to give researchers the opportunity to present novel and innovative ways that they are conducting research using OS concepts outlined above. In doing so we aim to push the boundary of how IS research is conducted and communicated to the community. Indeed, OS should be more transparent, with greater potential to verify, replicate, and freely share the results. Such openness should also provide a platform for creating research that demonstrates proof-of-use as the community strives to become more relevant to practice. We therefore suggest that the community should strongly engage with OS, and in order to do so, we are looking for both conceptual and empirical papers that either further our understanding of OS in IS research, or studies that practice it.

Thus, we invite papers that focus on, but is not limited to, any of the following topics around open science in IS research:

• Philosophical considerations
• Ethical implications
• Methodological considerations
• Policies of open science
• Papers that practice one (or more) concepts of open science in their study
• Registered reports in IS research (can be either stage 1 or stage 2)
• Understanding of different platforms/tools/technologies for practicing open science
• Success/Failure cases that have arisen while practicing open science
• How we can improve or innovate open science practices
• Benefits of practicing open science
• Challenges and/or problems with practicing open science

Minitrack Co-Chairs:

Cathal Doyle (Primary Contact)
Victoria University of Wellington
cathal.doyle@vuw.ac.nz

Yi-Te Chiu
Victoria University of Wellington
yi-te.chiu@vuw.ac.nz

Tadhg Nagle
University College Cork
t.nagle@ucc.ie

Markus Luczak-Roesch
Victoria University of Wellington
markus.luczak-roesch@vuw.ac.nz

The proliferation of collaborative and connected systems, including digital tools and platforms that facilitate collaboration, information sharing, and communication among multiple users, has dramatically transformed how we interact, work, and live. Integrating Artificial Intelligence (AI) and other intelligent technologies have brought about new levels of automation and intelligence, resulting in the emergence of AI-enabled collaborative and connected systems. This integration has opened new possibilities for human-machine collaboration, yet it has also raised important ethical, legal, and societal concerns, which necessitate the responsible design, development, and use of these systems.

The primary objective of this minitrack is to provide a forum for scholars and practitioners to present their research and engage in discussions on the responsible design, development, implementation, management, and use of AI-enabled collaborative and connected systems. The minitrack aims to address the current knowledge gaps and challenges posed by integrating AI and other intelligent technologies in collaborative and connected systems, particularly in the context of human-machine collaboration.

The scope of this minitrack encompasses, but is not limited to, the following areas of inquiry:

• Assessment of the social, ethical, and societal impacts of AI-enabled collaborative and connected systems
• Privacy and security considerations in the design and development of AI-enabled collaborative and connected systems
• Human-centered design principles for the development of AI-enabled collaborative and connected systems
• The impact of AI on human-machine collaboration and its implications for work and employment
• Explainability and transparency in AI-enabled collaborative and connected systems
• The role of responsible governance in the use and regulation of AI-enabled collaborative and connected systems
• Responsible AI and machine learning in the development of AI-enabled collaborative and connected systems
• Addressing bias and fairness in AI-enabled collaborative and connected systems
• Cross-cultural considerations in the design and deployment of AI-enabled collaborative and connected systems
• The potential impact of AI-enabled collaborative and connected systems on the future of work and innovation
• The role of AI-enabled collaborative and connected systems in promoting social good and sustainability in a digital age
• Ethical and legal considerations in responsible innovation in AI-enabled collaborative and connected systems
• Best practices in responsible innovation in human-machine collaboration
• Approaches to measuring and evaluating the effectiveness of responsible innovation in AI-enabled collaborative and connected systems.

High quality and relevant papers from this minitrack will be selected for fast-tracked development towards Internet Research. Selected papers will need to expand in content and length in line with the requirements for standard research articles published in the journal. Although the minitrack co-chairs are committed to guiding the selected papers towards final publication, further reviews may be needed before a final publication decision can be made. Internet Research (IntR) is an international and refereed journal that is indexed and abstracted in major databases (e.g., SSCI, SCI, ABI/INFORM Global), with an impact factor 6.353 in 2021.

Minitrack Co-Chairs:

Bo Sophia Xiao (Primary Contact)
University of Hawaii at Manoa
boxiao@hawaii.edu

Chee-Wee Tan
Copenhagen Business School
cta.itm@cbs.dk

Kaveh Abhari
San Diego State University
kabhari@sdsu.edu

There are new advances in technology on an almost daily basis. For example, we are currently seeing an explosion in the use of chatbots. These developments in AI and robotics technology are having a huge impact on social robots, as these agents evolve from automated devices to highly autonomous partners. These social robots are ready to enter office and service environments such as hotels, retail and banking, where they can interact with customers. The potential of social robots has always been great, but groundbreaking technological developments and the combination with AI multiply this potential. As a result, their integration is rapidly changing the landscape of business and services.

In contrast to other forms of technology, social robots have what is known as automated social presence, which is why customers tend to treat them as social entities rather than as machines. In addition, social robots are increasingly developing social skills that enable them to recognize human emotions and to behave in human-like ways. The wide range of benefits that social robots can offer may be the reason why they have attracted so much and growing interest from the research community.

We welcome exciting contributions that extend our understanding of social human- robot interaction by addressing issues that sharpen and guide our view of the future. Business and service environments are of particular interest. With these findings, we aim not only to advance theoretical knowledge on the topic but also to help practitioners and organizations. The following questions may be addressed by researchers, but are not exhaustive:

Topics of interest include, but are not limited to:

• How will the role of social and service robots evolve in an era of breakthrough innovations in the field of AI?
• How can new theories and constructs help to understand the implications of introducing service robots in business and service environments (such as task sharing between social robots and humans)?
• What are innovative methods in social human-robot interaction research?
• How can social robot technology and AI-based applications be fruitfully integrated into human-robot interaction (for example, speech or emotion recognition)?
• How can service robots have an impact on businesses and service industries through improvements in efficiency and effectiveness?
• What are innovative approaches to social robot applications in business and service contexts?
• How can we address the dark side of social robots (such as blame attribution or security breaches)?
• How can we understand privacy and protective mechanisms in human-robot interaction?
• What are ethical and moral considerations involved in human-robot interaction?
• How can emotional engagement with social robots be promoted, for example through individualized design approaches?
• What are sustainable applications for service robots?

Minitrack Co-Chairs:

Ruth Stock-Homburg (Primary Contact)
Technical University Darmstadt
rsh@bwl.tu-darmstadt.de

Lea Heitlinger
Technical University Darmstadt
lea.heitlinger@bwl.tu-darmstadt.de