Embracing Levin's Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design through Action Research and STS Approaches

Embracing Levin's Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design through Action Research and STS Approaches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Embracing Levin's Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design through Action Research and STS Approaches Karyne Ang, Shankar Sankaran, Dikai Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4497385/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2024 Read the published version in Systemic Practice and Action Research → Version 1 posted 8 You are reading this latest preprint version Abstract This paper investigates the synergy between Levin's theories on technology transfer as a socio-technical learning and developmental process (TLD process), action research, and sociotechnical systems (STS) theories. Levin's extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views are introduced to complement and extend Levin's theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. To illustrate the synergies and potential challenges from Levin’s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin's theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary sociotechnical systems face unique challenges in the context of AI-driven human-robot teams where intelligent robots also contribute to the sociotechnical learning. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate of ‘collaborative intelligence’ between human-robot teams enabled by AI. Consequently, Levin's theories of technology transfer might not fully address the complex ethical dilemmas caused by AI-driven HRT systems. Therefore addressing these challenges requires ongoing dialogue and collaboration among researchers, practitioners, and policymakers with different disciplinary backgrounds to develop robust and reliable sociotechnical systems frameworks to navigate the complexities of robotics and AI in today's rapidly evolving technological landscape. Sociotechnical systems Technology transfer Human-robot teams Multidisciplinary teams design and development Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction This paper investigates the synergy between Morten Levin's theories [ 1 , 2 ] on technology transfer as a socio-technical learning and developmental process (TLD process), action research, and sociotechnical systems (STS) theories. Levin's extensive work in organizational change, especially within technology-dominated environments highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning. Levin’s insights into the socially constructed nature of technology highlight the importance of incorporating cultural knowledge and skills into the technological transfer process. Levin’s application of STS emphasizes the importance of considering human factors in technology design and recognizes that successful technology transfer involves more than the physical movement of artifacts -- it encompasses the transfer of embedded cultural skills. He postulates that the organizational developmental process is intrinsic to successful technology transfer, outlining its participative nature and continuous learning aspects. STS views by other researchers complement Levin's theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. Contemporary STS frameworks such as those presented by Davis et al [ 3 ] encompassing ‘Goals, People, Infrastructure, Technology, Culture, and Processes’, Pasmore et al’s [ 4 ] sociotechnical action research process incorporating various levels of design, balanced optimisation combining design of the ecosystem, technical system, organisation and social system and levels of outcomes, and Maguire’s [ 5 ] 21st century view of STS postulating developing technologies extend Levin's theories by explicitly addressing the interconnectedness of several elements. This paper contributes by integrating Levin's TLD process, participative action research, and STS principles into the study of HRT development. The synergistic approach taken is significant in determining the complexities inherent in multidisciplinary teams engaged in HRT development, with implications for broader technology development and transfer initiatives. The challenges, contrasts and opportunities identified in the study provide practical insights for fostering co-generative learning, facilitating organizational innovation, and supporting technology transfer in contemporary contexts. Upon applying Levin’s TLD model and STS perspectives to the findings from the case study, a novel contribution of this paper is the breakthrough in a new STS concept of ‘Collaborative intelligence’. Rapidly developing technologies like AI in sociotechnical systems thinking and human-robot teams opens up a fresh area for future research. This paper is structured as follows: Firstly, Levin’s theories of technology transfer and various sociotechnical systems thinking concepts are outlined, compared and contrasted to identify the research questions. Next, to address the research questions, the qualitative research methodology is described. This is followed by the findings and discussions. Finally, we conclude by summing up the insights and implications, and offer suggestions for further research. Identifying and comparing Levin’s theories with other Sociotechnical Systems thinkers Levin’s theories of Technology transfer Levin's extensive work in organizational change, especially within technology-dominated environments highlights the significance of technology transfer as a means for organizational development. The Technology Transfer as a Learning and Developmental process (TLD) model emphasizes the importance of incorporating cultural knowledge and skills into the technological transfer process [ 6 ]. Levin argues that successful technology transfer involves more than the physical movement of artifacts; it encompasses the transfer of embedded cultural skills [ 2 ]. Therefore, the organizational developmental process is intrinsic to successful technology transfer, outlining the participative nature, co-generated learning and continuous learning aspects [ 2 , 6 ]. Levin’s model for technology transfer as a socio-technical learning and developmental process (also known as the TLD process in Levin, 1993 p. 513) suggests that technology transfer encompasses an innovation process that contributes to the successful use of new machines and equipment. Sociotechnical Systems (STS) theories and thinking Sociotechnical systems theory (STS) explores how the introduction of new technology in organizations impacts people, including how multi-skilled people work together as self-organized units to optimize social and technical systems [ 7 ]. STS advocates that the design and performance of any organizational system can only be understood and improved if both 'social' and 'technical' aspects are integrated and considered as interdependent parts of a complex system incorporating people, technology, infrastructure, culture, processes or procedures, goals, and metrics or measures [ 7 ]. Optimal performance in such systems requires attendance to both the social and technical aspects of work organization [ 7 ]. Since its foray in the sociotechnical world, STS thinking has evolved over the last 50 years. The next paragraphs introduce the thinking offered by several other key STS researchers like Davis et al [ 3 , 8 ], Pasmore et al [ 4 ]and Maguire [ 5 ]. Davis, Jayewardene & Clegg's [ 3 ] sociotechnical framework, known as the Sociotechnical Hexagon model, identifies six core components of socio-technical systems: goals, people, infrastructure, technology, culture, and processes. This model emphasizes the importance of considering the interplay between different components when analyzing and understanding complex systems. Meanwhile Pasmore et al [ 4 ] offer a forward-looking view of STS for organizations of the future. Their views for organizational design in the face of rapid technological advancement suggest that while technological progress is exponential, organizational design has lagged behind, creating a widening gap between technical solutions and the ability to effectively utilize them [ 4 ]. They present a participative STARlab (sociotechnical action research) model, that outlines three levels of design work: strategic, governance and ecosystem design. Subsequently, Maguire [ 5 ] discusses the application of STS principles to 21st-century technologies, including information integration, pervasive systems, artificial intelligence (AI) and cloud computing [ 6 ]. He suggests that AI, particularly intelligent agents, could present themselves to users as human-like characters or avatars, cueing responses through their use of language, assumption of social roles, or physical presence [ 5 ]. Maguire also highlights the importance of considering human factors in technology design and the need for users to learn the skills to make the best use of AI developments (e.g., as intelligent agents, human-robot interactions) while appreciating their benefits and limitations. AI’s interactional properties with its environment also enables its capability to learn and change its behaviour based on the cues from the environment [ 9 ]. However, as far as we know, these postulations have not yet been well observed and researched in practical scenarios from an STS and TLD perspective. Interconnectedness of Theories and Frameworks The works of Levin, Davis et al., Pasmore et al., and Maguire demonstrate the interconnectedness of their theories and frameworks. All emphasize the importance of considering both social and technical aspects when designing and implementing new technologies in organizations. Levin's TLD model [ 1 , 2 ] aligns with contemporary STS thinking, viewing technology as shaped by social, cultural, and political factors. The Sociotechnical Hexagon model by Davis et al. [ 3 ] complements Levin's theories by providing a systemic lens to understand the broader socio-technical context of the organisation in which technology transfer occurs. Their views imply that those involved with innovation and technology transfer require access to cultural knowledge encapsulated in technological artefacts. The acquisition of skills and understanding comes through a learning process as technology like machines and tools have cultural meaning embedded in them. This is because the individuals involved in their design and production operate under specific social and cultural conditions likely to encompass individual, professional and organisation culture. To successfully use these artifacts, users must have access to this inter-cultural knowledge. This view emphasizes the importance of communication and understanding between technology suppliers and users, and is achieved through mutual learning and dialogue. Levin highlights that co-generative learning occurs with organizational innovation and development through a participative process using action research and critical systems thinking. This would include learning across boundaries of work and disciplines, resulting in participative transformation and learning within teams and across organisations. Subsequently, Pasmore et al.'s STARlab model [ 4 ] extends Levin's theories by explicitly addressing the engagement and interconnectedness of different organizational levels and the need for continuous adaptation in the face of rapid technological change. Both Levin and Pasmore et al highlight multi-stakeholder participation and collaboration through action research for innovation with successful technology acceptance and transfer. Maguire's discussion of STS principles applied to developing technologies like AI further reinforces the importance of considering human factors and the social implications of emerging technologies [ 5 ]. In the context of advancing technologies like robotics and AI, STS theories suggest that the development and deployment of robots and AI should have an integrated focus on technical aspects, social implications and values embedded in the technology. These concepts are critical in addressing construction industry challenges like productivity stagnation and labour shortages. Human-robot teams offer a solution to boost productivity while ensuring worker well-being and safety. Therefore, STS integration into the HRT design and development aims not to replace humans but to enhance their capabilities through collaborative intelligence [ 10 ]. The successful development and integration of HRTs requires a socio-technical approach that addresses the interplay between technology, people, culture, and processes, as well as continuous learning and adaptation to optimize performance and human well-being. In the next section, the research questions and qualitative research methodology are outlined. A single unique case study was applied to trace the inception, design and development of a prototype human-robot team (HRT). Research questions This study investigates the interplay between Morten Levin's theories on technology transfer as a socio-technical learning and developmental process (TLD process), action research, and sociotechnical systems (STS) theories. Three research questions have been formulated. 1. How does having an STS mindset change the way a multi-disciplinary team works on researching, developing and testing HRTs? 2. What are the challenges in a multidisciplinary team in HRT development? 3. How does STS and Levin's theories serve to enable innovations and Technology Transfer to occur in a HRT development process? Research Methodology To illustrate the synergies and potential challenges from Levin’s theories of technology transfer coupled with STS concepts, this study employs a qualitative methodology, using a case study focusing on the interactions for the design and development of human-robot teams (HRTs) for construction tasks. The qualitative approach allows for in-depth exploration of the complex social phenomena within real-life contexts [ 11 ], making it suitable for investigating the socio-technical aspects of HRT development and deployment. Case Study and Participants The case study focuses on a collaborative endeavour involving a construction engineering organisation and a small multidisciplinary team comprising of robotics personnel working together to develop and use collaborative robots with workers in the construction industry [ 12 ]. The multidisciplinary robotics team at UNITECH developed the prototype Quendabot (Fig. 1 ), an intelligent robot developed for installing screws for mass timber construction work. This case study was selected because it enables the firsthand study of socio-technical phenomena in human-robot collaborative technologies within the context of the construction industry. Purposive sampling was employed to select participants who could provide rich insights into the HRT development process [ 13 ]. 60-minute interviews were conducted with industry experts, leaders, project engineers and timber construction consultants and the HRT development team members to gather their views on design and development, as well as their experiences as humans working alongside collaborative robots. Follow-up on the progress of the case and ongoing developments of the HRT team was possible, aligning with the STS concept of iterative and continuous systems. Case synopsis Researchers at UNITECH, in collaboration with MODA (building site) and AURORA (project engineers pseudonymed as PRAKA and PEDKA and timber consultant PEKA), developed an autonomous robot, Quendabot, for timber building construction. A simple user interface allows workers to monitor the operation of the Quenda-bot and view real-time data. At the point of prototype deployment on site, a human operator was needed to feed the screws to the robot as the self-feeding mechanism was not completely developed yet due to time constraints. This innovation addresses the challenges of repetitive and strenuous tasks involved in Mass Engineered Timber construction, enhancing both safety and efficiency. The UNITECH HRT development team, led by a director (pseudonymed as DISHKA), comprises hardware (ALKA) and software engineers (GIKA), postdoctoral researchers and other collaborating academics (KASHKA) focusing on technical and sociotechnical aspects. Team meetings are held regularly to discuss developments, achievements, and next steps, with DISHKA typically leading discussions. Quendabot is regularly showcased to industry visitors at the UNITECH Robotics Engineering Lab where the HRT development team demonstrate the human-robot teams completing construction tasks in a lab environment. Data Collection The case study was selected as it as it was possible to interview personnel involved in the development and use of Quendabot at site. The data collection in the case study approach includes semi-structured interviews, videos, photographs, and document analysis. Semi-structured interviews provide flexibility for participants to express their views while ensuring that key topics are covered [ 14 ]. The flexible data collection design enabled the researchers to follow up on some of the former interviews to look into the progress of the case and ensure that key aspects from original data collected are not been missed. As the data is analysed, emerging results are used to shape the next set of questions. Videos and photographs served as visual data sources, capturing the interactions and dynamics within the HRT development process. Document analysis involved reviewing relevant project documents, such as design specifications, progress reports, and meeting minutes, to gain further insights into the socio-technical considerations and decision-making processes. Data Analysis The data analysis followed an abductive approach, combining observation-based and rule-based thinking to provide a nuanced approach to iterative analysis and interpretations [ 10 ]. The qualitative analysis software NVivo 14 was used to manage and organize the interview data transcripts. This facilitates the coding process and the exploration of emerging themes. The coding and analysis process (Fig. 2 ) involved a combination of deductive and inductive coding. Deductive codes are derived from the literature review and the research questions, while inductive codes emerged from the data itself (example in Fig. 3 ). The analysis approach emphasizes the importance of subjective yet professional analysis based on interpretation and the socio-technical systems perspectives [ 15 , 16 ]. Data by way of verbatims as the participants’ spoken words are included in the findings as evidence, illustration, explanation to deepen understanding and to give participants a voice [ 17 ]. Video and photo analyses were conducted as a secondary data source, providing additional reference for context and insights into the HRT development process. The analysis recognizes the dialectical relationship between ideas and their impact on each other, as well as the reflexivity in the research process, substantiating the researchers' role and presence in the data analysis [ 16 ]. The multidisciplinary co-authors of this paper bring a shared purpose but with asymmetrical and divergent insights, enriching the analysis and interpretation of the data. Findings and Discussion Aligned with Levin’s TLD model, the case study demonstrates the innovation approach as research-driven, and involves a lead agent (PRAKA at AURORA) handling the mediation with multiple external stakeholders and strategic consultants (PEKA and PEDKA as the Case Timber Construction Consultants), while the technology supplier is UNITECH, a university robotics research team comprising DISHKA (Director), GIKA (Software Engineer), ALKA (Hardware Engineer) and KASHKA (STS Director). We discuss further aspects of STS elements with the following findings. Impact of an STS Mindset on Multi-Disciplinary Teams The adoption of an STS mindset has a significant impact on the way multi-disciplinary teams work on researching, developing, and testing Human-Robot Teams (HRTs). Leadership and Mindset The findings reveal that visionary leadership, and an open-minded, forward-thinking mindset are essential for driving innovation in HRTs for construction, for instance, “ We need some drivers or future-thinking leaders who have the vision for the future. Without them, it's very difficult to make it happen. " (DISHKA, HRT Director). This is further supported by the statement, "I think just being open minded and having that, that perseverance, towards driving excellence and driving innovation ” (KASHA, STS Director). Leaders with a clear vision can inspire and guide multi-disciplinary teams to push boundaries and embrace new technologies. Bridging Disciplinary Differences An STS mindset helps bridge the differences between various disciplines involved in HRT development, such as engineering, AI, IT, Construction and Project Management. KASHA indicated that engineers tend to have an " engineer's mentality " and focus on technical aspects, while an STS perspective considers broader social implications. Recognizing and accommodating different communication styles and priorities can facilitate better collaboration and understanding among team members, as suggested by KASHA, “Engineers will like draw pictures. The managers will not draw pictures, but they may have a lot of dialogue, so, depending on who is at the party." . This shows the benefits of effective management of team members from across functions is required in new product development (NPD) projects [ 18 ]. Balancing Research and Industry/Social Impact The findings suggest that an STS mindset encourages multi-disciplinary teams to balance academic research excellence with practical industry impact. While publishing papers is important, focusing on developing solutions that benefit the construction industry and end-users is equally crucial, as suggested, “Although we do not publish as many papers as we expect, but I think research needs to generate benefits to the industry and people working in the industry." (DISHKA). This mindset also emphasizes the benefits of considering human factors and social impact, such as wellbeing, ergonomics and user experience, in HRT design. This can be seen through the comments, “I think the robotics will only bring less fatigue, less stress on the human body and the guys working in allow them to do slightly modified tasks." (PEKA) and DISHKA, “We're not taking away jobs, we're, letting the robot do unsafe tasks” . Collaborative Engagement with Stakeholders An STS mindset promotes collaborative engagement with stakeholders, particularly industry partners, from the early stages of HRT development. Focusing on engagement and developing solutions that benefit the construction industry and end-users is crucial as commented by KASHA “That means that we are engaging with the people who are going to apply the solution, at the beginning of the process so that they continuously give us inputs. And we can work to develop something that they will use better." (KASHA). While participants agreed that this was an important aspect, in practice, this principle was not always well executed. PEDKA expressed concerns about the limited involvement of the physical trade in the Quendabot project, which he felt reduced opportunities for valuable feedback and learning opportunities. "It was very closely managed by the people who built it, programmed it and were seeking to get the innovative results out of it. It could have been a higher level of involvement between the guys that do it from a physical perspective." (PEDKA) Through actively seeking to understand the needs and problems of construction companies, multi-disciplinary teams are more likely to develop targeted solutions that address real-world challenges. Early and continuous engagement fosters trust, buy-in, and a sense of shared ownership, leading to more successful HRT implementations, as posited by Levins’ TLD model [ 2 ]. Levin’s emphasis on the social and cultural aspects of technology transfer is demonstrated, with the implication that effective communication and understanding across different organizational and cultural contexts are important. Co-generative learning and engagement The findings reveal that getting industry partners involved from day one to understand their problems, gather requirements and continuously seeking their input is key. This means early engagement and open conversations and with stakeholders like construction companies are needed to get their buy-in. Multiple comments were raised about these aspects from both the research and engineering perspectives, for instance, "It's not about us telling them what we can do. It's more about they tell us what they want" (DISHKA), "Early discussions, early engagement with anything, absolutely anything. Early engagement, as always is the key piece of the puzzle, bringing them into the table early and saying, here's the plan." (PRAKA, Project Lead Engineer at Aurora). This was also demonstrated through the comments by ALKA (HRT Hardware Engineer), “So it's very good if their team is working with researchers, not just the very big guy (seniors) that come to our labs. These statements strongly suggest that researchers and industry partners need to work closely together, be open, and not hide problems from each other. Additionally, as robots become more learning-oriented, teaching humans how to effectively interact with them will become increasingly important, “ it's important for humans to know how to teach robots. They have to understand what they're learning and what the robots are actually learning. " (GIKA). This leads to the next theme where robots are viewed as collaborative partners. An STS mindset that considers robots as collaborative partners rather than mere tools can foster a more human-centric approach to HRT development “I think the mindset of it is just treating a robot like another human, like if they were a new person on the work side” as commented by GIKA (HRT Software Engineer), leading to better integration and acceptance of robots on construction sites. Challenges faced by multidisciplinary teams The themes reveal that Levin's theories provide a valuable foundation for understanding innovation and technology transfer through having an STS mindset in multidisciplinary teams, contemporary sociotechnical systems face unique challenges in the same context, particularly resistance from stakeholders and end-users, conflicting organisational and stakeholder priorities and expectations, and emergence of AI-driven technologies in human-robot teams. Resistance from stakeholders One of the primary challenges is resistance from stakeholders and end-users, including construction companies and unions. This resistance typically stems from concerns about job displacement and changes to established work practices. This quote by PEKA illustrates the resistance and perceptions observed in the field, "The other guys who just want to use their drill every day of the week, because that's what they know." (PEDKA). “Some [xxxxx] people have a very narrow perception of it. It's taking jobs and all those sorts of [xxxxx] things. The reality is, I don't think the robots ever done anybody out of work, all it's done is created a diverse working environment and people who learn different skills. " (PEKA). Overcoming this resistance requires careful communication, education, and collaboration with these stakeholders to address their concerns and demonstrate the benefits of human-robot collaboration. Managing scope and expectations Another challenge is managing the scope and expectations of industry partners in the HRT design. GIKA commented that, “ We engineered this robot to go beyond these kinds of limitations that the robot has. If it was a proper project run by a partner, they would have better scoping ." (GIKA). Better scoping and clear communication of project goals and limitations can help manage expectations and ensure a more successful collaboration between the research team and industry partners. Limited resources Limited resources, both in terms of funding and time, can also hinder the development and scalability of HRT projects. These constraints can lead to compromises in robot design and functionality, as well as a lack of continued development and improvement. These comments illustrate the issue, "In terms of why the robot was not designed in the ideal way, it is mainly because we have the limitation of the resources." (DISHKA). Securing adequate funding and dedicating resources to further development are essential for realizing the full potential of HRTs in construction. Risky integration of HRT into existing construction processes and workflows STS principles hold that ideally, new technologies ought to be integrated with the social systems for a unified and wholistic approach. However, the case study indicates that integrating robots into existing construction processes and workflows can be challenging. PEDKA raises concerns about the ability of robots to handle dynamic and unstructured construction environments, where problem-solving and adaptability are crucial, for example, "I think that's where the robot is limited is when it comes across a problem, something's in its way, and it just stop and wait for somebody to solve its problem in the early days." (PEDKA). “There’s the risk of if the robot breaks down whilst it's working, then you're delaying work on site. So delay becomes a critical aspect of what we do for the robot to not have any downtime." (PRAKA, Lead Project Engineer). Robotic system downtime or breakdowns can cause delays and disrupt construction schedules, which are often tight and interdependent. For HRTs, ensuring robot reliability, maintainability, and seamless integration into construction workflows is crucial to mitigate these risks and minimize disruptions. Different views of robots: The Robot as a Team-Mate in Human-Robot Teams (HRTs) The findings reveal contrasting perspectives between industry participants and HRT developers regarding the role of robots in HRTs as illustrated in Fig. 4 . Industry participants tend to view robots as an object, such as a tool, equipment, or machine, or as a fully autonomous technology that will naturally progress and evolve as commented by PEKA "It’s just a natural progression in the arsenal of tools that we have at our disposal…. As we get more skilled in being able to program to do more complex tasks, that will be the way to evolve." (PEKA). In contrast, the HRT development team perceives robots as teammates, capable of decision-making, optimization, adaptability, and intelligence. This difference in perspective can be attributed to the designers' insight, intention, and proximity to the advancements in technology, including AI capabilities, since the inception of the Quendabot project. These differences in how robots are perceived were highlighted where different stakeholders attributed different descriptions to the robot, including “alien,” “machine,” “worker,” and “colleague” based on their familiarity and experience with the robot ([ 19 ] citing Ljungblad et al 2012). The challenges identified demonstrate the importance of effective stakeholder engagement, clear communication, adequate resource allocation, and thorough planning for the successful development and integration of human-robot teams in the construction industry. Addressing these requires a multi-faceted approach that considers the technical, social, and organizational aspects of HRT projects. Despite the different contexts, these themes are typically in line with TLD and STS thinking. However, contrasting views can spur innovation and new ways of sociotechnical thinking. This was found in the theme of ‘robot as team-mate’ and will be discussed in the next section. Emergence of a new sociotechnical dynamics From the findings, we observed that the integration of AI in HRTs introduces a new type of sociotechnical working relationship and dynamics between humans and robots. In the AI field, as robots become more proactive and intelligent agents, they challenge the traditional notions of human-robot interaction [ 9 ]. Robots as social entities or “co-worker” affects people’s perceptions regarding their social relationships [ 19 ]. This shift leads to changes in human behaviours and attitudes towards robots, communication modes and cues, team agency and autonomy, self-determination, and alternative work processes and routines [ 19 ]. The rapid advancements in AI challenge and extend Levin's theories of technology transfer and STS thinking. With the increasing sophistication of AI systems, the co-generation of knowledge and collaborative learning between humans and humans have moved into the domain of humans and robots through transfer of skills and machine learning. Furthermore, robots powered by AI have the potential to be proactive initiators rather than mere followers or responders, leading to a more dynamic and interactive technology transfer process. Collaborative Intelligence – an addition to the STS principles and Levin’s theories of co-generative learning for innovation and TLD The integration of AI in HRTs obliges an update to the traditional STS principles. The human-robot team can be viewed as an autonomous and adaptive subsystem, where team decisions are made jointly, and problems are solved within the unit based on each agent's capabilities to optimize performance and outcomes. This introduces a new principle of "collaborative intelligence" in STS theory, emphasizing the symbiotic relationship between human and artificial intelligence in sociotechnical systems. As Wilson & Daugherty [ 10 ] (p.123) observe ‘Organizations that use machines merely to displace workers through automation will miss the full potential of AI. Such a strategy is misguided from the get-go. Tomorrow’s leaders will instead be those that embrace collaborative intelligence, transforming their operations, their markets, their industries, and—no less important—their workforces’. Challenges and Opportunities for AI-driven HRTs Ethical Considerations The incorporation of HRTs that are enabled by AI as a system raises complex ethical dilemmas that need to be addressed. As AI systems become more autonomous and exhibit quasi-autonomous behaviours, issues of responsibility, accountability, transparency, and fairness become critical. Responsible AI includes considering AI systems as artefacts where humans set the purpose with societal, moral, and legal values [23]. Stakeholder Engagement and Organizational Dynamics The rapidly evolving nature of technologies poses challenges for stakeholder participation and inter-organizational dynamics in HRT development and implementation. Engaging stakeholders and managing organizational change in the context of AI-driven HRTs require adaptive approaches that can keep pace with the dynamic technological landscape. Conclusions This study delved into Levin's theories on technology transfer as a socio-technical learning and developmental process (TLD process), alongside action research and sociotechnical systems (STS) theories, within Human-Robot Team (HRT) development. The unique case study demonstrated how multidisciplinary teams navigate HRT design and development and provided insights into fostering co-generative learning, driving organizational innovation, and facilitating technology transfer in contemporary contexts. The research uncovered divergent viewpoints between industry participants and HRT developers regarding the role of intelligent robots in HRTs. While industry players often see robots as tools, visionary leaders and HRT designers regard them as decision-making teammates capable of optimization, adaptability, and intelligence. These differing perceptions of emerging technologies suggest the need for a paradigm shift, urging those working with robots to embrace them as collaborative partners rather than mere tools. The infusion of AI into HRTs introduces a new dimension of sociotechnical dynamics, challenging traditional human-robot interaction norms. As robots evolve into potentially proactive agents, they are likely to reshape human behaviours, communication modes, and work processes. This paper proposes integrating " collaborative intelligence " into STS theory to address these emerging dynamics, emphasizing the symbiotic relationship between humans, AI and robots. Levin's emphasis on incorporating cultural knowledge into technological transfer resonates with STS's holistic approach to HRT development. Collaboration, flexibility, and adaptability are crucial in technology transfer programs to navigate changing conditions and unpredictable technological landscapes. Additionally, integrating new technology into strategic plans, leveraging local competencies, and fostering continuous learning optimize HRT performance. Challenges from the case study such as stakeholder resistance, resource constraints, and integration issues reveal the importance of effective stakeholder engagement, clear communication, and thorough planning for successful HRT development and technology transfer. This study has broader implications for technology development and transfer initiatives, emphasizing skill leveraging, adaptability, and collaboration. However, limitations like single-case focus and evolving AI technologies may affect generalizability over time. Future research could explore AI-driven HRTs in diverse industries to grasp sociotechnical dynamics comprehensively. Developing STS principles and frameworks for HRTs enabled by AI that consider responsible and ethical deployment, skill leveraging, adaptability, and collaboration and organizational dynamics, are crucial areas of investigation. Lastly, while the original STS concepts by Emery and Trist [ 7 ] implied self-organizing teams, the question of whether intelligent robots could evolve into a new form of self-organizing teams with humans and robots jointly deciding how to allocate task dynamically during a the execution of a complex tasks in construction, has yet to be explored in sociotechnical systems research. Declarations We confirm that we understand Journal of Systemic Practice and Action Research is a transformative journal. When research is accepted for publication, there is a choice to publish using either immediate gold open access or the traditional publishing route. We declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper. The results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration (from all Contributing Authors) by another publisher. We have read the Nature Portfolio journal policies on author responsibilities and submit this manuscript in accordance with those policies. All of the material is owned by the authors and/or no permissions are required. The raw data that support the findings of this study are not openly available due to ethical reasons of participant confidentiality, anonymity and privacy. Summaries of the data are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at University of Technology Sydney. Ethics Approval This study has been approved by the University of Technology, Sydney Human Research Ethics Committee (ETH22-7525) with informed consent from participants. All information is kept confidential, anonymous and private. Funding No funding was received for conducting this study. Author Contribution K.A. wrote the main manuscript. S.S. and D.L. reviewed and edited the manuscript, suggested some ideas.All authors reviewed the manuscript. Data Availability The raw data that support the findings of this study are not openly available due to ethical reasons of participant confidentiality, anonymity and privacy. Summaries of the data are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at University of Technology Sydney. References Levin M (1993) Technology transfer as a learning and developmental process: an analysis of Norwegian programmes on technology transfer. Technovation 13(8):497–518 Levin M (1997) Technology transfer is organisational development: an investigation into the relationship between technology transfer and organisational change. Int J Technol Manage 14(2–4):297–308 Davis MC et al (2014) Advancing socio-technical systems thinking: A call for bravery. Appl Ergon 45(2):171–180 Pasmore W et al (2019) Reflections: sociotechnical systems design and organization change. J Change Manage 19(2):67–85 Maguire M (2014) Socio-technical systems and interaction design–21st century relevance. Appl Ergon 45(2):162–170 Levin M (1993) Creating networks for rural economic development in Norway. Hum Relat 46(2):193–218 Emery FE, Trist EL (1972) Towards a social ecology: contextual appreciation of the future in the present. Plenum, London Davis MC (2019) Socio-technical systems thinking and the design of contemporary workspace , in Organizational behaviour and the physical environment . Routledge, pp 128–146 Glikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Acad Manag Ann 14(2):627–660 Wilson HJ, Daugherty PR (2018) Collaborative intelligence: Humans and AI are joining forces. Harvard Business Rev 96(4):114–123 Yin RK (2014) Case Study Research Design and Methods, 5 edn. Sage, USA Le DDK et al (2023) The QUENDA-BOT: Autonomous Robot for Screw-Fixing Installation in Timber Building Construction . in. IEEE 19th International Conference on Automation Science and Engineering (CASE) . 2023 Patton MQ (2014) Qualitative research & evaluation methods: Integrating theory and practice. Sage Baskarada S (2014) Qualitative case study guidelines. Baškarada, S. Qualitative case studies guidelines. The Qualitative Report, 2014. 19(40): pp. 1–25 Srivastava P, Hopwood N (2009) A practical iterative framework for qualitative data analysis. Int J qualitative methods 8(1):76–84 Alvesson M, Sköldberg K (2017) Reflexive methodology: New vistas for qualitative research. sage Corden A, Sainsbury R (2006) Using verbatim quotations in reporting qualitative social research: researchers' views. University of York York Edmondson AC, Nembhard IM (2009) Product development and learning in project teams: The challenges are the benefits. J Prod Innov Manage 26(2):123–138 Sauppé A, Mutlu B (2015) The social impact of a robot co-worker in industrial settings . in Proceedings of the 33rd annual ACM conference on human factors in computing systems Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4497385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313426782,"identity":"d1503ba9-39ed-43a0-a8ff-8987916e6a8f","order_by":0,"name":"Karyne Ang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACCQST+QCEPkC8FrYEiGoStPAYEKdFsv3sM+mCCgZ5c/4136Q//GGQ47uRwPiZB48WaZ50M+kZZxgMd854u03iYBuDseSNBGZpfFrkGNLYpHnbGBg33DgL1NLAkLjhRgIDfi38z4Ba/jHYb7hx5pnEgT8M9UAtzL/xOkwCZAvI8PM9bBIH2BgSDG4ksOG1RXLGM2ZrnmMSyRtusBlbnG2TMJx55mGb5Rw8WiTOpzHe5qmxsd1w/vDDGxV/bOT5jicfvvEGjxYgYJEAR49EAtgIIGZswK8BmFA+gCn+A4QUjoJRMApGwUgFAIFhS1M+8LeeAAAAAElFTkSuQmCC","orcid":"","institution":"University of Technology Sydney","correspondingAuthor":true,"prefix":"","firstName":"Karyne","middleName":"","lastName":"Ang","suffix":""},{"id":313426783,"identity":"bb2b27a8-ea02-48ad-ba40-9737dbcd3ac1","order_by":1,"name":"Shankar Sankaran","email":"","orcid":"","institution":"University of Technology Sydney","correspondingAuthor":false,"prefix":"","firstName":"Shankar","middleName":"","lastName":"Sankaran","suffix":""},{"id":313426784,"identity":"d5dac760-98ed-40da-b89c-8fb381133ec4","order_by":2,"name":"Dikai Liu","email":"","orcid":"","institution":"University of Technology Sydney","correspondingAuthor":false,"prefix":"","firstName":"Dikai","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-29 13:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4497385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4497385/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11213-024-09705-y","type":"published","date":"2024-10-24T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58744396,"identity":"2d50c47e-e629-4234-b563-7debfdcab724","added_by":"auto","created_at":"2024-06-20 14:45:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208196,"visible":true,"origin":"","legend":"\u003cp\u003eThe Human-robot team comprising the construction worker and Quendabot at (a) simulated timber construction site; (b) actual construction site\u003c/p\u003e","description":"","filename":"Figure1Humanrobotteam.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4497385/v1/9db41d212b63b177f8b5d229.jpg"},{"id":58744392,"identity":"9aff393d-d582-4926-96ab-f96571a3c977","added_by":"auto","created_at":"2024-06-20 14:45:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10963,"visible":true,"origin":"","legend":"\u003cp\u003eData coding and analysis process\u003c/p\u003e","description":"","filename":"Figure2Datacodingandanalysisprocess.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4497385/v1/4d9538a391c3486f870458bd.jpg"},{"id":58744393,"identity":"a43bd6f0-383a-4983-8f27-e24f37475e47","added_by":"auto","created_at":"2024-06-20 14:45:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31124,"visible":true,"origin":"","legend":"\u003cp\u003eAn example of the deductive and inductive themes\u003c/p\u003e","description":"","filename":"Figure3Exampleofdeductiveandinductivethemes.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4497385/v1/acda7d3945c50400649fb7ad.jpg"},{"id":58744394,"identity":"338cb327-7718-4e4c-80f6-f83b41cfeea5","added_by":"auto","created_at":"2024-06-20 14:45:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18847,"visible":true,"origin":"","legend":"\u003cp\u003eContrasting views between industry and human-robot team developers regarding the role of intelligent robots\u003c/p\u003e","description":"","filename":"Figure4ContrastingviewsindustryandHRTdevelopers.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4497385/v1/4ce2e57195e17577a31c80f8.jpg"},{"id":67684000,"identity":"5c8eb247-5704-4a96-9710-4bc8413b339b","added_by":"auto","created_at":"2024-10-28 16:22:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":624278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4497385/v1/4f883876-ce6f-4210-9dff-cbd3c0a70340.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Embracing Levin's Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design through Action Research and STS Approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThis paper investigates the synergy between Morten Levin's theories [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] on technology transfer as a socio-technical learning and developmental process (TLD process), action research, and sociotechnical systems (STS) theories. Levin's extensive work in organizational change, especially within technology-dominated environments highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning.\u003c/p\u003e \u003cp\u003eLevin’s insights into the socially constructed nature of technology highlight the importance of incorporating cultural knowledge and skills into the technological transfer process. Levin’s application of STS emphasizes the importance of considering human factors in technology design and recognizes that successful technology transfer involves more than the physical movement of artifacts -- it encompasses the transfer of embedded cultural skills. He postulates that the organizational developmental process is intrinsic to successful technology transfer, outlining its participative nature and continuous learning aspects.\u003c/p\u003e \u003cp\u003eSTS views by other researchers complement Levin's theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. Contemporary STS frameworks such as those presented by Davis et al [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] encompassing ‘Goals, People, Infrastructure, Technology, Culture, and Processes’, Pasmore et al’s [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] sociotechnical action research process incorporating various levels of design, balanced optimisation combining design of the ecosystem, technical system, organisation and social system and levels of outcomes, and Maguire’s [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] 21st century view of STS postulating developing technologies extend Levin's theories by explicitly addressing the interconnectedness of several elements.\u003c/p\u003e \u003cp\u003eThis paper contributes by integrating Levin's TLD process, participative action research, and STS principles into the study of HRT development. The synergistic approach taken is significant in determining the complexities inherent in multidisciplinary teams engaged in HRT development, with implications for broader technology development and transfer initiatives. The challenges, contrasts and opportunities identified in the study provide practical insights for fostering co-generative learning, facilitating organizational innovation, and supporting technology transfer in contemporary contexts.\u003c/p\u003e \u003cp\u003eUpon applying Levin’s TLD model and STS perspectives to the findings from the case study, a novel contribution of this paper is the breakthrough in a new STS concept of ‘Collaborative intelligence’. Rapidly developing technologies like AI in sociotechnical systems thinking and human-robot teams opens up a fresh area for future research.\u003c/p\u003e \u003cp\u003eThis paper is structured as follows: Firstly, Levin’s theories of technology transfer and various sociotechnical systems thinking concepts are outlined, compared and contrasted to identify the research questions. Next, to address the research questions, the qualitative research methodology is described. This is followed by the findings and discussions. Finally, we conclude by summing up the insights and implications, and offer suggestions for further research.\u003c/p\u003e \u003cp\u003eIdentifying and comparing Levin’s theories with other Sociotechnical Systems thinkers\u003c/p\u003e \u003cp\u003eLevin’s theories of Technology transfer\u003c/p\u003e \u003cp\u003eLevin's extensive work in organizational change, especially within technology-dominated environments highlights the significance of technology transfer as a means for organizational development. The Technology Transfer as a Learning and Developmental process (TLD) model emphasizes the importance of incorporating cultural knowledge and skills into the technological transfer process [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Levin argues that successful technology transfer involves more than the physical movement of artifacts; it encompasses the transfer of embedded cultural skills [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, the organizational developmental process is intrinsic to successful technology transfer, outlining the participative nature, co-generated learning and continuous learning aspects [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Levin’s model for technology transfer as a socio-technical learning and developmental process (also known as the TLD process in Levin, 1993 p. 513) suggests that technology transfer encompasses an innovation process that contributes to the successful use of new machines and equipment.\u003c/p\u003e \u003cp\u003eSociotechnical Systems (STS) theories and thinking\u003c/p\u003e \u003cp\u003eSociotechnical systems theory (STS) explores how the introduction of new technology in organizations impacts people, including how multi-skilled people work together as self-organized units to optimize social and technical systems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. STS advocates that the design and performance of any organizational system can only be understood and improved if both 'social' and 'technical' aspects are integrated and considered as interdependent parts of a complex system incorporating people, technology, infrastructure, culture, processes or procedures, goals, and metrics or measures [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Optimal performance in such systems requires attendance to both the social and technical aspects of work organization [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Since its foray in the sociotechnical world, STS thinking has evolved over the last 50 years. The next paragraphs introduce the thinking offered by several other key STS researchers like Davis et al [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Pasmore et al [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]and Maguire [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDavis, Jayewardene \u0026amp; Clegg's [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] sociotechnical framework, known as the Sociotechnical Hexagon model, identifies six core components of socio-technical systems: goals, people, infrastructure, technology, culture, and processes. This model emphasizes the importance of considering the interplay between different components when analyzing and understanding complex systems. Meanwhile Pasmore et al [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] offer a forward-looking view of STS for organizations of the future. Their views for organizational design in the face of rapid technological advancement suggest that while technological progress is exponential, organizational design has lagged behind, creating a widening gap between technical solutions and the ability to effectively utilize them [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. They present a participative STARlab (sociotechnical action research) model, that outlines three levels of design work: strategic, governance and ecosystem design.\u003c/p\u003e \u003cp\u003eSubsequently, Maguire [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] discusses the application of STS principles to 21st-century technologies, including information integration, pervasive systems, artificial intelligence (AI) and cloud computing [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. He suggests that AI, particularly intelligent agents, could present themselves to users as human-like characters or avatars, cueing responses through their use of language, assumption of social roles, or physical presence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Maguire also highlights the importance of considering human factors in technology design and the need for users to learn the skills to make the best use of AI developments (e.g., as intelligent agents, human-robot interactions) while appreciating their benefits and limitations. AI’s interactional properties with its environment also enables its capability to learn and change its behaviour based on the cues from the environment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, as far as we know, these postulations have not yet been well observed and researched in practical scenarios from an STS and TLD perspective.\u003c/p\u003e \u003cp\u003eInterconnectedness of Theories and Frameworks\u003c/p\u003e \u003cp\u003eThe works of Levin, Davis et al., Pasmore et al., and Maguire demonstrate the interconnectedness of their theories and frameworks. All emphasize the importance of considering both social and technical aspects when designing and implementing new technologies in organizations. Levin's TLD model [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] aligns with contemporary STS thinking, viewing technology as shaped by social, cultural, and political factors. The Sociotechnical Hexagon model by Davis et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] complements Levin's theories by providing a systemic lens to understand the broader socio-technical context of the organisation in which technology transfer occurs. Their views imply that those involved with innovation and technology transfer require access to cultural knowledge encapsulated in technological artefacts. The acquisition of skills and understanding comes through a learning process as technology like machines and tools have cultural meaning embedded in them. This is because the individuals involved in their design and production operate under specific social and cultural conditions likely to encompass individual, professional and organisation culture. To successfully use these artifacts, users must have access to this inter-cultural knowledge. This view emphasizes the importance of communication and understanding between technology suppliers and users, and is achieved through mutual learning and dialogue.\u003c/p\u003e \u003cp\u003eLevin highlights that co-generative learning occurs with organizational innovation and development through a participative process using action research and critical systems thinking. This would include learning across boundaries of work and disciplines, resulting in participative transformation and learning within teams and across organisations. Subsequently, Pasmore et al.'s STARlab model [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] extends Levin's theories by explicitly addressing the engagement and interconnectedness of different organizational levels and the need for continuous adaptation in the face of rapid technological change. Both Levin and Pasmore et al highlight multi-stakeholder participation and collaboration through action research for innovation with successful technology acceptance and transfer. Maguire's discussion of STS principles applied to developing technologies like AI further reinforces the importance of considering human factors and the social implications of emerging technologies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of advancing technologies like robotics and AI, STS theories suggest that the development and deployment of robots and AI should have an integrated focus on technical aspects, social implications and values embedded in the technology. These concepts are critical in addressing construction industry challenges like productivity stagnation and labour shortages. Human-robot teams offer a solution to boost productivity while ensuring worker well-being and safety. Therefore, STS integration into the HRT design and development aims not to replace humans but to enhance their capabilities through collaborative intelligence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The successful development and integration of HRTs requires a socio-technical approach that addresses the interplay between technology, people, culture, and processes, as well as continuous learning and adaptation to optimize performance and human well-being.\u003c/p\u003e \u003cp\u003eIn the next section, the research questions and qualitative research methodology are outlined. A single unique case study was applied to trace the inception, design and development of a prototype human-robot team (HRT).\u003c/p\u003e \u003cp\u003eResearch questions\u003c/p\u003e \u003cp\u003eThis study investigates the interplay between Morten Levin's theories on technology transfer as a socio-technical learning and developmental process (TLD process), action research, and sociotechnical systems (STS) theories. Three research questions have been formulated.\u003c/p\u003e \u003cp\u003e1. How does having an STS mindset change the way a multi-disciplinary team works on researching, developing and testing HRTs?\u003c/p\u003e \u003cp\u003e2. What are the challenges in a multidisciplinary team in HRT development?\u003c/p\u003e \u003cp\u003e3. How does STS and Levin's theories serve to enable innovations and Technology Transfer to occur in a HRT development process?\u003c/p\u003e"},{"header":"Research Methodology","content":"\u003cp\u003eTo illustrate the synergies and potential challenges from Levin’s theories of technology transfer coupled with STS concepts, this study employs a qualitative methodology, using a case study focusing on the interactions for the design and development of human-robot teams (HRTs) for construction tasks. The qualitative approach allows for in-depth exploration of the complex social phenomena within real-life contexts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], making it suitable for investigating the socio-technical aspects of HRT development and deployment.\u003c/p\u003e\u003cp\u003eCase Study and Participants\u003c/p\u003e\u003cp\u003eThe case study focuses on a collaborative endeavour involving a construction engineering organisation and a small multidisciplinary team comprising of robotics personnel working together to develop and use collaborative robots with workers in the construction industry [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The multidisciplinary robotics team at UNITECH developed the prototype Quendabot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), an intelligent robot developed for installing screws for mass timber construction work. This case study was selected because it enables the firsthand study of socio-technical phenomena in human-robot collaborative technologies within the context of the construction industry. Purposive sampling was employed to select participants who could provide rich insights into the HRT development process [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 60-minute interviews were conducted with industry experts, leaders, project engineers and timber construction consultants and the HRT development team members to gather their views on design and development, as well as their experiences as humans working alongside collaborative robots. Follow-up on the progress of the case and ongoing developments of the HRT team was possible, aligning with the STS concept of iterative and continuous systems.\u003c/p\u003e\u003cp\u003eCase synopsis\u003c/p\u003e\u003cp\u003eResearchers at UNITECH, in collaboration with MODA (building site) and AURORA (project engineers pseudonymed as PRAKA and PEDKA and timber consultant PEKA), developed an autonomous robot, Quendabot, for timber building construction. A simple user interface allows workers to monitor the operation of the Quenda-bot and view real-time data. At the point of prototype deployment on site, a human operator was needed to feed the screws to the robot as the self-feeding mechanism was not completely developed yet due to time constraints.\u003c/p\u003e\u003cp\u003eThis innovation addresses the challenges of repetitive and strenuous tasks involved in Mass Engineered Timber construction, enhancing both safety and efficiency. The UNITECH HRT development team, led by a director (pseudonymed as DISHKA), comprises hardware (ALKA) and software engineers (GIKA), postdoctoral researchers and other collaborating academics (KASHKA) focusing on technical and sociotechnical aspects. Team meetings are held regularly to discuss developments, achievements, and next steps, with DISHKA typically leading discussions.\u003c/p\u003e\u003cp\u003eQuendabot is regularly showcased to industry visitors at the UNITECH Robotics Engineering Lab where the HRT development team demonstrate the human-robot teams completing construction tasks in a lab environment.\u003c/p\u003e\u003cp\u003eData Collection\u003c/p\u003e\u003cp\u003eThe case study was selected as it as it was possible to interview personnel involved in the development and use of Quendabot at site. The data collection in the case study approach includes semi-structured interviews, videos, photographs, and document analysis. Semi-structured interviews provide flexibility for participants to express their views while ensuring that key topics are covered [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The flexible data collection design enabled the researchers to follow up on some of the former interviews to look into the progress of the case and ensure that key aspects from original data collected are not been missed. As the data is analysed, emerging results are used to shape the next set of questions.\u003c/p\u003e\u003cp\u003eVideos and photographs served as visual data sources, capturing the interactions and dynamics within the HRT development process. Document analysis involved reviewing relevant project documents, such as design specifications, progress reports, and meeting minutes, to gain further insights into the socio-technical considerations and decision-making processes.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe data analysis followed an abductive approach, combining observation-based and rule-based thinking to provide a nuanced approach to iterative analysis and interpretations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The qualitative analysis software NVivo 14 was used to manage and organize the interview data transcripts. This facilitates the coding process and the exploration of emerging themes.\u003c/p\u003e\u003cp\u003eThe coding and analysis process (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) involved a combination of deductive and inductive coding. Deductive codes are derived from the literature review and the research questions, while inductive codes emerged from the data itself (example in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis approach emphasizes the importance of subjective yet professional analysis based on interpretation and the socio-technical systems perspectives [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Data by way of verbatims as the participants’ spoken words are included in the findings as evidence, illustration, explanation to deepen understanding and to give participants a voice [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Video and photo analyses were conducted as a secondary data source, providing additional reference for context and insights into the HRT development process.\u003c/p\u003e\u003cp\u003eThe analysis recognizes the dialectical relationship between ideas and their impact on each other, as well as the reflexivity in the research process, substantiating the researchers' role and presence in the data analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The multidisciplinary co-authors of this paper bring a shared purpose but with asymmetrical and divergent insights, enriching the analysis and interpretation of the data.\u003c/p\u003e"},{"header":"Findings and Discussion","content":"\u003cp\u003eAligned with Levin’s TLD model, the case study demonstrates the innovation approach as research-driven, and involves a lead agent (PRAKA at AURORA) handling the mediation with multiple external stakeholders and strategic consultants (PEKA and PEDKA as the Case Timber Construction Consultants), while the technology supplier is UNITECH, a university robotics research team comprising DISHKA (Director), GIKA (Software Engineer), ALKA (Hardware Engineer) and KASHKA (STS Director). We discuss further aspects of STS elements with the following findings.\u003c/p\u003e\u003cp\u003eImpact of an STS Mindset on Multi-Disciplinary Teams\u003c/p\u003e\u003cp\u003eThe adoption of an STS mindset has a significant impact on the way multi-disciplinary teams work on researching, developing, and testing Human-Robot Teams (HRTs).\u003c/p\u003e\u003cp\u003eLeadership and Mindset\u003c/p\u003e\u003cp\u003eThe findings reveal that visionary leadership, and an open-minded, forward-thinking mindset are essential for driving innovation in HRTs for construction, for instance, “\u003cem\u003eWe need some drivers or future-thinking leaders who have the vision for the future. Without them, it's very difficult to make it happen.\u003c/em\u003e\" (DISHKA, HRT Director). This is further supported by the statement, \u003cem\u003e\"I think just being open minded and having that, that perseverance, towards driving excellence and driving innovation\u003c/em\u003e” (KASHA, STS Director). Leaders with a clear vision can inspire and guide multi-disciplinary teams to push boundaries and embrace new technologies.\u003c/p\u003e\u003cp\u003eBridging Disciplinary Differences\u003c/p\u003e\u003cp\u003eAn STS mindset helps bridge the differences between various disciplines involved in HRT development, such as engineering, AI, IT, Construction and Project Management. KASHA indicated that engineers tend to have an \"\u003cem\u003eengineer's mentality\u003c/em\u003e\" and focus on technical aspects, while an STS perspective considers broader social implications. Recognizing and accommodating different communication styles and priorities can facilitate better collaboration and understanding among team members, as suggested by KASHA, \u003cem\u003e“Engineers will like draw pictures. The managers will not draw pictures, but they may have a lot of dialogue, so, depending on who is at the party.\"\u003c/em\u003e. This shows the benefits of effective management of team members from across functions is required in new product development (NPD) projects [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBalancing Research and Industry/Social Impact\u003c/p\u003e\u003cp\u003eThe findings suggest that an STS mindset encourages multi-disciplinary teams to balance academic research excellence with practical industry impact. While publishing papers is important, focusing on developing solutions that benefit the construction industry and end-users is equally crucial, as suggested, \u003cem\u003e“Although we do not publish as many papers as we expect, but I think research needs to generate benefits to the industry and people working in the industry.\"\u003c/em\u003e (DISHKA). This mindset also emphasizes the benefits of considering human factors and social impact, such as wellbeing, ergonomics and user experience, in HRT design. This can be seen through the comments, \u003cem\u003e“I think the robotics will only bring less fatigue, less stress on the human body and the guys working in allow them to do slightly modified tasks.\"\u003c/em\u003e (PEKA) and DISHKA, \u003cem\u003e“We're not taking away jobs, we're, letting the robot do unsafe tasks”\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eCollaborative Engagement with Stakeholders\u003c/p\u003e\u003cp\u003eAn STS mindset promotes collaborative engagement with stakeholders, particularly industry partners, from the early stages of HRT development. Focusing on engagement and developing solutions that benefit the construction industry and end-users is crucial as commented by KASHA \u003cem\u003e“That means that we are engaging with the people who are going to apply the solution, at the beginning of the process so that they continuously give us inputs. And we can work to develop something that they will use better.\"\u003c/em\u003e (KASHA).\u003c/p\u003e\u003cp\u003eWhile participants agreed that this was an important aspect, in practice, this principle was not always well executed. PEDKA expressed concerns about the limited involvement of the physical trade in the Quendabot project, which he felt reduced opportunities for valuable feedback and learning opportunities. \u003cem\u003e\"It was very closely managed by the people who built it, programmed it and were seeking to get the innovative results out of it. It could have been a higher level of involvement between the guys that do it from a physical perspective.\"\u003c/em\u003e (PEDKA)\u003c/p\u003e\u003cp\u003eThrough actively seeking to understand the needs and problems of construction companies, multi-disciplinary teams are more likely to develop targeted solutions that address real-world challenges. Early and continuous engagement fosters trust, buy-in, and a sense of shared ownership, leading to more successful HRT implementations, as posited by Levins’ TLD model [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Levin’s emphasis on the social and cultural aspects of technology transfer is demonstrated, with the implication that effective communication and understanding across different organizational and cultural contexts are important.\u003c/p\u003e\u003cp\u003eCo-generative learning and engagement\u003c/p\u003e\u003cp\u003eThe findings reveal that getting industry partners involved from day one to understand their problems, gather requirements and continuously seeking their input is key. This means early engagement and open conversations and with stakeholders like construction companies are needed to get their buy-in.\u003c/p\u003e\u003cp\u003eMultiple comments were raised about these aspects from both the research and engineering perspectives, for instance, \u003cem\u003e\"It's not about us telling them what we can do. It's more about they tell us what they want\"\u003c/em\u003e (DISHKA), \u003cem\u003e\"Early discussions, early engagement with anything, absolutely anything. Early engagement, as always is the key piece of the puzzle, bringing them into the table early and saying, here's the plan.\"\u003c/em\u003e (PRAKA, Project Lead Engineer at Aurora). This was also demonstrated through the comments by ALKA (HRT Hardware Engineer), \u003cem\u003e“So it's very good if their team is working with researchers, not just the very big guy (seniors) that come to our labs.\u003c/em\u003e These statements strongly suggest that researchers and industry partners need to work closely together, be open, and not hide problems from each other.\u003c/p\u003e\u003cp\u003eAdditionally, as robots become more learning-oriented, teaching humans how to effectively interact with them will become increasingly important, “\u003cem\u003eit's important for humans to know how to teach robots. They have to understand what they're learning and what the robots are actually learning.\u003c/em\u003e\" (GIKA). This leads to the next theme where robots are viewed as collaborative partners. An STS mindset that considers robots as collaborative partners rather than mere tools can foster a more human-centric approach to HRT development \u003cem\u003e“I think the mindset of it is just treating a robot like another human, like if they were a new person on the work side”\u003c/em\u003e as commented by GIKA (HRT Software Engineer), leading to better integration and acceptance of robots on construction sites.\u003c/p\u003e\u003cp\u003eChallenges faced by multidisciplinary teams\u003c/p\u003e\u003cp\u003eThe themes reveal that Levin's theories provide a valuable foundation for understanding innovation and technology transfer through having an STS mindset in multidisciplinary teams, contemporary sociotechnical systems face unique challenges in the same context, particularly resistance from stakeholders and end-users, conflicting organisational and stakeholder priorities and expectations, and emergence of AI-driven technologies in human-robot teams.\u003c/p\u003e\u003cp\u003eResistance from stakeholders\u003c/p\u003e\u003cp\u003eOne of the primary challenges is resistance from stakeholders and end-users, including construction companies and unions. This resistance typically stems from concerns about job displacement and changes to established work practices. This quote by PEKA illustrates the resistance and perceptions observed in the field, \u003cem\u003e\"The other guys who just want to use their drill every day of the week, because that's what they know.\"\u003c/em\u003e (PEDKA). \u003cem\u003e“Some [xxxxx] people have a very narrow perception of it. It's taking jobs and all those sorts of [xxxxx] things. The reality is, I don't think the robots ever done anybody out of work, all it's done is created a diverse working environment and people who learn different skills.\u003c/em\u003e\" (PEKA).\u003c/p\u003e\u003cp\u003eOvercoming this resistance requires careful communication, education, and collaboration with these stakeholders to address their concerns and demonstrate the benefits of human-robot collaboration.\u003c/p\u003e\u003cp\u003eManaging scope and expectations\u003c/p\u003e\u003cp\u003eAnother challenge is managing the scope and expectations of industry partners in the HRT design. GIKA commented that, “\u003cem\u003eWe engineered this robot to go beyond these kinds of limitations that the robot has. If it was a proper project run by a partner, they would have better scoping\u003c/em\u003e.\" (GIKA). Better scoping and clear communication of project goals and limitations can help manage expectations and ensure a more successful collaboration between the research team and industry partners.\u003c/p\u003e\u003cp\u003eLimited resources\u003c/p\u003e\u003cp\u003eLimited resources, both in terms of funding and time, can also hinder the development and scalability of HRT projects. These constraints can lead to compromises in robot design and functionality, as well as a lack of continued development and improvement. These comments illustrate the issue, \u003cem\u003e\"In terms of why the robot was not designed in the ideal way, it is mainly because we have the limitation of the resources.\"\u003c/em\u003e (DISHKA). Securing adequate funding and dedicating resources to further development are essential for realizing the full potential of HRTs in construction.\u003c/p\u003e\u003cp\u003eRisky integration of HRT into existing construction processes and workflows\u003c/p\u003e\u003cp\u003eSTS principles hold that ideally, new technologies ought to be integrated with the social systems for a unified and wholistic approach. However, the case study indicates that integrating robots into existing construction processes and workflows can be challenging.\u003c/p\u003e\u003cp\u003ePEDKA raises concerns about the ability of robots to handle dynamic and unstructured construction environments, where problem-solving and adaptability are crucial, for example,\u003c/p\u003e\u003cp\u003e \u003cem\u003e\"I think that's where the robot is limited is when it comes across a problem, something's in its way, and it just stop and wait for somebody to solve its problem in the early days.\"\u003c/em\u003e (PEDKA). \u003cem\u003e“There’s the risk of if the robot breaks down whilst it's working, then you're delaying work on site. So delay becomes a critical aspect of what we do for the robot to not have any downtime.\"\u003c/em\u003e (PRAKA, Lead Project Engineer). Robotic system downtime or breakdowns can cause delays and disrupt construction schedules, which are often tight and interdependent. For HRTs, ensuring robot reliability, maintainability, and seamless integration into construction workflows is crucial to mitigate these risks and minimize disruptions.\u003c/p\u003e\u003cp\u003eDifferent views of robots: The Robot as a Team-Mate in Human-Robot Teams (HRTs)\u003c/p\u003e\u003cp\u003eThe findings reveal contrasting perspectives between industry participants and HRT developers regarding the role of robots in HRTs as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Industry participants tend to view robots as an object, such as a tool, equipment, or machine, or as a fully autonomous technology that will naturally progress and evolve as commented by PEKA \u003cem\u003e\"It’s just a natural progression in the arsenal of tools that we have at our disposal…. As we get more skilled in being able to program to do more complex tasks, that will be the way to evolve.\"\u003c/em\u003e (PEKA). In contrast, the HRT development team perceives robots as teammates, capable of decision-making, optimization, adaptability, and intelligence. This difference in perspective can be attributed to the designers' insight, intention, and proximity to the advancements in technology, including AI capabilities, since the inception of the Quendabot project. These differences in how robots are perceived were highlighted where different stakeholders attributed different descriptions to the robot, including “alien,” “machine,” “worker,” and “colleague” based on their familiarity and experience with the robot ([\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] citing Ljungblad et al 2012).\u003c/p\u003e\u003cp\u003eThe challenges identified demonstrate the importance of effective stakeholder engagement, clear communication, adequate resource allocation, and thorough planning for the successful development and integration of human-robot teams in the construction industry. Addressing these requires a multi-faceted approach that considers the technical, social, and organizational aspects of HRT projects. Despite the different contexts, these themes are typically in line with TLD and STS thinking. However, contrasting views can spur innovation and new ways of sociotechnical thinking. This was found in the theme of ‘robot as team-mate’ and will be discussed in the next section.\u003c/p\u003e\u003cp\u003eEmergence of a new sociotechnical dynamics\u003c/p\u003e\u003cp\u003eFrom the findings, we observed that the integration of AI in HRTs introduces a new type of sociotechnical working relationship and dynamics between humans and robots. In the AI field, as robots become more proactive and intelligent agents, they challenge the traditional notions of human-robot interaction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Robots as social entities or “co-worker” affects people’s perceptions regarding their social relationships [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This shift leads to changes in human behaviours and attitudes towards robots, communication modes and cues, team agency and autonomy, self-determination, and alternative work processes and routines [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe rapid advancements in AI challenge and extend Levin's theories of technology transfer and STS thinking. With the increasing sophistication of AI systems, the co-generation of knowledge and collaborative learning between humans and humans have moved into the domain of humans and robots through transfer of skills and machine learning. Furthermore, robots powered by AI have the potential to be proactive initiators rather than mere followers or responders, leading to a more dynamic and interactive technology transfer process.\u003c/p\u003e\u003cp\u003eCollaborative Intelligence – an addition to the STS principles and Levin’s theories of co-generative learning for innovation and TLD\u003c/p\u003e\u003cp\u003eThe integration of AI in HRTs obliges an update to the traditional STS principles. The human-robot team can be viewed as an autonomous and adaptive subsystem, where team decisions are made jointly, and problems are solved within the unit based on each agent's capabilities to optimize performance and outcomes. This introduces a new principle of \u003cem\u003e\"collaborative intelligence\"\u003c/em\u003e in STS theory, emphasizing the symbiotic relationship between human and artificial intelligence in sociotechnical systems. As Wilson \u0026amp; Daugherty [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] (p.123) observe \u003cem\u003e‘Organizations that use machines merely to displace workers through automation will miss the full potential of AI. Such a strategy is misguided from the get-go. Tomorrow’s leaders will instead be those that embrace collaborative intelligence, transforming their operations, their markets, their industries, and—no less important—their workforces’.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eChallenges and Opportunities for AI-driven HRTs\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEthical Considerations\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe incorporation of HRTs that are enabled by AI as a system raises complex ethical dilemmas that need to be addressed. As AI systems become more autonomous and exhibit quasi-autonomous behaviours, issues of responsibility, accountability, transparency, and fairness become critical. Responsible AI includes considering AI systems as artefacts where humans set the purpose with societal, moral, and legal values [23].\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eStakeholder Engagement and Organizational Dynamics\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe rapidly evolving nature of technologies poses challenges for stakeholder participation and inter-organizational dynamics in HRT development and implementation. Engaging stakeholders and managing organizational change in the context of AI-driven HRTs require adaptive approaches that can keep pace with the dynamic technological landscape.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study delved into Levin's theories on technology transfer as a socio-technical learning and developmental process (TLD process), alongside action research and sociotechnical systems (STS) theories, within Human-Robot Team (HRT) development.\u003c/p\u003e \u003cp\u003eThe unique case study demonstrated how multidisciplinary teams navigate HRT design and development and provided insights into fostering co-generative learning, driving organizational innovation, and facilitating technology transfer in contemporary contexts.\u003c/p\u003e \u003cp\u003eThe research uncovered divergent viewpoints between industry participants and HRT developers regarding the role of intelligent robots in HRTs. While industry players often see robots as tools, visionary leaders and HRT designers regard them as decision-making teammates capable of optimization, adaptability, and intelligence. These differing perceptions of emerging technologies suggest the need for a paradigm shift, urging those working with robots to embrace them as collaborative partners rather than mere tools. The infusion of AI into HRTs introduces a new dimension of sociotechnical dynamics, challenging traditional human-robot interaction norms. As robots evolve into potentially proactive agents, they are likely to reshape human behaviours, communication modes, and work processes. This paper proposes integrating \"\u003cem\u003ecollaborative intelligence\u003c/em\u003e\" into STS theory to address these emerging dynamics, emphasizing the symbiotic relationship between humans, AI and robots.\u003c/p\u003e \u003cp\u003eLevin's emphasis on incorporating cultural knowledge into technological transfer resonates with STS's holistic approach to HRT development. Collaboration, flexibility, and adaptability are crucial in technology transfer programs to navigate changing conditions and unpredictable technological landscapes. Additionally, integrating new technology into strategic plans, leveraging local competencies, and fostering continuous learning optimize HRT performance. Challenges from the case study such as stakeholder resistance, resource constraints, and integration issues reveal the importance of effective stakeholder engagement, clear communication, and thorough planning for successful HRT development and technology transfer. This study has broader implications for technology development and transfer initiatives, emphasizing skill leveraging, adaptability, and collaboration.\u003c/p\u003e \u003cp\u003eHowever, limitations like single-case focus and evolving AI technologies may affect generalizability over time. Future research could explore AI-driven HRTs in diverse industries to grasp sociotechnical dynamics comprehensively. Developing STS principles and frameworks for HRTs enabled by AI that consider responsible and ethical deployment, skill leveraging, adaptability, and collaboration and organizational dynamics, are crucial areas of investigation. Lastly, while the original STS concepts by Emery and Trist [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] implied self-organizing teams, the question of whether intelligent robots could evolve into a new form of self-organizing teams with humans and robots jointly deciding how to allocate task dynamically during a the execution of a complex tasks in construction, has yet to be explored in sociotechnical systems research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWe confirm that we understand Journal of Systemic Practice and Action Research is a transformative journal. When research is accepted for publication, there is a choice to publish using either immediate gold open access or the traditional publishing route.\u003c/p\u003e \u003cp\u003eWe declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e \u003cp\u003eThe results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration (from all Contributing Authors) by another publisher.\u003c/p\u003e \u003cp\u003eWe have read the Nature Portfolio journal policies on author responsibilities and submit this manuscript in accordance with those policies.\u003c/p\u003e \u003cp\u003eAll of the material is owned by the authors and/or no permissions are required.\u003c/p\u003e \u003cp\u003eThe raw data that support the findings of this study are not openly available due to ethical reasons of participant confidentiality, anonymity and privacy. Summaries of the data are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at University of Technology Sydney.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003eThis study has been approved by the University of Technology, Sydney Human Research Ethics Committee (ETH22-7525) with informed consent from participants. All information is kept confidential, anonymous and private.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.A. wrote the main manuscript. S.S. and D.L. reviewed and edited the manuscript, suggested some ideas.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data that support the findings of this study are not openly available due to ethical reasons of participant confidentiality, anonymity and privacy. Summaries of the data are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at University of Technology Sydney.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLevin M (1993) Technology transfer as a learning and developmental process: an analysis of Norwegian programmes on technology transfer. Technovation 13(8):497\u0026ndash;518\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin M (1997) Technology transfer is organisational development: an investigation into the relationship between technology transfer and organisational change. Int J Technol Manage 14(2\u0026ndash;4):297\u0026ndash;308\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis MC et al (2014) Advancing socio-technical systems thinking: A call for bravery. Appl Ergon 45(2):171\u0026ndash;180\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasmore W et al (2019) Reflections: sociotechnical systems design and organization change. J Change Manage 19(2):67\u0026ndash;85\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaguire M (2014) Socio-technical systems and interaction design\u0026ndash;21st century relevance. Appl Ergon 45(2):162\u0026ndash;170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin M (1993) Creating networks for rural economic development in Norway. Hum Relat 46(2):193\u0026ndash;218\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmery FE, Trist EL (1972) Towards a social ecology: contextual appreciation of the future in the present. Plenum, London\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis MC (2019) \u003cem\u003eSocio-technical systems thinking and the design of contemporary workspace\u003c/em\u003e, in \u003cem\u003eOrganizational behaviour and the physical environment\u003c/em\u003e. Routledge, pp 128\u0026ndash;146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Acad Manag Ann 14(2):627\u0026ndash;660\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson HJ, Daugherty PR (2018) Collaborative intelligence: Humans and AI are joining forces. Harvard Business Rev 96(4):114\u0026ndash;123\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin RK (2014) Case Study Research Design and Methods, 5 edn. Sage, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe DDK et al (2023) \u003cem\u003eThe QUENDA-BOT: Autonomous Robot for Screw-Fixing Installation in Timber Building Construction\u003c/em\u003e. in. \u003cem\u003eIEEE 19th International Conference on Automation Science and Engineering (CASE)\u003c/em\u003e. 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatton MQ (2014) Qualitative research \u0026amp; evaluation methods: Integrating theory and practice. Sage\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaskarada S (2014) \u003cem\u003eQualitative case study guidelines.\u003c/em\u003e Baškarada, S. Qualitative case studies guidelines. The Qualitative Report, 2014. 19(40): pp. 1\u0026ndash;25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrivastava P, Hopwood N (2009) A practical iterative framework for qualitative data analysis. Int J qualitative methods 8(1):76\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvesson M, Sk\u0026ouml;ldberg K (2017) Reflexive methodology: New vistas for qualitative research. sage\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorden A, Sainsbury R (2006) Using verbatim quotations in reporting qualitative social research: researchers' views. University of York York\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdmondson AC, Nembhard IM (2009) Product development and learning in project teams: The challenges are the benefits. J Prod Innov Manage 26(2):123\u0026ndash;138\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaupp\u0026eacute; A, Mutlu B (2015) \u003cem\u003eThe social impact of a robot co-worker in industrial settings\u003c/em\u003e. in \u003cem\u003eProceedings of the 33rd annual ACM conference on human factors in computing systems\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"systemic-practice-and-action-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"spaa","sideBox":"Learn more about [Systemic Practice and Action Research](http://link.springer.com/journal/11213)","snPcode":"11213","submissionUrl":"https://submission.nature.com/new-submission/11213/3","title":"Systemic Practice and Action Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sociotechnical systems, Technology transfer, Human-robot teams, Multidisciplinary teams, design and development","lastPublishedDoi":"10.21203/rs.3.rs-4497385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4497385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates the synergy between Levin's theories on technology transfer as a socio-technical learning and developmental process (TLD process), action research, and sociotechnical systems (STS) theories. Levin's extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views are introduced to complement and extend Levin's theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs.\u003c/p\u003e \u003cp\u003eTo illustrate the synergies and potential challenges from Levin\u0026rsquo;s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin's theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary sociotechnical systems face unique challenges in the context of AI-driven human-robot teams where intelligent robots also contribute to the sociotechnical learning. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate of \u0026lsquo;collaborative intelligence\u0026rsquo; between human-robot teams enabled by AI. Consequently, Levin's theories of technology transfer might not fully address the complex ethical dilemmas caused by AI-driven HRT systems. Therefore addressing these challenges requires ongoing dialogue and collaboration among researchers, practitioners, and policymakers with different disciplinary backgrounds to develop robust and reliable sociotechnical systems frameworks to navigate the complexities of robotics and AI in today's rapidly evolving technological landscape.\u003c/p\u003e","manuscriptTitle":"Embracing Levin's Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design through Action Research and STS Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 14:45:08","doi":"10.21203/rs.3.rs-4497385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-08T17:41:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-15T17:50:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64920942333666285051892067165031611316","date":"2024-06-12T06:16:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93027349679104287516863216437805143204","date":"2024-06-11T09:58:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-09T08:51:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-03T07:53:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-02T22:50:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Systemic Practice and Action Research","date":"2024-05-29T13:27:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"systemic-practice-and-action-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"spaa","sideBox":"Learn more about [Systemic Practice and Action Research](http://link.springer.com/journal/11213)","snPcode":"11213","submissionUrl":"https://submission.nature.com/new-submission/11213/3","title":"Systemic Practice and Action Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"60e2c8df-cdc0-4513-90da-933718ac4bbf","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T16:11:10+00:00","versionOfRecord":{"articleIdentity":"rs-4497385","link":"https://doi.org/10.1007/s11213-024-09705-y","journal":{"identity":"systemic-practice-and-action-research","isVorOnly":false,"title":"Systemic Practice and Action Research"},"publishedOn":"2024-10-24 15:57:48","publishedOnDateReadable":"October 24th, 2024"},"versionCreatedAt":"2024-06-20 14:45:08","video":"","vorDoi":"10.1007/s11213-024-09705-y","vorDoiUrl":"https://doi.org/10.1007/s11213-024-09705-y","workflowStages":[]},"version":"v1","identity":"rs-4497385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4497385","identity":"rs-4497385","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}