Human-technology Interaction Research Articles

Integrating artificial intelligence into team decision‐making: Toward a theory of AI–human team effectiveness

AbstractArtificial intelligence (AI), a technological advancement radically affecting the workplace of the future, offers benefits for decision‐making processes. The most critical organizational decisions typically occur through teams. However, team theories are fundamentally psychosocial, and AI disrupts this context by introducing a non‐human actor. Therefore, negative implications of AI's role must also be understood. Toward theory on the effective integration of AI into team decision‐making, we synthesize the literatures on team effectiveness and team decision‐making with research from cognate disciplines on human–technology interaction and teaming. Based on this synthesis, we offer propositions highlighting key variable relationships and negative side effects that must be accounted for in AI–human team decision‐making and follow with practical suggestions for management's adaption to this new context. Overall, our analysis emphasizes critical themes, constructs, and relationships valuable for further research aimed at modernizing theory and practice in the face of this emerging technological shift.

Construction Jobsite Image Classification Using an Edge Computing Framework

Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that have limited telecommunication support or access due to high signal attenuation within a structure. To address this issue, this research proposes an efficient edge-computing-enabled image classification framework for support of real-time construction AI applications. A lightweight binary image classifier was developed using MobileNet transfer learning, followed by a quantization process to reduce model size while maintaining accuracy. A complete edge computing hardware module, including components like Raspberry Pi, Edge TPU, and battery, was assembled, and a multimodal software module (incorporating visual, textual, and audio data) was integrated into the edge computing environment to enable an intelligent image classification system. Two practical case studies involving material classification and safety detection were deployed to demonstrate the effectiveness of the proposed framework. The results demonstrated the developed prototype successfully synchronized multimodal mechanisms and achieved zero latency in differentiating materials and identifying hazardous nails without any internet connectivity. Construction managers can leverage the developed prototype to facilitate centralized management efforts without compromising accuracy or extra investment in computing resources. This research paves the way for edge “intelligence” to be enabled for future construction job sites and promote real-time human-technology interactions without the need for high-speed internet.

Understanding human–technology interaction: evolving boundaries

PurposeThis paper explores the intricate evolution of human–machine interaction within the framework of Society 5.0, highlighting how technological advancements influence human behavior and societal progress.Design/methodology/approachA multidimensional framework is developed through a qualitative approach to analyze the evolution of human–technology interaction. This innovative conceptual model provides a comprehensive understanding of these dynamics.FindingsThe proposed model unveils a significant shift from perceiving technology as a mere tool to acknowledging its profound influence on daily routines and potential enhancements. This revelation underscores the critical role of human-centric strategies, enlightening us about the evolving nature of technology.Research limitations/implicationsThe study synthesizes existing research to present a comprehensive framework for understanding human–machine interaction, providing insights that can inform future research and theory development.Practical implicationsThis study equips policymakers, researchers and practitioners with actionable strategies to enhance technology use. By incorporating human-centric approaches, addressing issues such as technostress and promoting effective digital knowledge management, this research empowers the audience with practical solutions.Social implicationsThe paper underscores the broader impact of technology on societal progress. It emphasizes the importance of incorporating human-centric strategies in technology implementation to promote fairness, inclusivity and overall societal well-being.Originality/valueThis paper introduces an innovative conceptual model based on a multidimensional framework to analyze the complex evolution of human–technology interaction. It enriches the ongoing debate by highlighting the nuanced relationship between humans and technology, addressing significant gaps in existing literature and offering an original interpretative perspective.

Care robot literacy: integrating AI ethics and technological literacy in contemporary healthcare

AbstractHealthcare work is guided by care ethics, and any technological changes, including the use of robots and artificial intelligence (AI), must comply with existing norms, values and work practices. By bridging technological literacy and AI ethics, this study provides a nuanced definition and an integrative conceptualization of care robot literacy (CRL) for contemporary care work. Robotized care tasks require new orientation and qualifications on the part of employees. CRL is considered as one of these new demands, which requires practitioners to have the resources, skills and understanding necessary to work with robots. This study builds on sociotechnical approach of literacy by highlighting a dynamic relationship of care robotization in which successful human–technology interaction relies on exchanges between the technological and the social. Our findings from directed content analysis and theoretical synthesis of in-demand technological literacy and AI ethics in care work emphasize competencies and situational awareness regarding both using the robot and communicating about the care robot. The initial conceptualization of CRL provides a conceptual framework for future studies, implementation and product development of care robots, drastically differing from studying, implementing and developing robots in general. In searching for technologically sound and ethically compliant solutions, the study advocates for the future significance of context-specific CRL as valuable addition to the terminology of ethical AI in healthcare.

Dwelling in matter: flows of affect in teachers’ professional becoming-with-technology

ABSTRACT The aim of the present article is to leverage new materialism as a framework for re-conceptualising teachers’ digital professional development. Building upon the Deleuzo-Guattarian concepts of affect and machinism, I suggest that technology use cannot be conceptualised as pre-given property or service of independently existing entities. Instead, it needs to be studied as a relational phenomenon in its ongoing materialisation. I further argue that technologically supported processes of teachers’ professional development cannot have a pre-existing status, since they gain substance only when drawn into machinic encounters occurring between human and non-human entities. My main argument is that such encounters generate new potential that interrupts and differentiates the intensity and quality of teachers’ becoming-with-technology. By building upon these insights, I propose the concept of techno-ontology as an ontology of meaning that can help teacher educators re-examine the multiplicity of the affective forces that constitute the conditions of human-technology interaction. This is a vision that seeks to free teachers’ professional development from rigidified standards of digital competence and attend to its reinvestment as a creative process of digitally agentic becomings.

How can gaps in human-technology interaction be closed to allow older people to participate in digital solutions?

How can gaps in human-technology interaction be closed to allow older people to participate in digital solutions?

Virtual Experiences, Real Memories? A Study on Information Recall and Recognition in the Metaverse

AbstractThere are high expectations towards extended reality (XR), namely the “metaverse”. However, human performance in the metaverse has been called into question when undertaking everyday activities (e.g., working, shopping, and learning etc.), as complex human-technology interaction required may hinder cognitive abilities such as processing of information. Therefore, this study attempts to address whether and how XR impacts abilities to recall and recognize information in daily-life settings. We investigated the effects of VR and AR in a 2 (VR: yes vs. no) × 2 (AR: yes vs. no) between-subjects design experiment related to a shopping task (N = 153) on textual (product names) and pictorial (product pictures) recognition and recall. The results show that textual information recall and pictorial information recognition did not significantly suffer in XR compared to shopping in a brick-and-mortar store. While regarding textual information recognition performance, the results show that fully physical environments offered the highest performance compared to the different XR technologies being used. Overall, the study provides important findings and guidance for the use of extended reality technologies in consumer-facing businesses, as well as the use of XR in everyday life in general.

A summative user evaluation of connected thermostats

Thermostats are the nexus of human comfort within buildings. However, the building industry has prioritized a “product-centered” approach over a “human-centered” approach when developing thermostats. As technology advances, we have seen a proliferation of connected thermostats developed to provide a more convenient way to manage thermal comfort. However, if connected thermostats are not designed based on user needs, their benefits will not be fully realized. To understand how connected thermostats meet or do not meet user needs, this summative evaluation study employed text-mining of 130,237 user reviews from Amazon (n = 35,275), and Apple App Store (n = 94,962) to systematically evaluate users' perception of 52 connected thermostats and their 37 associated iOS apps using a grounded theory approach. The analysis revealed emergent themes from Amazon reviews, including “usability,” “functionality,” “user support,” “connection and compatibility” “overall impression,” and “cost and value”. Similarly, Apple App Store reviews highlighted the themes “usability,” “functionality,” “update and maintenance,” “reliability and performance,” “overall impression,” and “user support” in order of their importance. Usability emerged as the most frequent and important theme in both Amazon and Apple App Store reviews, highlighting the role of human-technology interactions. While users extensively discussed concepts like “ease” and “comfort,” “energy” was not as prominently featured. In addition, the analysis found that “usage data privacy” was a significant concern for users. This study's approach can be employed to enhance human factors knowledge in connected building interface design, and this methodological approach could be an effective supplement for other human-building product evaluations.

Challenges in adopting and using online recruitment tools from employers’ perspective

Purpose This paper aims to contribute to the existing literature by providing a unique exploration of the challenges in the adoption and usage of corporate websites, job aggregators, digital job boards, professional social media and artificial intelligence (AI)-enabled tools for recruitment. Design/methodology/approach In this exploratory study, interviews were conducted with 15 experts in digital companies with AI, recruitment or human eesources expertise. Findings The findings suggest three major themes, including technological, human–technology interaction and peripheral challenges. Moreover, seven sub-themes of challenges emerged from this study, namely, inefficient facilities and resources, inefficient data accumulation, resistance to change, distrust in technology, restricting regulations, toxic work culture and economic and social obstacles. Finally, this study proposes important implications and practical solutions to help professionals, companies and employers overcome challenges associated with adopting and using online recruitment tools. Originality/value Electronic human resource management has not studied the challenges associated with online recruitment tools in the context of Iranian digital organizations. This paper provides a unique exploration of the challenges in the adoption of AI in recruitment.

Bridging the digital divide: the impact of technological innovation on income inequality and human interactions

This study seeks to understand the nuanced relationship between technological innovation and income inequality with an emphasis on the broader implications of this interplay on human–technology interactions in diverse socioeconomic settings. Using cross-country panel data from 59 nations (31 developed and 28 developing) from 1995 to 2020, the study employed the common correlated effect mean group (CCEMG) estimator. The robustness of our findings was validated using the augmented mean group (AMG) estimator and the panel causality test. The results indicate that technological innovation, while heralded for its potential to bridge communication and operational gaps, inadvertently exacerbates income disparities, with a pronounced effect in developed economies. Moreover, interactions between technological innovation and variables such as economic growth, globalisation and export trade introduce additional complexities, including both buffering and acceleration effects on the primary relationship. These findings shed light on the double-edged nature of technological advancements, underscoring the need for informed policy-making that harnesses the benefits of innovation while mitigating its unintended socioeconomic consequences. The study sets the stage for domain-specific explorations such as in education, public health and business. It also invites interdisciplinary discourse on the ethical and behavioural dimensions of technology adoption, especially user experiences and societal outcomes.

Artificial intelligent housekeeper based on consumer purchase decision: a case study of online E-commerce

PurposeThe rise of artificial intelligence (AI) and machine learning has largely promoted the emergence of “autonomous decision-making” (ADM). This paper aims to establish a personalized artificial intelligent housekeeper (AIH) that knows more about our hobbies, habits, personality traits, and shopping needs than ourselves and can replace us to do some habitual purchasing behavior.Design/methodology/approachWe propose an AI decision-making method based on machine learning algorithm, a novel framework for personalized customer preference and purchase. First, the method uses interactive big data to predict a potential consumer’s decision possibility. Then, the method mines the correlation between consumer decision possibility and various factors affecting consumer behavior. Finally, the machine learning algorithm is used to estimate the consumer’s purchase decision according to the comprehensive influencing factors data of the target consumer.FindingsThe experimental results show that the method can predict the regular consumption behavior of consumers in advance and make accurate decision-making behavior. It can find correlations from a large amount of data to help predict many simple purchase decisions in our life, and become our AIH.Originality/valueThis study introduces a new approach that not only has the auxiliary decision-making function but also has the decision-making function. These findings contribute to the research on automated decision-making process of AI and on human–technology interaction by investigating how data attributes consumer purchase decision to AI.

Transhumanism and One Health

Abstract In this article, we draw on an interdisciplinary seminar at the University of Basel in the spring semester 2023. The seminar firstly discussed technical transhumanism between improving human lives through technological innovation and breathtaking advances in artificial intelligence (AI). Secondly, integrative approaches to health such as One Health have a transhumanist dimension, meaning that the interaction of human and animal health contributes to human health and development. As technological and biological dimensions of transhumanism raise philosophical, anthropological and ethical questions, we ask what is specifically human and what kind of future human-technology interactions is societally acceptable. With the rapid recent development of AI, questions arise about algorithms taking over human intellectual functions. Transhumanism can be described as a current of a posthumanist turn that generally asks where the limits of the human body and also of human existence lie. From an anthropological point of view a controversial discussion is held ‘beyond the human’ about blurring of the boundaries between humans, animals and nature. The One Health approach demands that animals should not suffer in place of humans and that the health of humans, animals, plants and the environment must be promoted simultaneously. This places the One Health approach in a tension and dilemma between conflicting ethical and purely human demands for the protection and welfare of animals, as well as the legitimate demands for animal food and sheer survival in many areas of the world. A biological transhumanism certainly has less of a claim to surpass specifically human performance and thus stands less or not at all in the more gnostic technological current of technical transhumanism, which replaces human labour and suddenly achieves the ability to turn against humans. One Health impact statement Integrative approaches to health such as One Health have a transhumanist dimension, meaning that the interaction of human and animal health contributes to human health and development. The One Health approach demands that animals should not suffer in place of humans and that the health of humans, animals, plants and the environment must be promoted simultaneously. This places the One Health approach in a tension and dilemma between conflicting ethical and purely human demands for the protection and welfare of animals, as well as the legitimate demands for animal food and sheer survival in many areas of the world. Because of the high variability of the culturally-religiously shaped human-animal-environment relationship, such a transdisciplinary, participatory consensus would need to be contextually adapted, that is, to geographically, culturally-religiously distinct situations.Keywords: Transhumanism, Technical, Biological, One Health, Anthropology, Ontology, Ethics

A drop of light: an interactive new media art investigation of human-technology symbiosis

Human-technology interactions have constantly exposed humans’ technologically mediated perception to a psychological paradox of two conflicting coupled states. The first supports human capabilities, whereas the second affects individuals’ self-perceptions. “A Drop of Light” is an interactive new media art investigation based on human-computer interaction. It seeks to reproduce this paradox by developing a computer-generated holographic spot. This spot acts as a responsive technological hypermedium with which participants interact, and their technologically mediated perceptions are embodied, expressed, and analyzed. By defining and applying three structural principles of an assumed technological embodiment model of human perception, a dialogue was established between the generated holographic spot and the participants to facilitate three levels of integration with the technological hypermedium. The project culminated in the creation of an empirical interactive installation performance employed as an experimental psychology lab. In this exhibition-based lab, the interaction zone embodied the performer’s hesitation mode. Her technological attitude and consequent responsive activities oscillated between technophilia and technophobia. These are two conflicting psychological states. By analyzing the interactions’ real-time-based processes, the role of the structural principles was validated. They were instrumental in developing the technologically mediated symbolic conversation between the performer and the hypermedium. Operationally, the principles equipped the conversation’s two parties to upgrade their mutual interactions toward a symbiotic coexistence in the installation’s ecosystem. Through this, the performer’s hesitation mode was reduced, and her balanced technological attitudes were validated. Within this context, the project emphasizes how interactive new media practices contribute. They help investigate participants’ technologically mediated experiences and behaviors. They also aid in the derivation of related quantitative and qualitative data in favor of human perception studies and related psychological inquiries.

Audio Enabled Object Detection System with NLP based Interaction

The project aims to develop an innovative object detection system that uses audio input and output to enhance user engagement and provide detailed product information. The system employs deep learning techniques for real-time object identification, generating an audio-based output with the object's name. In order to enable users to interact with the system verbally, it also incorporates Natural Language Processing (NLP), which interprets and processes speech in order to identify the object. The system also includes an online search module, which provides users with descriptions of the object's attributes and potential applications. The system undergoes rigorous testing and optimization to ensure accuracy and responsiveness. The project employs user-friendly technology that can assist people with low vision or disabilities, providing a practical and time-saving alternative for seeking instant information about objects. In order to make object recognition and information retrieval a smooth and inclusive experience for all users, this audio-enabled system is a first step towards bridging the gap between artificial intelligence and human-technology interaction. Key Words: Object Detection, Natural Language Processing, YOLOv4, Google Gemini, MeaningCloud, Wikipedia library

Soft tissue material properties based on human abdominal in vivo macro-indenter measurements

Simulations of human-technology interaction in the context of product development require comprehensive knowledge of biomechanical in vivo behavior. To obtain this knowledge for the abdomen, we measured the continuous mechanical responses of the abdominal soft tissue of ten healthy participants in different lying positions anteriorly, laterally, and posteriorly under local compression depths of up to 30 mm. An experimental setup consisting of a mechatronic indenter with hemispherical tip and two time-of-flight (ToF) sensors for optical 3D displacement measurement of the surface was developed for this purpose. To account for the impact of muscle tone, experiments were conducted with both controlled activation and relaxation of the trunk muscles. Surface electromyography (sEMG) was used to monitor muscle activation levels. The obtained data sets comprise the continuous force-displacement data of six abdominal measurement regions, each synchronized with the local surface displacements resulting from the macro-indentation, and the bipolar sEMG signals at three key trunk muscles. We used inverse finite element analysis (FEA), to derive sets of nonlinear material parameters that numerically approximate the experimentally determined soft tissue behaviors. The physiological standard values obtained for all participants after data processing served as reference data. The mean stiffness of the abdomen was significantly different when the trunk muscles were activated or relaxed. No significant differences were found between the anterior-lateral measurement regions, with exception of those centered on the linea alba and centered on the muscle belly of the rectus abdominis below the intertubercular plane. The shapes and areas of deformation of the skin depended on the region and muscle activity. Using the hyperelastic Ogden model, we identified unique material parameter sets for all regions. Our findings confirmed that, in addition to the indenter force-displacement data, knowledge about tissue deformation is necessary to reliably determine unique material parameter sets using inverse FEA. The presented results can be used for finite element (FE) models of the abdomen, for example, in the context of orthopedic or biomedical product developments.

Role of Digital Transformation and Its Impact on Employee Readiness for Technology Changes Through Measurement of ‘Digital Prowess’ During Online Mode of Working

The global impact of the Covid-19 pandemic and the emergence of the civil rights movement through technologies like Chat-GPT has given a highly complexly woven social and technical fabric to dwell upon. The capacity to visualize the evolving relationship between technology and social frames in organizations through online mode of working has been the focus of the current research. In this research, the ways in which the digital prowess of employees can be measured and enhanced through the online mode of working mode of engagement of an employee with an organization has been explored. A quantitative method (SEM) has been applied in the current study to study the socio-technical factors that interplay for employee effectiveness during online mode of working. This study investigates the affordance of digital technologies through the construct of digital prowess for enhancing employee efficacy at work during the online mode of working mode of working. A research model has been developed based on the socio materiality theory and affordance theory as the theoretical background. The results show that the digital prowess constitutes three dimensions namely technical acumen, uncertainty management, and ability to collaborate. Specifically, this study provides an impetus to the role of digital transformations and its impact on employee readiness for technology changes for effectiveness at work through affordances theory which advocates that human-technology interactions can be harnessed for problem-solving capabilities if the employees are empowered with competencies like digital prowess and ways to nurture this balance of technology embedded in social interactions.

Operator guidance systems in road construction: a technological mediation perspective

PurposeUsing real-time support systems may help operators in road construction to improve paving and compaction operations. Nowadays, these systems transform from descriptive to prescriptive systems. Prescriptive or operator guidance systems propose operators actionable compaction strategies and guidance, based on the data collected. It is investigated how these systems mediate the perceptions and actions of operators in road pavement practice.Design/methodology/approachA case study is conducted on the specific application of an operator guidance system in a road pavement project. In this case study, comprehensive information is presented regarding the process of converting input in the form of data from cameras and sensors into useful output. The ways in which the operator guidance systems translate data into actionable guidance for operators are analyzed from the technological mediation perspective.FindingsOperator guidance systems mediate actions of operators physically, cognitively and contextually. These different types of action mediation are related to preconditions for successful implementation and use of these systems. Coercive interventions only succeed if there is widespread agreement among the operators. Persuasive interventions are most effective when collective and individual interests align. Contextual influence relates to designs of the operator guidance systems that determine human-technology interactions when using them.Originality/valueThis is the first study that analyzes the functioning of an operator guidance system using the technological mediation approach. It adds a new perspective on the interaction between this system and its users in road pavement practice.

When the recipe is more important than the ingredients: Unveiling the complexity of consumer use of voice assistants

AbstractThe widespread use of voice assistants (VAs) creates a pressing need to understand what drives consumers to use different VAs. Existing studies have commonly focused on the net effects of antecedents that explain why consumers adopt or continue using VAs, ignoring the complexity of consumer behavior and the combinatorial effects of multiple antecedents. Our study proposes that consumer intention to continue using VAs does not depend on a single characteristic of products or consumers but on specific configurations of such characteristics. By integrating human–technology interaction and media richness theories, we suggest that consumers with distinct psychometric profiles and learning styles may evaluate humanlike and technological attributes of VAs differently. Our study shows that the complex interconnectedness between different VA attributes and consumer characteristics can provide a holistic understanding of why some consumers continue or stop using VAs. The results advance the media richness literature by offering novel insights into multimodality in consumer–technology interactions by examining consumer evaluations of single and multimodal VAs (e.g., smart speakers vs. touchscreen smart speakers). Our study provides templates for managers to effectively design VAs aligned with their segmentation and targeting strategies.

Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review

The rapid evolution of Artificial Intelligence (AI) since its inception in the mid-20th century has significantly influenced the field of Natural Language Processing (NLP), transforming it from a rule-based system to a dynamic and adaptive model capable of understanding the complexities of human language. This paper aims to offer a comprehensive review of the various applications and methodologies of AI in NLP, serving as a detailed guide for future research and practical applications. In the early sections, the paper elucidates the indispensable role of AI in NLP, highlighting its transition from symbolic reasoning to a focus on machine learning and deep learning, and its extensive applications in sectors such as healthcare, transportation, and finance. It emphasizes the symbiotic relationship between AI and NLP, facilitated by platforms like AllenNLP, which aid in the development of advanced language understanding models. Further, the paper explores specific AI techniques employed in NLP, including machine learning, Naive Bayes, and Support Vector Machines, and identifies pressing challenges and avenues for future research. It delves into the applications of AI in NLP, showcasing its transformative potential in tasks such as machine translation, facilitated by deep learning methods, and the development of chatbots and virtual assistants that have revolutionized human-technology interaction. The paper also highlights other fields impacted by AI techniques, including text summarization, sentiment analysis, and named entity recognition, emphasizing the efficiency and accuracy brought about by the integration of AI in these areas. In conclusion, the paper summarizes the remarkable advancements and persistent challenges in NLP, such as language ambiguity and contextual understanding, and underscores the need for diverse and representative labeled data for training. Looking forward, it identifies promising research avenues including Explainable AI, Few-shot and Zero-shot Learning, and the integration of NLP with other data modalities, aiming for a holistic understanding of multimodal data. The paper calls for enhanced robustness and security in NLP systems, especially in sensitive applications like content moderation and fake news detection, to foster trust and reliability in AI technologies. It advocates for continual learning in NLP models to adapt over time without losing previously acquired knowledge, paving the way for a future where AI and NLP work synergistically to understand and generate human language more effectively and efficiently.

Talking to Multi-Party Conversational Agents in Advisory Services: Command-based vs. Conversational Interactions

Interacting with a conversational agent (CA) is becoming a major paradigm for human-technology interaction. Yet, ways for interacting with CAs are still forming, especially in situations involving more than one human. Starting an interaction with a CA might involve a wakeword and command. Alternatively, it could become active based on implicit requests and context information. Hence, CA designers face a serious dilemma: explicit commands disturb a natural conversation flow, while implicit requests might cause inadequate CA behavior. This study explores this dilemma and discusses observations from a project featuring a CA for financial advisory services. Advisors initially envisioned a CA that ''blends with the background'' and acts on context information. However, when engaging with a CA, they used conversational interactions in one part of the encounter and command-based interactions in another. We discuss this observation and contrast it against previous literature. This insight has implications for design and research.