The rapid advancements in artificial intelligence (AI) have ushered in a new era of innovative applications, while also prompting concerns regarding risks and adverse consequences. In light of the growing interest in comprehending AI's impact on society and its alignment with human values and needs, Human-Centred Artificial Intelligence (HCAI) has emerged as a potential approach to address questions and concerns. In this Systematic Literature Review, we aim to contribute to conceptual clarity around the definition, conceptualisation, and implementation of HCAI. The first part of our review addresses how HCAI is defined in the existing literature, culminating in a novel comprehensive HCAI definition. Subsequently, we delve into the identified constituent elements of HCAI, namely ‘purpose’, ‘values’, and ‘properties’. Purposes include augmentation, AI autonomy, and automation. Values relate to ethics, safety, and performance. Properties cover oversight, comprehension, and integrity. The third part of the review explores Human-Centred Design processes, methods, and tools and their applicability for HCAI. In conclusion, we discuss the characteristics and critiques of HCAI and provide a research agenda. This literature review contributes to advancing the discourse on HCAI, thus enhancing human welfare and societal well-being.Abbreviations: AI: artificial intelligence; AI-HLEG: high-level expert group on artificial intelligence; GenAI: generative AI; HCAI: human-centred artificial intelligence; HCD: human-centred design; HCI: human-computer interaction; ISO: international organization for standardization; OECD: organisation for economic co-operation and development.

Advancements in human/AI interactions led to smartification of public services via the use of chatbots. Here, we present findings from a clinical inquiry research project in a key public service organisation in Norway. In this project, researchers and practitioners worked together to generate insights on the action possibilities offered to human service agents by chatbots and the potential for creating hybrid human/AI service teams. The project sensitised service agents to discover affordances based on their actual practices, rather than on the predefined use of chatbots. The different affordances identified can be useful for practitioners who design and deploy chatbot-based services. The action possibilities afforded by chatbots provide new ways for service agents and chatbots to work as a team addressing citizens’ needs. Drawing from the whole research process, we offer three lessons learned from the Norwegian Government on human/AI partnerships, theory-based interventions, and institutionalised collaborative research that can be useful for researchers that want to engage with practice and organisations that want to evolve their technology use, stimulate innovation, and engage with research.

The confluence of wearable technologies for sensing learners and the quantified-self provides a unique opportunity to understand learners’ experience in diverse learning contexts. We use data from learners using Empatica Wristbands and self-reported questionnaire. We compute stress, arousal, engagement and emotional regulation from physiological data; and perceived performance from the self-reported data. We use Fuzzy Set Qualitative Comparative Analysis (fsQCA) to find relations between the physiological measurements and the perceived learning performance. The results show how the presence or absence of arousal, engagement, emotional regulation, and stress, as well as their combinations, can be sufficient to explain high perceived learning performance

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.

The assessment of learning during class activities mostly relies on standardized questionnaires to evaluate the efficacy of the learning design elements. However, standardized questionnaires pose additional strain on students, do not provide “temporal” information during the learning experience, require considerable effort and language competence, and sometimes are not appropriate. To overcome these challenges, we propose using wearable devices, which allow for continuous and unobtrusive monitoring of physiological parameters during learning. In this paper we set out to quantify how well we can infer students’ learning experience from wrist-worn devices capturing physiological data. We collected data from 31 students in 93 class sessions (3 class sessions per student), and our analysis shows that wrist data can predict the learning experience with 11% error. We also show that 6.25 min (SD = 3.1 min) of data are needed to achieve a reliable estimate (i.e., 13.8% error). Our work highlights the benefits and limitations of utilizing wearable devices to assess learning experiences. Our findings help shape the future of quantified-self technologies in learning by pointing out the substantial benefits of physiological sensing for self-monitoring, evaluation, and metacognitive reflection in learning.

Context: Startups aim at scaling their business, often by developing innovative products with limited human and financial resources. The development of software products in the startup context is known as opportunistic, agility-driven, and with high tolerance for technical debt. The special context of hardware startups calls for a better understanding of state-of-the-practice of hardware startups’ activities. Objective: This study aimed to identify whether and how startups can achieve product quality while maintaining focus on agility. Method: We conducted an exploratory study with 13 hardware startups, collecting data through semi-structured interviews and analysis of documentation. We proposed an integrative model of agility and quality in hardware startups. Results: Agility in hardware startups is complex and not achieved through adoption of fast-paced development practices alone. Hardware startups follow a quality-driven approach for development of core components, where frequent user testing is a measure for early debt management. Hardware startups often lack mindset and strategies for achieving long-term quality in early stages. Conclusions: Hardware startups need attention to hardware quality to allow for evolutionary prototyping and speed. Future research should focus on defining quality-driven practices that contribute to agility, and strategies and mindsets to support long-term quality in the hardware startup context.

It is widely recognized by public and private organizations, that the biggest challenge faced in light of the data revolution is finding people with the required set of skills to transform data into actionable insight. The growing interest on the role of the data scientist and the relating data analytics skills has seen an increasing amount of research on the importance of data analytics skills in the contemporary working environment. Yet, there is still limited understanding on the importance of data analytic skills, and even more, there is limited research on the discrepancies between the skills that are needed in the market and what graduates possess. To this end, this research uses a mixed-methods approach combining quantitative survey data from 113 IT executives, and qualitative interview data from 27 big data project managers to explore the significance, discrepancies, and aspects of data analytic skills. Our results show that data analytic skills significantly contribute firm performance, particularly for firms that are data-oriented. In addition, we find that the need for skills greatly exceeds those that graduates possess. Lastly, our analysis suggests that the data skills of the data scientist span multiple subject areas which are further discussed.

Educating social innovators in higher education is of great importance as many societal challenges exist. This study combines experiential learning with ICT tools to provide students with the needed competences and experiences to solve societal challenges. We employ this approach in an innovative course, named Experts in Teamwork (EiT), which follows the experiential learning cycle. The participants of this study are undergraduate students interested to learn how they can solve societal challenges. Specifically, 26 students with various background and nationalities participated. A collaborative platform was developed that supports teamwork and cooperation, as well as the social innovation process. The findings show that this approach can influence positively learning outcomes and increase students' engagement and motivation with both social innovation and the learning process. Also, students' creativity was increased leading to the development of better solutions. The overall outcomes contribute to theoretical and practical development, to allow educators to take appropriate measures to enhance students' learning experience and foster social innovation through ICT.

This study focuses on compiling students' response-time allocated to answer correctly or wrongly, their self-regulation, as well as their satisfaction from content, in order to explain high or medium/low learning performance. To this end, it proposes a conceptual model in conjunction with research propositions. For the evaluation of the approach, an empirical study with 452 students was conducted. The fuzzy set qualitative comparative analysis (fsQCA) revealed five configurations driven by the admitted factors that explain students' high performance, as well as five additional patterns, interpreting students' medium/low performance. These findings advance our understanding of the relations between actual usage and latent behavioral factors, as well as their combined effect on students' test score. Limitations and potential implications of these findings are also discussed.

Educators and researchers have been working to understand the reasons that may be contributing to high dropout rates, and low rates of participation, by females in the computer and information sciences discipline. Along the same lines, and propelled by the increased need for information technology (IT) professionals worldwide, we implemented a students' survey during the fall of 2015 in Norway's primary university for technological education. In this initiative we aim to identify reasons that may be contributing to high dropout rates, low rates of participation by females and aspects important for the efficient preparation of young people for careers in computer science and information technology. The results provide valuable insights and allow us to take appropriate measures for enhancing students' learning experience in the computer and information sciences.

This study aims to identify the interrelations among performance expectancy, effort expectancy, enjoyment, and satisfaction in order to predict high intention to use a mobile application for educational services. To this end a mobile application was developed which includes important services for students in one place and it was tested through feedback from questionnaires. Building on complexity and configuration theory we present a conceptual model and employ fuzzy-set qualitative comparative analysis (fsQCA) to examine how performance expectancy, effort expectancy, enjoyment, and satisfaction combine in order to explain high and low intention to use mobile learning. The results indicate different configurations of the examined factors that explain user behavior, and verify the existence of asymmetric relations among them. The study is one of the first in the area evaluating a mobile learning application, and has both theoretical and practical implications towards the development, design and provision of mobile learning applications.

Programming-based making activities are at the core of teaching strategies to engage young students in learning programming for developing computational thinking skills. Despite the initial evidences of enthusiastic participation in such activities, more systematic studies are needed to better understand drivers of students' intentions to participate in programming-based making activities. In this paper, we present an exploratory study which aim to address this issue by examining the interrelations among cognitive (i.e., perceived usefulness, perceived ease of use) and affective (i.e., enjoyment) characteristics for both boys and girls. To this end, we build on complexity theory and configuration theory, present a conceptual model, and employ fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of 105 young students, to identify such interrelations. The findings provide insights on how the examined factors may have a different influence for boys and girls, an outcome that can be used to re-design educational programs targeting maximizing engagement regardless gender.

Social commerce has been gaining momentum over the last few years as a novel form of e-commerce, creating substantial changes for both businesses and consumers. However, little is known about how consumer behaviour is influenced by characteristics on social commerce platforms. The purpose of this research is to elucidate how user intentions to purchase and to spread word-of-mouth (WOM) are influenced by characteristics present on social commerce platforms. More specifically, we adopt a uses-and-gratifications perspective and examine the influence of socialising, personal recommendation agents, product selection, and information availability. Partial least squares structural equation modelling analysis is performed on a sample of 165 social commerce users. Outcomes of the analysis indicate that socialising and personal recommendation agents positively influence purchase and WOM intentions, while product selection is found to only enhance purchase intentions. Interestingly, our findings reveal that information availability has no significant effect on purchase and WOM intentions. Finally, we find that when purchase intentions are triggered, they will tend increase consumers’ intentions to WOM.

This study uses complexity theory to understand the causal patterns of factors that stimulate students’ intention to continue studies in computer science (CS). To this end, it identifies gains and barriers as essential factors in CS education, including motivation and learning performance, and proposes a conceptual model along with research propositions. To test its propositions, the study employs fuzzy-set qualitative comparative analysis on a data sample from 344 students. Findings indicate eight configurations of cognitive and noncognitive gains, barriers, motivation for studies, and learning performance that explain high intention to continue studies in CS. This research study contributes to the literature by (1) offering new insights into the relationships among the predictors of CS students’ intention to continue their studies and (2) advancing the theoretical foundation of how students’ gains, barriers, motivation, and learning performance combine to better explain high intentions to continue CS studies.

Research in the area of Computer Science (CS) education, has focused on identifying the reasons that students do not finish their studies in CS. Although there is increasing demand for CS professionals, there is not enough knowledge to explain the high dropout rates in CS education. This study aims to empirically examine how students' intention to complete their studies (retention) in CS is affected by variables playing a key role in higher education. By identifying which variables contribute to dropout in CS studies, we will be able to focus on how to improve aspects related with them in order to reduce dropout rates. To do so we identified the following variables: Year of studies, Gender, Age, Students' Effort, Absence from Classes, Expected Grade point average (GPA), and Current GPA, and tested their effect on retention, based on the responses collected from 241 CS student. Year of studies and Effort have positive effects on students' intention to finish their studies in CS. Interestingly, the expected GPA has a negative effect on students' intentions to finish their studies. The findings contribute to theory and practice, as they offer CS educators and policy makers insights that may aid towards increased student retention and reduced dropout rates.