Haaga-Helia University of Applied Sciences

This study investigates how transparency, fairness, and employee participation influence the ethical use and development of artificial intelligence (AI) in the workplace, with a focus on knowledge workers in Finland. Drawing on a survey of 474 respondents, the research explores how these ethical principles contribute to employee engagement and the development of digital and developer agency. The study employs confirmatory factor analysis and path analysis to validate a four-factor model comprising sense of fairness, transparency and involvement, participation and engagement, and development of digital and developer agency. The results show that transparency and fairness significantly enhance employee participation and engagement, which in turn fosters the development of digital and developer agency. The findings highlight the importance of inclusive and transparent AI practices in promoting ethical AI adoption and strengthening professional agency in evolving work environments. The study contributes to the growing body of research on responsible AI by offering empirical evidence on the social and organisational dimensions of AI ethics.

AI-powered devices, for example, service robots, are expected to revolutionize hospitality. As a result, research on service robots has surged in the context of hospitality. While prior studies have explored customer and employee attitudes toward using or abandoning robots in different contexts, for example, health care, there is a dearth of research into strategic decision-makers’ views on deciding to continue using or abandoning service robots in hospitality settings after their introduction. To that end, we adopt the Technology-Organization-Environment (TOE) framework through a phenomenon-driven bottom-up approach to explore why strategic decision-makers plan to (dis)continue using service robots in hospitality after their introduction. In practice, semistructured interviews were conducted with robot suppliers ( n = 8) and strategic decision-makers ( n = 10) to explore the planned (dis)continuance of service robot usage. Six key factors related to planned (dis)continuance of service robots in hospitality settings were found, revolving around drivers and barriers of robot adoption (e.g., availability of off-the-shelf robot solutions; operational complexity), the strategic focus of the company (e.g., vision and mission), the user experience (e.g., robots’ persona), technical aspects (e.g., sensors and interoperability), and economic aspects (e.g., managing cashflow). Extending the Technology-Organization-Environment framework, we derive implications for hospitality management theory and practice by developing a novel model for planned (dis)continuance of service robot usage, thus adding to the broader body of work on robot usage continuance in operational settings.

Abstract. Solid biofuels are one option to reduce fossil fuel combustion and mitigate climate change. However, large-scale combustion of solid biofuels can have significant impacts on air quality and the emissions of short-lived climate forcers. Due to lack of detailed scientific experimental data, these atmospheric emissions and their impacts are mostly unknown. In this study, we characterized primary particle emissions before and after the flue gas cleaning as well as the potential of emissions to form secondary particulate mass in the atmosphere from the compounds emitted from a large-scale, biomass-fired modern heating plant. Experiments were made at three power plant loads, i.e., 30 MW, 60 MW, and 100 MW (full load), and at each of these loads, flue gas particles were characterized by comprehensive instrument setup both for their physical and chemical characteristics. The study highlights the importance of efficient flue gas cleaning in biofuel applications; the bag-house filters (BHFs) utilized to clean the flue gas from combustion boiler reduced the particle number emissions three orders of magnitudes and the BC emissions close to zero. After the filtration, the measured primary particle number emissions were at 30 MW, 60 MW, and 100 MW 1.7∙103 MJ-1, 5.2∙103 MJ-1, and 7.2∙103 MJ-1, respectively. By number, emitted particles existed mostly in sub-200 nm mobility particle size range. When measuring the potential of flue gas to form secondary aerosol in the atmosphere, for the first time according to authors knowledge, we observed that the secondary aerosol formation potential of the flue gas is high; the total impact of flue gases to atmospheric particulate matter concentrations can be even 100–1000 times higher than the impact of primary particle emissions. In general, the results of the study enable emission inventory updates, improved air-quality assessments, and climate modeling to support the transition toward climate-neutral societies.

ABSTRACT There is a vibrant debate concerning the integration of immersive learning content into hospitality education to (a.) embed the learning process in real-life professional contexts and, (b.) uphold student engagement. Contributing to this debate, this article explores the development process of low-end immersive AR/VR content for hospitality education, leveraging design-based research (DBR) centered around student feedback. This study is based on qualitative focus group data triangulated with survey data collected across four universities located in Europe, each of which offers hospitality programs. Our findings suggest a three-stage development process framework. This process comprises three decision-making dilemmas which need to be addressed in developing low-end immersive learning content for hospitality education: 1.) Identifying application scenarios, 2.) Choosing technology solutions, and 3.) Designing immersive content. Our study has implications for hospitality educators by underscoring the importance of student feedback, ongoing experimentation, and educator upskilling in evolving hospitality education through integrating immersive content.

Small and medium-sized enterprises (SMEs) lag behind in AI adoption and require support to assess and enhance their AI implementation capabilities. Such support is often provided via advisory sessions, but there is a potential to automate these tasks partially. Needs analysis identified the following key requirements for the supporting tool: 1. Ability to suggest and select improvement actions (prescriptive functionality) in addition to the assessment of current state (descriptive functionality); 2. SMEs-focus, which is mostly reflected in the selection of maturity assessment indicators and emphasis on low maturity levels; 3. Completeness justification of AI implementation capability assessment. The existing AI maturity models do not satisfy all these requirements, so there is a need to develop a new tool. The paper presents an ongoing research project aiming to create an AI implementation capability development planning tool for SMEs (AICapDev). This tool will include a prescriptive maturity model for AI implementation capability and roadmap drafting component to prioritise and plan improvement action. The current paper presents the preliminary research results, more specifically problem definition and requirements specification, description of the AICapDev tool and its usage demonstration. The future work presented in the conclusion.

On the technical side, both traditional, shallow feature-based methods (e.g., Support Vector Machines and Random Forest) and deep neural network models (e.g., LSTM and CNN types) have been utilized (Khare et al., 2024;Pepa et al., 2023). A key focus has been the development of multimodal systems that integrate diverse data sources, such as facial expressions, voice tone, dialogue sentiment, and physiological signals (Geetha et al., 2024). Multiple signals enable the aggregation of complementary information, leveraging the strengths of various measurement techniques to enhance robustness and accuracy.Technology is only effective with users; thus, beyond developing methods, fostering adoption among professionals and citizens is essential. Alongside technical advancements, researchers have explored psychological and social factors influencing the acceptance and trust of AI systems, particularly in healthcare and education. Trust has proven pivotal for AI adoption, with studies investigating how to build it and what system attributes ensure trustworthiness (Li et al., 2024). Key trust factors include explainability, transparency in processes and data usage, and the credibility of the institutions behind AI development.This Research Topic explores novel theories, methodologies, and applications of Emotion AI in healthcare and education. The published works address technical aspects, models, and AI acceptance and literacy. Burgess et al. (2023) evaluated automated facial coding software for parent-infant interactions across five studies with fathers and mothers. Automated detection rates were low (~25%) compared to manual coding in naturalistic settings but strongly correlated with manual assessments, particularly for positive expressions, when successful. Key challenges included poor lighting, facial occlusion, and rapid movements, highlighting the need for greater robustness in real-world conditions. Despite these limitations, the study demonstrated the potential of automated systems for analyzing authentic emotional expressions in parent-child interactions. 2023) introduced an innovative semi-supervised learning approach for stress monitoring. Using physiological data from 14 participants across five experiments, they achieved 77% accuracy with label propagation and 76% with deep autoencoders despite utilizing only 17% labeled data. Their method matched the performance of fully supervised approaches while substantially reducing annotation requirements, offering a practical solution for continuous stress monitoring in real-world healthcare applications. Žvanut and Mihelič (2024) identified four distinct attitudes among older adults toward domestic social robots: Cautious Optimists, Skeptical Traditionalists, Positive Optimists, and Technophiles. Through interviews with 24 participants, they highlighted the influence of factors like technology familiarity, privacy concerns, and perceived utility on AI acceptance. Their findings offer valuable insights for designing emotionally intelligent robotic assistants tailored to diverse user needs and concerns. Shen and Cui (2024) investigated the link between psychological needs satisfaction and AI literacy in 445 university students. Their findings revealed that technical and teacher support positively impacted students' autonomy and competence, subsequently improving AI literacy. Notably, satisfying psychological needs proved more critical than direct support in enhancing AI literacy. These insights offer valuable guidance for designing AI-enhanced educational environments that foster better learning and engagement. Gong et al. (2024) examined patients' trust in AI-powered pharmacy intravenous admixture services across five studies. They found that patients generally trusted AI PIVAS less than human services, primarily due to a limited subjective understanding of AI systems. However, informed consent significantly improved trust by enhancing patients' understanding. This study underscores the critical role of transparent communication and psychological factors in adopting AI in healthcare.The articles in this Research Topic advance methods for analyzing real-world datasets and explore how Emotion AI can be effectively integrated into healthcare and education, addressing challenges related to trust, privacy, and user acceptance. While Emotion AI shows promise, its use in these fields raises technical and ethical concerns, including data privacy, surveillance, and potential misuse of personal information. Clinical applications are further constrained by small sample sizes, lack of control groups, limited real-world testing, and methodological variability, which hinder reproducibility and reliability (Pepa et al., 2023). These studies tackle these challenges by developing novel model-training approaches for limited and noisy labeled data and identifying factors influencing AI acceptance and trust.Future research should prioritize enhancing the robustness of emotion recognition in naturalistic settings, developing efficient learning methods to reduce dependence on labeled data, and addressing ethical considerations to support user acceptance of AI. Emphasis on cross-cultural validation and long-term evaluation in real-world applications is crucial. Recent generative AI (GenAI) foundation models, such as GPT-4, offer significant potential for advancing Emotion AI due to their versatility (Cheng et al., 2023). GenAI has already shown promise in recognizing emotional indicators (Elyoseph et al., 2024) and could help alleviate data scarcity challenges that hinder specialized model development. Additionally, there is growing interest in the emotional analysis of groups and crowds, broadening the field's scope (Veltmeijer et al., 2023;Li et al. 2024).

PurposeThe experience of the service is not a conscious decision. Customer experience is built on the customer’s interpretations and feelings that they experience during customer journey. The customer experience is influenced by different interaction situations, as well as, for example, the physical environment. In sports services, customers and instructors are in various interaction situations during service journey. Each customer experiences the service in their own way, and for this reason, the experiences of sports services vary between customers. The aim of this study was to examine the factors that influence customer experience in sports services.MethodsThe study was carried out using qualitative methods. There were 22 participants in the study who occasionally use or do not use sports services. On average, the participants were 48 years old. The data were collected through written stories during autumn 2023. Participants described thoughts and feelings related to different phases of sports service journey with the help of assistive questions. The data were analyzed using content analysis.ResultsThe results showed that safety experience, encounters, and the instructor’s expertise strengthen the customer experience in sports services. The experience of safety was related to the activities of the sports service and the instructor, the content of the group, other participants of the group, and the surrounding environment. When using the sports service, customers experience fear of how their own competence, fitness, and skill fit into the selected group.ConclusionThis study provided a broader understanding of the construction of customer experience in sports services. It makes visible which are the main elements that strengthen the customer experience in sports services. The results can be used to develop the customer experience of users of sports services and to promote the use of people’s sports services as part of physical activity promotion.The research has been co-funded by the European Union.

Facial expression recognition (FER) is crucial in enhancing human-computer interaction. While current FER methods, leveraging various open-source deep learning models and training techniques, have shown promising accuracy and generalizability, their efficacy often diminishes in real-world scenarios that are not extensively studied. Addressing this gap, we introduce a novel in-the-wild balanced testing facial expression dataset designed for cross-domain validation, called BTFER. We rigorously evaluated widely utilized networks and self-designed architectures, adhering to a standardized protocol. Additionally, we explored different configurations, including input resolutions, class balance management, and pre-trained strategies, to ascertain their impact on performance. Through comprehensive testing across three major FER datasets and our in-depth cross-validation, we have ranked these network architectures and formulated a series of practical guidelines for implementing deep learning-based FER solutions in real-life applications. This paper also delves into the ethical considerations, privacy concerns, and regulatory aspects relevant to the deployment of FER technologies in sectors such as marketing, education, entertainment, and healthcare, aiming to foster responsible and effective use. The BTFER dataset and the implementation code are available in Kaggle and Github, respectively.

We studied change initiatives towards self-managing organization in five companies, focusing on changes in leader-follower relations. Our discursive analysis based on interviews of 18 middle-managers and 38 employees suggests that organizational members identify with different types of responsibilities depending on their organizational position. We grouped these responsibilities into four orientations – organizational, institutional, coordination, and individual/work – that involve both synergistic and antagonistic elements, reflecting a plurality of interests and organizational concerns. When the authority relations between ‘leaders’ and ‘followers’ were weakened in the case organizations, these asymmetries of responsibility pushed the authoring of organizational activities into divergent trajectories. Sometimes this divergence was perceived by managers as conflicting with the organizational or institutional responsibilities they identified with. Managers controlled this tension both by influencing their subordinates’ authoring normatively and by resorting to hierarchical control practices in situations and authoring arenas perceived as critical. This resulted in hybrid arrangements including both shared and hierarchical forms of control. Eventually, one of the companies remained in and another one reintroduced conventional hierarchical structures. Accordingly, we discuss our reservations regarding the emancipatory enthusiasm around shared forms of control, as the shared mode of control seems to ‘work’ as long as employee authoring is contained within managerial power and interests.