University of Southern Maine

Service robots have been adopted in many industries, including hospitality. Yet the conditions under which customers see these robotic assistants as social entities remain unclear. This research investigates how different levels of social influence, including a service robot’s communication skills (i.e., congruent co-verbal gestures), the degree of commensality (i.e., social practice of dining together), and customers’ interactive experiences (i.e., interaction with the robot), shape human–robot interaction and subsequent service robot adoption. Three interconnected studies, including two online experiments and one field study at a restaurant, were conducted based on social impact theory. The results suggest that customers require a certain level of social influence to perceive a service robot’s social presence. This type of presence is the primary factor informing service robot adoption at low to moderate levels of social influence. However, as the level of social influence increases, customers begin to rely more on their interactive experiences (e.g., while dining or interacting with the service robot directly) when making adoption decisions. This research offers several meaningful implications. Theoretically, it is one of the first to experimentally assess the effects of social presence, customers’ dining experiences, and interactivity on service robot adoption under varying levels of social influence. Practically, it provides useful insights for managers by contextualizing customers’ real-world behavior in a restaurant setting.

The John R. Borchert Map Library at the University of Minnesota developed a K-12 Summer Teacher Fellowship in 2023. The program was modeled on programs at other map libraries through research and conversation. This article explores how the program is related to the continuing relevance of map libraries and map librarianship, the impetus for exploring a K-12 program, and the process of developing the program. The article then examines the program alongside three similar initiatives around the country. These four case studies offer a set of common elements of success that could inform the development of similar programs elsewhere, as well as justification for funding such a program. The article highlights how a collaborative approach builds a strong community of teacher fellows and demonstrates how map libraries can function as intersectional hubs with the ability to bridge the gap between the University’s extensive academic research resources and the practical needs of K-12 educators. The article includes an appendix with example calls for proposals, acceptance and rejection letters, lesson plan templates, and follow up surveys.

ABSTRACT This case study, derived from a real-life scenario faced by a New England landscaping company, reinforces essential financial accounting principles and introduces Microsoft Excel functions. Students analyze the company’s financial data to identify and correct accounting errors. The case introduces key Excel functions for business majors and illustrates their practical applications. This case study fills the gap in the literature by integrating financial accounting principles with Excel training. Feedback from students shows that the case not only enhanced their understanding of accounting and improved their Excel skills but also sparked their enthusiasm by applying their knowledge in a real-life scenario. The case effectively helps students improve their analytical thinking, motivation, and confidence. To implement the case effectively, a basic understanding of financial accounting concepts is required.

Purpose The adoption of service robots in the hospitality and tourism industries has transformed service encounters into human–robot interactions. This study aims to investigate whether service providers can benefit from increasing recovery effort when service robots are involved. Design/methodology/approach Drawing on the theory of mind perception, the study employed field and online experiments to examine the effects of robotic service recovery strategies on customer forgiveness and satisfaction. This mixed-methods approach ensures robust and ecologically valid findings. Findings The results reveal that higher recovery effort by a service robot positively influences customer forgiveness, which in turn increases customer satisfaction. Interestingly, when customers attribute less blame to the robot and perceive stronger rapport with it, increasing recovery effort does not yield additional positive effects. Practical implications This study offers practical guidance for restaurant managers on managing robotic service failures and improving the effectiveness of recovery efforts and outcomes. Originality/value This research offers novel insights into the dynamics of service recovery in human–robot interactions. It demonstrates that service robots can foster customer forgiveness through cost-effective recovery strategies, offering both theoretical and practical implications for the restaurant industry. Also, the findings advance our understanding of how mind perception attribution shapes robotic service recovery outcomes and provide actionable guidance for optimizing service robot recovery strategies in real-world contexts.

Abstract Dementia, including Alzheimer’s disease, poses substantial challenges due to its progressive and multifaceted nature. Addressing these complexities requires healthcare students to undergo specialized training emphasizing empathy and equipping them with the skills necessary to provide compassionate, patient-centered care. This study used an embedded mixed-methods, one-group quasi-experimental design to evaluate empathy levels in pre-licensure nursing and graduate occupational therapy students (N = 125) using the Kiersma-Chen Empathy Scale-Revised (KCES-R). Empathy data was gathered at three intervals: before, immediately after, and six weeks following a virtual dementia simulation. Additionally, 36 focus groups (comprising 3-4 students each) were conducted during post-simulation debriefings to collect qualitative insights. The findings demonstrated significant increases in empathy scores across all time points, F (2,124) = 17.02, p = 0.000. Thematic analysis of the qualitative data revealed five core themes: 1) The Illusion of Empathy, 2) Building Empathetic Competence, 3) Facing Difficult Realities, 4) The Eureka Moment: Transformative Insights, and 5) Empathy within Power Dynamics. Collectively, the results indicate that simulation-based dementia education effectively fosters empathy among healthcare students while inspiring them to deliver higher-quality care. Experiential learning approaches like these are vital for preparing the next generation of healthcare professionals to meet the growing needs of individuals living with dementia.

Dementia, including Alzheimer's disease, poses complex challenges requiring health care providers to respond with empathy and skill. The current study examined whether a simulation-based dementia education intervention could enhance empathy in health care students. Using embedded mixed methods, one-group quasi-experimental design, empathy levels in 125 pre-licensure nursing and graduate occupational therapy students were measured via the Kiersma-Chen Empathy Scale-Revised (KCES-R) before, immediately after, and 6 weeks post-simulation. In addition, 36 post-debriefing focus groups explored student experiences qualitatively. Findings showed significant empathy score increases across all time points (F[2,124] = 17.02, p < 0.001). Thematic analysis revealed five themes: The Illusion of Empathy, Developing Empathetic Skills, Confronting Uncomfortable Truths, The Eureka Moment: Transformative Realizations, and Empathy and Power Dynamics. Findings suggest that simulation-based dementia experience improves empathy and motivates students to provide better care. Experiential learning is critical to preparing future health care professionals to meet the growing demands of dementia care.

The rapid advancement of generative AI has enabled the creation of highly realistic forged facial images, posing significant threats to AI security, digital media integrity, and public trust. Face forgery techniques-ranging from face swapping and attribute editing to powerful diffusion-based image synthesis-are increasingly being used for malicious purposes such as misinformation, identity fraud, and defamation. This growing challenge underscores the urgent need for robust and generalizable face forgery detection methods as a critical component of AI security infrastructure. In this work, we propose a novel dual-branch convolutional neural network for face forgery detection that leverages complementary cues from both spatial and frequency domains. The RGB branch captures semantic information, while the frequency branch focuses on high-frequency artifacts that are difficult for generative models to suppress. A channel attention module is introduced to adaptively fuse these heterogeneous features, highlighting the most informative channels for forgery discrimination. To guide the network's learning process, we design a unified loss function-FSC Loss-that combines focal loss, supervised contrastive loss, and a frequency center margin loss to enhance class separability and robustness. We evaluate our model on the DiFF benchmark, which includes forged images generated from four representative methods: text-to-image, image-to-image, face swap, and face edit. Our method achieves strong performance across all categories and outperforms average human accuracy. These results demonstrate the model's effectiveness and its potential contribution to safeguarding AI ecosystems against visual forgery attacks.