Among runners, there is a high drop-out rate due to injuries and loss of motivation. These runners often lack personalized guidance and support. While there is much potential for sports apps to act as (e-)coaches to help these runners to avoid injuries, set goals, and maintain good intentions, most available running apps primarily focus on persuasive design features like monitoring, they offer few or no features that support personalized guidance (e.g., personalized training schemes). Therefore, we give a detailed description of the working mechanism of Inspirun e-Coach app and on how this app uses a personalized coaching approach with automatic adaptation of training schemes based on biofeedback and GPS-data. We also share insights into how end-users experience this working mechanism. The primary conclusion of this study is that the working mechanism (if provided with accurate data) automatically adapts training sessions to the runners’ physical workload and stimulates runners’ goal perception, motivation, and experienced personalization. With this mechanism, we attempted to make optimal use of the potential of wearable technology to support the large group of novice or less experienced runners and that by providing insight in our working mechanisms, it can be applied in other technologies, wearables, and types of sports.
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In an era of information overload, relevance is key. Even more so in thephysical store, where consumers are in a ‘shopping state of mind’ (Shankaret al., 2010), and where still a significant proportion of all purchasedecisions is being made.Relevance can be achieved by filtering information and targeting shopperswith context-aware messages (Riegger at al. 2022). A commonly studiedexample is that of location-based messaging (i.e. aligning the message withthe consumers’ geographic position; Meents et al. 2020). An alternativeapproach is to adapt the message to the characteristics and behavior of thein-store receiver in question, implying personalization of communication.Various technological devices can be used by retailers to transferpersonalized messages to shoppers in their stores. The focus of this studyis on digital signage (DS) in stores, as these are commonly used byretailers for their digital in-store communication.While the personalization of DS messages may benefit customers (e.g.message relevance), it also comes with high perceived risk to individualprivacy (Hess et al. 2020) To employ these type of personalized messageseffectively, it is important to understand how customers feel and respond.The present study has four objectives, examining (1) whether theperceived benefits of varying levels of personalized DS communication atthe point-of-sale outweigh the perceived risks, (2) why or why not, and (3)who is more and less open to it, and (4) for which specific situations is itmore accepted. We address these objectives both from a practitioner andconsumer perspective, using a mixed-methods approach.First, we have conducted 16 exploratory expert interviews with variousspecialists in the domain of artificial intelligence, shopper marketing, datamanagement and consumer privacy. Transcripts have been contentanalyzedusing NVIVO 12 software. Insights emerged in terms of how toimplement personalized targeting via DS in retail stores, minding legal aswell as ethical challenges in preserving consumer privacy. For example,the level of personalization via in-store digital screens differs greatly; DScontent can be adapted based on customers’ demographics, emotions,preferences and shopping behavior, and all possible combinations of suchpersonal information. It is expected that customers will responddifferently, depending on the level of personalization.In Spring 2023, these results will be complemented based on a consumersurvey. That way, consumers and a multitude of specialists in the smartservices context of personalized communication at the point-of-sale havebeen investigated, allowing for setting the boundaries in terms ofdesirability and feasibility (technology- and privacy preservation-wise).
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Maintaining the child-robot relationship after a significant break, such as a holiday, is an important step for developing sustainable social robots for education. We ran a four-session user study (n = 113 children) that included a nine-month break between the third and fourth session. During the study, participants practiced math with the help of a social robot math tutor. We found that social personalization is an effective strategy to better sustain the child-robot relationship than the absence of social personalization. To become reacquainted after the long break, the robot summarizes a few pieces of information it had stored about the child. This gives children a feeling of being remembered, which is a key contributor to the effectiveness of social personalization. Enabling the robot to refer to information previously shared by the child is another key contributor to social personalization. Conditional for its effectiveness, however, is that children notice these memory references. Finally, although we found that children's interest in the tutoring content is related to relationship formation, personalizing the topics did not lead to more interest in the content. It seems likely that not all of the memory information that was used to personalize the content was up-to-date or socially relevant.
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Let's Get Personal - Social Personalization for Sustainable Long-Term Educational Robots
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The Importance of a Robot Math Tutor’s Social Interaction Skills: Scaffolding and Personalization
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PurposeThis study aims to develop an understanding of how customers of a physical retail store valuate receiving location-based mobile phone messages when they are in proximity of the store. It proposes and tests a model relating two benefits (personalization and location congruency) and two sacrifices (privacy concern and intrusiveness) to message value perceptions and store visit attitudes.Design/methodology/approachThe study uses a vignette-based survey to collect data from a sample of 1,225 customers of a fashion retailer. The postulated research model is estimated using SmartPLS 3.0 with the consistent-PLS algorithm and further validated via a post-hoc test.FindingsThe empirical testing confirms the predictive validity and robustness of the model and reveals that location congruency and intrusiveness are the location-based message characteristics with the strongest effects on message value and store visit attitude.Originality/valueThe paper adds to the underexplored field of store entry research and extends previous location-based messaging studies by integrating personalization, location congruency, privacy concern and intrusiveness into one validated model.
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Post-training quantization reduces the computational demand of Large Language Models (LLMs) but can weaken some of their capabilities. Since LLM abilities emerge with scale, smaller LLMs are more sensitive to quantization. In this paper, we explore how quantization affects smaller LLMs’ ability to perform retrieval-augmented generation (RAG), specifically in longer contexts. We chose personalization for evaluation because it is a challenging domain to perform using RAG as it requires long-context reasoning over multiple documents. We compare the original FP16 and the quantized INT4 performance of multiple 7B and 8B LLMs on two tasks while progressively increasing the number of retrieved documents to test how quantized models fare against longer contexts. To better understand the effect of retrieval, we evaluate three retrieval models in our experiments. Our findings reveal that if a 7B LLM performs the task well, quantization does not impair its performance and long-context reasoning capabilities. We conclude that it is possible to utilize RAG with quantized smaller LLMs.
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Purpose: Aftercare for curatively treated breast cancer patients includes support and information provision. As patients differ in their needs, personalization of aftercare is advocated, but clear guidelines on how to achieve personalization are currently missing. This study investigates patients’ preferences regarding assessment of care needs and information provision. Method: Semi-structured interviews were conducted with 18 breast cancer patients (15 female, 3 male) who received aftercare for at least three months in five Dutch hospitals. Interviews were analyzed using thematic analysis. Results: Several patients perceived current aftercare as too intensive or too little, therefore they preferred to discuss their needs beforehand with their health care provider to align aftercare with their needs. Patients preferred more attention to needs on the domains of social and emotional wellbeing and return to work. Patients preferred a comprehensive resource of information on potential (late) effects of cancer and its treatment and of available support options, enabling them to self-manage the dosage and timing of desired information. Patients had positive expectations about an aftercare plan, as it would provide a better overview of their care needs, support options and agreements about the aftercare trajectory. Conclusions: To facilitate personalization in aftercare, information and care needs should be better addressed and summarized in an aftercare plan. Patients and healthcare practitioners should create the aftercare plan together in shared decision-making. A supporting tool is needed to improve assessment of care needs on multiple domains, to provide layered information and facilitate use of aftercare plans.
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