9,855 research outputs found
Frontline Employeesâ Informal Learning and Customer Relationship Skills in Macao Casinos: An Empirical Study
This study uses qualitative methods to better understand how the informal learning of frontline employees influenced their customer relationship skills in dealing with patrons at gaming tables, in the hope of achieving positive customer experiences in a competitive environment in Macao. As casino operators need to get their employees to work after limited formal training, they might find that their emphasis on formal training might be insufficient to provide patrons with customized service in Macao. In this context, the concept of informal learning, which is determined and directed by learners themselves to further improve what they have learned from their formal training, is likely to be of special significance in Macao. Based upon a constructivistic framework, this study used semi-structured interviews to gather data from 49 frontline employees. The study relied upon the Miles and Huberman (1994) framework to analyze qualitative data. Data analysis suggested that informal learning among frontline employees would lead to four strategies: (i) to be polite and respect patrons; (ii) to uncover patronsâ emotional status from their body language; (iii) to manage patronsâ emotions in their gaming pursuit; and (iv) to self-regulate emotions to the demands of a service encounter
Affective automotive user interfaces
Technological progress in the fields of ubiquitous sensing and machine learning has been fueling the development of user-aware human-computer interaction in recent years. Especially natural user interfaces, like digital voice assistants, can benefit from understanding their users in order to provide a more naturalistic experience. Such systems can, for example, detect the emotional state of users and accordingly act in an empathic way. One major research field working on this topic is Affective Computing, where psycho-physiological measures, speech input, and facial expressions are used to sense human emotions.
Affective data allows natural user interfaces to respond to emotions, providing promising perspectives not only for user experience design but also for safety aspects. In automotive environments, informed estimations of the driverâs state can potentially avoid dangerous errors and evoking positive emotions can improve the experience of driving.
This dissertation explores Affective Automotive User Interfaces using two basic interaction paradigms: firstly, emotion regulation systems react to the current emotional state of the user based on live sensing data, allowing for quick interventions. Secondly, emotional interaction synthesizes experiences which resonate with the user on an emotional level. The constituted goals of these two interaction approaches are the promotion of safe behavior and an improvement of user experience.
Promoting safe behavior through emotion regulation: Systems which detect and react to the driverâs state are expected to have great potential for improving road safety. This work presents a model and methods needed to investigate such systems and an exploration of several approaches to keep the driver in a safe state. The presented methods include techniques to induce emotions and to sample the emotional state of drivers. Three driving simulator studies investigate the impacts of emotionaware interventions in the form of implicit cues, visual mirroring and empathic speech synthesis. We envision emotion-awareness as a safety feature which can detect if a driver is unfit or in need of support, based on the propagation of robust emotion detection technology.
Improving user experience with emotional interaction: Emotional perception is an essential part of user experience. This thesis entails methods to build emotional experiences derived from a variety of lab and simulator studies, expert feedback, car-storming sessions and design thinking workshops. Systems capable of adapting to the userâs preferences and traits in order to create an emotionally satisfactory user experience do not require the input of emotion detection. They rather create value through general knowledge about the user by adapting the output they generate. During this research, cultural and generational influences became evident, which have to be considered when implementing affective automotive user interfaces in future cars.
We argue that the future of user-aware interaction lies in adapting not only to the driverâs preferences and settings but also to their current state. This paves the way for the regulation of safe behavior, especially in safety-critical environments like cars, and an improvement of the driving experience.Aktuelle Fortschritte in den Bereichen des Machine Learning und Ubiquitous Computing ermöglichen es heute adaptive Mensch-Maschine-Schnittstellen zu realisieren. Vor allem natuÌrliche Interaktion, wie wir sie von Sprachassistenten kennen, profitiert von einem verbesserten VerstĂ€ndnis des Nutzerverhaltens. Zum Beispiel kann ein Assistent mit Informationen uÌber den emotionalen Zustand des Nutzers natuÌrlicher interagieren, vielleicht sogar Empathie zeigen. Affective Computing ist das damit verbundene Forschungsfeld, das sich damit beschĂ€ftigt menschliche Emotionen durch Beobachtung von physiologischen Daten, Sprache und Mimik zu erkennen.
Dabei ermöglicht Emotionserkennung natuÌrliche Interaktion auf Basis des Fahrer/innenzustands, was nicht nur vielversprechend in Bezug auf die Gestaltung des Nutzerelebnisses klingt, sondern auch Anwendungen im Bereich der Verkehrssicherheit hat. Ein Einsatz im Fahrkontext könnte so vermeidbare UnfĂ€lle verringern und gleichzeitig Fahrer durch emotionale Interaktion begeistern.
Diese Dissertation beleuchtet Affective Automotive User Interfaces â zu Deutsch in etwa Emotionsadaptive Benutzerschnittstellen im Fahrzeug â auf Basis zweier inhaltlicher SĂ€ulen: erstens benutzen wir AnsĂ€tze zur Emotionsregulierung, um im Falle gefĂ€hrlicher FahrerzustĂ€nde einzugreifen. Zweitens erzeugen wir emotional aufgeladene Interaktionen, um das Nutzererlebnis zu verbessern.
Erhöhte Sicherheit durch Emotionsregulierung: Emotionsadaptiven Systemen wird ein groĂes Potenzial zur Verbesserung der Verkehrssicherheit zugeschrieben. Wir stellen ein Modell und Methoden vor, die zur Untersuchung solcher Systeme benötigt werden und erforschen AnsĂ€tze, die dazu dienen Fahrer in einer GefuÌhlslage zu halten, die sicheres Handeln erlaubt. Die vorgestellten Methoden beinhalten AnsĂ€tze zur Emotionsinduktion und -erkennung, sowie drei Fahrsimulatorstudien zur Beeinflussung von Fahrern durch indirekte Reize, Spiegeln von Emotionen und empathischer Sprachinteraktion. Emotionsadaptive Sicherheitssysteme können in Zukunft beeintrĂ€chtigten Fahrern UnterstuÌtzung leisten und so den Verkehr sicherer machen, vorausgesetzt die technischen Grundlagen der Emotionserkennung gewinnen an Reife.
Verbesserung des Nutzererlebnisses durch emotionale Interaktion: Emotionen tragen einen groĂen Teil zum Nutzerlebnis bei, darum ist es nur sinnvoll den zweiten Fokuspunkt dieser Arbeit auf systeminitiierte emotionale Interaktion zu legen.Wir stellen die Ergebnisse nutzerzentrierter Ideenfindung und mehrer Evaluationsstudien der resultierenden Systeme vor. Um sich den Vorlieben und Eigenschaften von Nutzern anzupassen wird nicht zwingend Emotionserkennung benötigt. Der Mehrwert solcher Systeme besteht vielmehr darin, auf Basis verfuÌgbarer Verhaltensdaten ein emotional anspruchsvolles Erlebnis zu ermöglichen. In unserer Arbeit stoĂen wir auĂerdem auf kulturelle und demografische EinfluÌsse, die es bei der Gestaltung von emotionsadaptiven Nutzerschnittstellen zu beachten gibt.
Wir sehen die Zukunft nutzeradaptiver Interaktion im Fahrzeug nicht in einer rein verhaltensbasierten Anpassung, sondern erwarten ebenso emotionsbezogene Innovationen. Dadurch können zukuÌnftige Systeme sicherheitsrelevantes Verhalten regulieren und gleichzeitig das Fortbestehen der Freude am Fahren ermöglichen
Affective automotive user interfaces
Technological progress in the fields of ubiquitous sensing and machine learning has been fueling the development of user-aware human-computer interaction in recent years. Especially natural user interfaces, like digital voice assistants, can benefit from understanding their users in order to provide a more naturalistic experience. Such systems can, for example, detect the emotional state of users and accordingly act in an empathic way. One major research field working on this topic is Affective Computing, where psycho-physiological measures, speech input, and facial expressions are used to sense human emotions.
Affective data allows natural user interfaces to respond to emotions, providing promising perspectives not only for user experience design but also for safety aspects. In automotive environments, informed estimations of the driverâs state can potentially avoid dangerous errors and evoking positive emotions can improve the experience of driving.
This dissertation explores Affective Automotive User Interfaces using two basic interaction paradigms: firstly, emotion regulation systems react to the current emotional state of the user based on live sensing data, allowing for quick interventions. Secondly, emotional interaction synthesizes experiences which resonate with the user on an emotional level. The constituted goals of these two interaction approaches are the promotion of safe behavior and an improvement of user experience.
Promoting safe behavior through emotion regulation: Systems which detect and react to the driverâs state are expected to have great potential for improving road safety. This work presents a model and methods needed to investigate such systems and an exploration of several approaches to keep the driver in a safe state. The presented methods include techniques to induce emotions and to sample the emotional state of drivers. Three driving simulator studies investigate the impacts of emotionaware interventions in the form of implicit cues, visual mirroring and empathic speech synthesis. We envision emotion-awareness as a safety feature which can detect if a driver is unfit or in need of support, based on the propagation of robust emotion detection technology.
Improving user experience with emotional interaction: Emotional perception is an essential part of user experience. This thesis entails methods to build emotional experiences derived from a variety of lab and simulator studies, expert feedback, car-storming sessions and design thinking workshops. Systems capable of adapting to the userâs preferences and traits in order to create an emotionally satisfactory user experience do not require the input of emotion detection. They rather create value through general knowledge about the user by adapting the output they generate. During this research, cultural and generational influences became evident, which have to be considered when implementing affective automotive user interfaces in future cars.
We argue that the future of user-aware interaction lies in adapting not only to the driverâs preferences and settings but also to their current state. This paves the way for the regulation of safe behavior, especially in safety-critical environments like cars, and an improvement of the driving experience.Aktuelle Fortschritte in den Bereichen des Machine Learning und Ubiquitous Computing ermöglichen es heute adaptive Mensch-Maschine-Schnittstellen zu realisieren. Vor allem natuÌrliche Interaktion, wie wir sie von Sprachassistenten kennen, profitiert von einem verbesserten VerstĂ€ndnis des Nutzerverhaltens. Zum Beispiel kann ein Assistent mit Informationen uÌber den emotionalen Zustand des Nutzers natuÌrlicher interagieren, vielleicht sogar Empathie zeigen. Affective Computing ist das damit verbundene Forschungsfeld, das sich damit beschĂ€ftigt menschliche Emotionen durch Beobachtung von physiologischen Daten, Sprache und Mimik zu erkennen.
Dabei ermöglicht Emotionserkennung natuÌrliche Interaktion auf Basis des Fahrer/innenzustands, was nicht nur vielversprechend in Bezug auf die Gestaltung des Nutzerelebnisses klingt, sondern auch Anwendungen im Bereich der Verkehrssicherheit hat. Ein Einsatz im Fahrkontext könnte so vermeidbare UnfĂ€lle verringern und gleichzeitig Fahrer durch emotionale Interaktion begeistern.
Diese Dissertation beleuchtet Affective Automotive User Interfaces â zu Deutsch in etwa Emotionsadaptive Benutzerschnittstellen im Fahrzeug â auf Basis zweier inhaltlicher SĂ€ulen: erstens benutzen wir AnsĂ€tze zur Emotionsregulierung, um im Falle gefĂ€hrlicher FahrerzustĂ€nde einzugreifen. Zweitens erzeugen wir emotional aufgeladene Interaktionen, um das Nutzererlebnis zu verbessern.
Erhöhte Sicherheit durch Emotionsregulierung: Emotionsadaptiven Systemen wird ein groĂes Potenzial zur Verbesserung der Verkehrssicherheit zugeschrieben. Wir stellen ein Modell und Methoden vor, die zur Untersuchung solcher Systeme benötigt werden und erforschen AnsĂ€tze, die dazu dienen Fahrer in einer GefuÌhlslage zu halten, die sicheres Handeln erlaubt. Die vorgestellten Methoden beinhalten AnsĂ€tze zur Emotionsinduktion und -erkennung, sowie drei Fahrsimulatorstudien zur Beeinflussung von Fahrern durch indirekte Reize, Spiegeln von Emotionen und empathischer Sprachinteraktion. Emotionsadaptive Sicherheitssysteme können in Zukunft beeintrĂ€chtigten Fahrern UnterstuÌtzung leisten und so den Verkehr sicherer machen, vorausgesetzt die technischen Grundlagen der Emotionserkennung gewinnen an Reife.
Verbesserung des Nutzererlebnisses durch emotionale Interaktion: Emotionen tragen einen groĂen Teil zum Nutzerlebnis bei, darum ist es nur sinnvoll den zweiten Fokuspunkt dieser Arbeit auf systeminitiierte emotionale Interaktion zu legen.Wir stellen die Ergebnisse nutzerzentrierter Ideenfindung und mehrer Evaluationsstudien der resultierenden Systeme vor. Um sich den Vorlieben und Eigenschaften von Nutzern anzupassen wird nicht zwingend Emotionserkennung benötigt. Der Mehrwert solcher Systeme besteht vielmehr darin, auf Basis verfuÌgbarer Verhaltensdaten ein emotional anspruchsvolles Erlebnis zu ermöglichen. In unserer Arbeit stoĂen wir auĂerdem auf kulturelle und demografische EinfluÌsse, die es bei der Gestaltung von emotionsadaptiven Nutzerschnittstellen zu beachten gibt.
Wir sehen die Zukunft nutzeradaptiver Interaktion im Fahrzeug nicht in einer rein verhaltensbasierten Anpassung, sondern erwarten ebenso emotionsbezogene Innovationen. Dadurch können zukuÌnftige Systeme sicherheitsrelevantes Verhalten regulieren und gleichzeitig das Fortbestehen der Freude am Fahren ermöglichen
Reinforcement Learning Approaches in Social Robotics
This article surveys reinforcement learning approaches in social robotics.
Reinforcement learning is a framework for decision-making problems in which an
agent interacts through trial-and-error with its environment to discover an
optimal behavior. Since interaction is a key component in both reinforcement
learning and social robotics, it can be a well-suited approach for real-world
interactions with physically embodied social robots. The scope of the paper is
focused particularly on studies that include social physical robots and
real-world human-robot interactions with users. We present a thorough analysis
of reinforcement learning approaches in social robotics. In addition to a
survey, we categorize existent reinforcement learning approaches based on the
used method and the design of the reward mechanisms. Moreover, since
communication capability is a prominent feature of social robots, we discuss
and group the papers based on the communication medium used for reward
formulation. Considering the importance of designing the reward function, we
also provide a categorization of the papers based on the nature of the reward.
This categorization includes three major themes: interactive reinforcement
learning, intrinsically motivated methods, and task performance-driven methods.
The benefits and challenges of reinforcement learning in social robotics,
evaluation methods of the papers regarding whether or not they use subjective
and algorithmic measures, a discussion in the view of real-world reinforcement
learning challenges and proposed solutions, the points that remain to be
explored, including the approaches that have thus far received less attention
is also given in the paper. Thus, this paper aims to become a starting point
for researchers interested in using and applying reinforcement learning methods
in this particular research field
Exploring the factors affecting employee motivation to be innovative on product development: A case study for Woolworths South Africa
The global business industry is greatly affected by revolutionised human knowledge that requires a continued understanding of human preferences, needs and wants. Motivation to innovate must be understood when marketers aim for business success. Business success is seen in customer satisfaction and employee performance. The starting point for success is the miraculous ideas of employees. These ideas can be anything from the creation of a product or service to the execution of that product or service. Exploring the factors affecting the motivation to innovate on product development may lead marketers to business success by increased profitability, a bigger customer base, and retaining motivated skilled employees with the ability to innovate. This research is based on the South African multinational retailer Woolworths, with the focus on food and design packaging. A qualitative research approach was followed where data was collected from 11 participants using semi-structured individual face-to-face in-depth interviews and structured questionnaires. This study followed a manual thematic approach in an inductive manner. The needs of the participants in this study were categorised into three sections: Need for power, need for achievement and need for affiliation. This study also introduced McClelland's extended needs, and the motive for self-expression stood out among the cohort, confirming their creative skills. With the aid of individual components, domain-relevant skills, intrinsic task motivation and creativity stimulants, the study was able to link the presence of creativity to motivation and innovation. It confirmed that once the creativity intersection combines with organisational components there is a motivational synergy that produces innovation. The results of the study further indicated that internal motivation factors had a greater impact than external factors. As per organisational components, business values were shown to have an influence on the development of a product design. Legislation policies were deemed beneficial to forced creative thinking, yet it was also regarded as a limitation that can be improved through creative flexibility. Other factors that emerged were organisational socialisation, aligned stakeholder communication, sufficient market research, and respecting the emergence of seasonal trends. Future research should explore ways of improving organisational components that act as external influences on individual creative thinking. Moreover, future research should explore how effective training can help stakeholders learn and acquire the rights needs together with the continuous support from the business
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A video-based automated recommender (VAR) system for garments
In this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppersâ preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopperâs behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers.
Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0984.The authors thank the participants in presentations given by the authors in College of Business at City Univeristy of HongKong and Cambridge Judge Business School for their feedback, as well as the Editor, the Area Editor, and two anonymous Marketing Science reviewers for their insightful comments. This research was supported by two National Natural Science Foundation of China Fund (Grants 71232008 & 71502039), and the Institute for Sustainable Innovation and Growth (iSIG) at School of Management, Fudan University
Retail managersâ preparedness to capture customersâ emotions: a new synergistic framework to exploit unstructured data with new analytics
Although emotions have been investigated within strategic management literature from an internal perspective, managersâ ability and willingness to understand consumersâ emotions, with emphasis on the retail sector, is still a scarcely explored theme in management research. The aim of this paper is to explore the match between the supply of new analytical tools and retail managersâ attitudes towards new tools to capture customersâ emotions. To this end, Study 1 uses machine learning algorithms to develop a new system to analytically detect emotional responses from customersâ static images (considering the exemplar emotions of happiness and sadness), whilst Study 2 consults management decision-makers to explore the practical utility of such emotion recognition systems, finding a likely demand for a number of applications, albeit tempered by concern for ethical issues. While contributing to the retail management literature with regard to customersâ emotions and big data analytics, the findings also provide a new framework to support retail managers in using new analytics to survive and thrive in difficult times
Conditions for effective smart learning environments
Reference: Koper, E.J.R. (2014) Conditions for effective smart learning environments. Smart Learning Environments,1(5), 1-17.
http://www.slejournal.com/content/1/1/5/abstract
doi:10.1186/s40561-014-0005-4Smart learning environments (SLEs) are defined in this paper as physical
environments that are enriched with digital, context-aware and adaptive devices, to
promote better and faster learning. In order to identify the requirements for âbetter
and faster learningâ, the idea of Human Learning Interfaces (HLI) is presented, i.e. the
set of learning related interaction mechanisms that humans expose to the outside
world that can be used to control, stimulate and facilitate their learning processes. It
is assumed that humans have and use these HLIs for all types of learning, and that
others, such as parents, teachers, friends, and digital devices can interact with the
interface to help a person to learn something. Three basic HLIs are identified that
represent three distinct types of learning: learning to deal with new situations
(identification), learning to behave in a social group (socialization) and learning by
creating something (creation). These three HLIs involve a change in cognitive
representations and behavior. Performance can be increased using the practice HLI,
and meta-cognitive development is supported by the reflection HLI. This analysis of
HLIs is used to identify the conditions for the development of effective smart learning
environments and a research agenda for SLEs
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