Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Artificial intelligence (AI)-enabled voice assistants (VAs) such as Amazon Alexa increasingly assist shopping decisions and exhibit empathic behavior. The advancement of empathic AI raises concerns about machines nudging consumers into purchasing undesired or unnecessary products. Yet, it is unclear how the machine’s empathic behavior affects consumer responses and decision-making outcomes during voice-enabled shopping. This article draws from the service robot acceptance model (sRAM) and social response theory (SRT) and presents an individual-session experiment where families (vs. individuals) complete actual shopping tasks using an ad-hoc Alexa app featuring high (vs. standard) empathic capabilities. We apply the experimental conditions as moderators to the structural model, bridging selected functional, social-emotional, and relational variables. Our framework collocates affective empathy, explicates the bases of consumers’ beliefs, and predicts behavioral outcomes. Findings demonstrate (i) an increase in consumers’ perceptions, beliefs, and adoption intentions with empathic Alexa, (ii) a positive response to empathic Alexa holding constant in family settings, and (iii) an interaction effect only on the functional model dimensions whereby families show greater responses to empathic Alexa while individuals to standard Alexa

LANGUAGE STRATEGIES IN INTERNATIONAL BUSINESS: NEW PROSPECTS FOR NEGOTIATION AND CONFLICT MANAGEMENT

With the COVID-19 pandemic of 2020 — when negotiations have been almost exclusively carried out in online settings — there is a growing need for research which addresses this new norm. This dissertation explores how linguistic cues can corroborate or challenge the established measures in negotiation and conflict management research. The overarching objective is to study the interdependence of language and culture in the presence of technology within the domain of international negotiations and conflict resolution. The first essay of the dissertation addresses the anomalies regarding the use of the two major negotiation strategies identified by prior research – questions and answers (Q&A) and substantiation and offers (S&O) – and their effectiveness across cultures. I triangulate between cognitive methods utilized in negotiations research (mental model convergence, fixed-pie bias), linguistic cues (words with positive and negative connotations), and language style matching (LSM), a novel analysis in international buyer-seller negotiations. Based on an online negotiation simulation between representatives of a high-context (Hong Kong Chinese) and low-context (U.S.) communication culture (total sample size is 300) and subsequent linguistic analysis of the transcripts, the essay questions the notion of normative strategy; shows the conditions when the strategies have an integrative versus distributive character; identifies cognitive mechanisms which explain why S&O might be more beneficial than Q&A in a high-context communication culture; and clarifies in which cultural contexts the index of language style matching reflects a deeper, cognitive simmilarity and in which an automatic process. The second essay is a systematic literature review of studies about language in international conflict management research. The essay emphasizes a positive potential of a conflict and suggests how it can be achieved linguistically in an intercultural environment. It shows how language can give a dynamic process to conflict management. Unlike the static view of conflict, the proposed theoretical framework underscores the importance of poly-contextual behavior, i.e., how the behavior changes across contexts. By focusing on the multilingualism, the essay further disentangles language and culture, which are often mixed together. The essay suggests short term and long term strategies for a dynamic conflict de-escalation in the domain of international business

Towards Video Transformers for Automatic Human Analysis

[eng] With the aim of creating artificial systems capable of mirroring the nuanced understanding and interpretative powers inherent to human cognition, this thesis embarks on an exploration of the intersection between human analysis and Video Transformers. The objective is to harness the potential of Transformers, a promising architectural paradigm, to comprehend the intricacies of human interaction, thus paving the way for the development of empathetic and context-aware intelligent systems. In order to do so, we explore the whole Computer Vision pipeline, from data gathering, to deeply analyzing recent developments, through model design and experimentation. Central to this study is the creation of UDIVA, an expansive multi-modal, multi-view dataset capturing dyadic face-to-face human interactions. Comprising 147 participants across 188 sessions, UDIVA integrates audio-visual recordings, heart-rate measurements, personality assessments, socio- demographic metadata, and conversational transcripts, establishing itself as the largest dataset for dyadic human interaction analysis up to this date. This dataset provides a rich context for probing the capabilities of Transformers within complex environments. In order to validate its utility, as well as to elucidate Transformers' ability to assimilate diverse contextual cues, we focus on addressing the challenge of personality regression within interaction scenarios. We first adapt an existing Video Transformer to handle multiple contextual sources and conduct rigorous experimentation. We empirically observe a progressive enhancement in model performance as more context is added, reinforcing the potential of Transformers to decode intricate human dynamics. Building upon these findings, the Dyadformer emerges as a novel architecture, adept at long-range modeling of dyadic interactions. By jointly modeling both participants in the interaction, as well as embedding multi- modal integration into the model itself, the Dyadformer surpasses the baseline and other concurrent approaches, underscoring Transformers' aptitude in deciphering multifaceted, noisy, and challenging tasks such as the analysis of human personality in interaction. Nonetheless, these experiments unveil the ubiquitous challenges when training Transformers, particularly in managing overfitting due to their demand for extensive datasets. Consequently, we conclude this thesis with a comprehensive investigation into Video Transformers, analyzing topics ranging from architectural designs and training strategies, to input embedding and tokenization, traversing through multi-modality and specific applications. Across these, we highlight trends which optimally harness spatio-temporal representations that handle video redundancy and high dimensionality. A culminating performance comparison is conducted in the realm of video action classification, spotlighting strategies that exhibit superior efficacy, even compared to traditional CNN-based methods.[cat] Aquesta tesi busca crear sistemes artificials que reflecteixin les habilitats de comprensió i interpretació humanes a través de l'ús de Transformers per a vídeo. L'objectiu és utilitzar aquestes arquitectures per comprendre millor la interacció humana i desenvolupar sistemes intel·ligents i conscients de l'entorn. Això implica explorar àmplies àrees de la Visió per Computador, des de la recopilació de dades fins a l'anàlisi de l'estat de l'art i la prova experimental d'aquests models. Una part essencial d'aquest estudi és la creació d'UDIVA, un ampli conjunt de dades multimodal i multivista que enregistra interaccions humanes cara a cara. Amb 147 participants i 188 sessions, UDIVA inclou contingut audiovisual, freqüència cardíaca, perfils de personalitat, dades sociodemogràfiques i transcripcions de les converses. És el conjunt de dades més gran conegut per a l'anàlisi de la interacció humana diàdica i proporciona un context ric per a l'estudi de les capacitats dels Transformers en entorns complexos. Per tal de validar la seva utilitat i les habilitats dels Transformers, ens centrem en la regressió de la personalitat. Inicialment, adaptem un Transformer de vídeo per integrar diverses fonts de context. Mitjançant experiments exhaustius, observem millores progressives en els resultats amb la inclusió de més context, confirmant la capacitat dels Transformers. Motivats per aquests resultats, desenvolupem el Dyadformer, una arquitectura per interaccions diàdiques de llarga duració. Aquesta nova arquitectura considera simultàniament els dos participants en la interacció i incorpora la multimodalitat en un sol model. El Dyadformer supera la nostra proposta inicial i altres treballs similars, destacant la capacitat dels Transformers per abordar tasques complexes. No obstant això, aquestos experiments revelen reptes d'entrenament dels Transformers, com el sobreajustament, per la seva necessitat de grans conjunts de dades. La tesi conclou amb una anàlisi profunda dels Transformers per a vídeo, incloent dissenys arquitectònics, estratègies d'entrenament, preprocessament de vídeos, tokenització i multimodalitat. S'identifiquen tendències per gestionar la redundància i alta dimensionalitat de vídeos i es realitza una comparació de rendiment en la classificació d'accions a vídeo, destacant estratègies d'eficàcia superior als mètodes tradicionals basats en convolucions

Politeness in contemporary Chinese: a postmodernist analysis of generational variation in the use of compliments and compliment responses

There is some evidence from scholarship that politeness norms in China are diversified. I maintain that a study aiming to provide systematic evidence of this would require an approach to politeness phenomena that is able to address such diversity. Drawing upon the insights of recent scholarship on the distinction between the modernist and postmodernist approaches to politeness, I survey relevant literature. I show that many current works on politeness argue that the modernist approach (Lakoff 1973/1975, Brown and Levinson 1987[1978], Leech 1983) generally tends to assume that society is relatively homogeneous with regard to politeness norms. By contrast, I demonstrate that the postmodernist approach to politeness (e.g. Eelen 2001, Mills 2003, Watts 2003) foregrounds the heterogeneity of society and the rich variability of politeness norms within a given culture. I argue that, by using a postmodernist approach to politeness, it is possible to show evidence of differences between groups of the Chinese in their politeness behaviour and the informing norms of politeness. I then explore this issue in depth by focusing on compliments and compliment responses (CRs). I show that studies on these speech acts in Chinese have to date tended to adopt a modernist approach to politeness and often assume a compliment and a CR to be easily identifiable. Moreover, I show that they do not address the heterogeneity of Chinese society and generally assume interactants to be homogeneous in terms of politeness norms that inform compliment and CR behaviours. On this basis, I raise the questions as to whether, by adopting a postmodernist rather than modernist approach, there is empirical evidence that politeness norms informing compliments and CRs vary among the Chinese, and whether these norms correlate with generation. v To this end, by audio-recording both spontaneous naturally occurring conversations and follow-up interviews, I construct a corpus of compliments and CRs generated by two generations of the Chinese brought up before and after the launch of China’s reform. Quantitative and qualitative analyses of these data show that there is variation in compliment and CR behaviours in Chinese and the informing politeness norms. Furthermore, the result shows that this variation is correlated with generation. I then show how, by using a research methodology which emphasizes the interactants’ perceptions obtained through follow-up interviews, my study brings to light problems with previous studies on compliments and CRs which hitherto are not addressed. By showing evidence that compliments and CRs are not as easy to identify as many previous researchers have indicated. I argue that my emic approach to data analysis provides a useful perspective on the complexity of intention in studies on speech acts and perhaps beyond. My study, therefore, makes an interesting contribution to the debate over this notion central to politeness research. Moreover, I argue my methodology which is able to categorize and analyze data according to participants’ self-reported perceptions allows me to draw out differences in the two generations’ compliment and CR behaviours and the informing politeness norms

Human-Machine Communication: Complete Volume. Volume 1

This is the complete volume of HMC Volume 1

The SSPNet-Mobile Corpus: from the detection of non-verbal cues to the inference of social behaviour during mobile phone conversations

Mobile phones are one of the main channels of communication in contemporary society. However, the effect of the mobile phone on both the process of and, also, the non-verbal behaviours used during conversations mediated by this technology, remain poorly understood. This thesis aims to investigate the role of the phone on the negotiation process as well as, the automatic analysis of non-verbal behavioural cues during conversations using mobile telephones, by following the Social Signal Processing approach. The work in this thesis includes the collection of a corpus of 60 mobile phone conversations involving 120 subjects, development of methods for the detection of non-verbal behavioural events (laughter, fillers, speech and silence) and the inference of characteristics influencing social interactions (personality traits and conflict handling style) from speech and movements while using the mobile telephone, as well as the analysis of several factors that influence the outcome of decision-making processes while using mobile phones (gender, age, personality, conflict handling style and caller versus receiver role). The findings show that it is possible to recognise behavioural events at levels well above chance level, by employing statistical language models, and that personality traits and conflict handling styles can be partially recognised. Among the factors analysed, participant role (caller versus receiver) was the most important in determining the outcome of negotiation processes in the case of disagreement between parties. Finally, the corpus collected for the experiments (the SSPNet-Mobile Corpus) has been used in an international benchmarking campaign and constitutes a valuable resource for future research in Social Signal Processing and more generally in the area of human-human communication