Francesc Busquet I Segui

Voice-based interfaces provide new opportunities for firms to interact with consumers along the customer journey. The current work demonstrates across four studies that voice-based (as opposed to text-based) interfaces promote more flow-like user experiences, resulting in more positively-valenced service experiences, and ultimately more favorable behavioral firm outcomes (i.e., contract renewal, conversion rates, and consumer sentiment). Moreover, we also provide evidence for two important boundary conditions that reduce such flow-like user experiences in voice-based interfaces (i.e., semantic disfluency and the amount of conversational turns). The findings of this research highlight how fundamental theories of human communication can be harnessed to create more experiential service experiences with positive downstream consequences for consumers and firms. These findings have important practical implications for firms that aim at leveraging the potential of voice-based interfaces to improve consumers' service experiences and the theory-driven ''conversational design'' of voice-based interfaces.

User-generated content, such as product reviews, offer one of the most accessible and abundant information sources to firms. Coupled with recent technological advances in natural language processing these review postings provide opportunities for marketing managers to gather market intelligence in the form of consumer opinions and competitive information. One of the most prevalent analysis methods to determine consumer sentiment on a given topic is sentiment analysis (Karlgren et al. 2012), which uses a lexicon-based approach to classify different terms within the review text based on their polarity (i.e. as either positive, negative, or neutral) or by using a machine learning-based approach in which a model is used to assess the sentiment of a text, resulting, in both cases, a contextual polarity score for the review text. However, this approach provides very little insight into the underlying factors influencing the review sentiment, thus limiting its applicability to future marketing and product innovation decisions. Recently, researchers have started investigating deeper forms of sentiment analysis aimed at increasing interpretability by including additional factors to qualify the syntax of the reviews (e.g. readability) or by conducting more fine-grained sentiment analysis on sub-sentences or aspects within the review text (Thet, Na, and Khoo 2010). Consider for example the following review text: “Phone battery and fingerprint reader are great, the problem I have with this phone is how slow it is. The phone's touch screen is horrible.”. In this exemplary review, some product aspects are positively evaluated by the consumer while other aspects are negatively evaluated. Yet, traditional sentiment analysis would score the overall sentiment for this text as neutral, thus providing very little insight on how consumers evaluate specific product features. The current work provides a novel methodological approach to conduct aspect-based sentiment analysis in order to identify the sentiment linked to different product features mentioned in a text. We leverage traditional text analysis along with prior knowledge to identify the most representative product features, and introduce a reusable python script to easily conduct this analysis. To illustrate our approach, we scraped 1,147 consumer reviews on the Samsung Galaxy A10s from a major online review site (amazon.com). Leveraging LDA topic modelling coupled with previous knowledge about smartphones, we identified ten relevant aspects of the phone (e.g. software, shape, price, battery life). Next, we extract all nouns from the review text using part-of-speech tagging and determine which nouns in the review texts relate to each of the ten aspects. We do this by converting words and aspects into vector representations and then calculating the cosine similarity between them. To assign a sentiment score to each noun we separate text sentences and compute the sentiment for each sentence in which one of those relevant nouns is found, taking adjectives as sentiment indicators and adverbs as multipliers. Finally, we aggregate the sentiment for each noun belonging to a specific aspect, thus allowing us to calculate a sentiment score for each product aspect. As shown in Figure 1, such an aspect-based sentiment analysis provides a much deeper insight into consumers’ likes and dislikes of a specific product. Unlike traditional sentiment analysis, the proposed, multi-method approach provides far reaching practical applications for marketing managers and product developers, opening up new opportunities to leverage unstructured, user-generated text to gain insight into feature-level product performance and identify consumer unmet needs.

Aspect-based sentiment analysis provides a more fine-grained view of sentiment in comparison to classical sentiment analysis approaches, by obtaining the sentiment of different aspects or features of a product. In this paper, we show how it can be used to assess consumer perceptions of different product features and, thus, define opportunities for innovation.

Recent advances in artificial intelligence and natural language processing are gradually transforming how humans search, shop, and express their preferences. Leveraging the new affordances and modalities of human-machine interaction through voice-controlled interfaces will require a nuanced understanding of the physics and psychology of speech formation as well as the systematic extraction and analysis of vocal features from the human voice. In this paper, we first develop a conceptual framework linking vocal features in the human voice to experiential outcomes and emotional states. We then illustrate the effective processing, editing, analysis, and visualization of voice data based on an Amazon Alexa user interaction, utilizing state-of-the-art signal-processing packages in R. The current research offers novel insight into the ways in which future marketing scholars might employ voice and sound analytics moving forward, including a discussion of the ethical implications of building multi-modal databases for business and society. Finally, we present the MAFiA R package, a novel R package developed at our lab to automate the voice analytics process, especially suitable for social scientists and experimental researchers.

The ubiquitous availability of image data, advances in cloud-computing, and recent developments in classification algorithms gave rise to a new class of automated emotion detection systems which claim to perform accurate emotion detection from faces at scale. In this research, we provide a tightly controlled validation study using pretrained emotion detection algorithms of the Google ML, Microsoft Cognitive Service, GfK EmoScan, and other platforms to test the robustness and consistency across and within current emotion detection systems. Our results demonstrate considerable variability in predictive validity across emotion detection systems, high variability across different types of discrete emotions with strong positive emotions (such as an open mouth smile) being easier to classify compared to negative emotions such as anger or fear, and we detect sizable positive correlations of theoretically opposite emotions (such as surprise and fear). We provide two modeling strategies to improve prediction accuracy by either combining feature sets or by averaging across emotion detection systems using ensemble methods.