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Purpose: To outline recent developments in digital service delivery in order to encourage researchers to pursue collaborations with computer science, operations research, and data science colleagues and to show how such collaborations can expand the scope of research on emotion in service delivery.

Design/methodology/approach: Uses archived resources available at http://LivePerson.com to extract data based in genuine service conversations between agents and customers. We refer to these as “digital traces” and analyze them using computational science models.

Findings: Although we do not test significance or causality, the data presented in this chapter provide a unique lens into the dynamics of emotions in service; results that are not obtainable using traditional research methods.

Research limitations/implications: This is a descriptive study where findings unravel new dynamics that should be followed up with more research, both research using traditional experimental methods, and digital traces research that allows inferences of causality.

Practical implications: The digital data and newly developed tools for sentiment analyses allow exploration of emotions in large samples of genuine customer service interactions. The research provides objective, unobtrusive views of customer emotions that draw directly from customer expressions, with no self-report intervention and biases.

Originality/value: This is the first objective and detailed depiction of the actual emotional encounters that customers express, and the first to analyze in detail the nature and content of customer service work.

This paper proposes a research method for analysing talk about accounting concepts, systems and numbers. The authors argue that studying accounting talk in situ is a fruitful way to understand both the role accounting plays in the framing of relationships between individuals and the associated emotional content of these exchanges. As such, the authors argue that conversation analysis (CA) is a useful complement to interviews in qualitative research.

The authors introduce a specific approach to CA called positioning theory, which captures the linguistic and emotional subtleties embedded within interpersonal interactions, and the way accounting impacts and mediates these relations through measuring, assessment and control. The authors draw on one particularly animated conversation about accounting in a manufacturing company. The conversation was a largely emotional and animated exchange between individuals where talk about accounting was imbued with metaphors, violence, sex and humour.

While participants in conversations may appear to draw on similar forms of language and expression, CA allows researchers to see that the meaning of these shared expressions change based on who is saying them, whom they are saying them to and how they are saying them. Dissecting conversations as they unfold, offers a more nuanced and multifaceted understanding of accounting as central to the social fabric of organisational life.

As opposed to interviews, which often suffer from the rationality of hindsight (referred to as retrospective rationality), CA captures the unfolding nature of accounting talk in real-time–not upon reflection.

Although emotions are relevant for conflicted interactions, the role of emotions in organizational conflicts has remained understudied. The purpose of this paper is to contribute to this by looking at the role of nonverbal affective elements in conversations.

Bringing together organizational “becoming” and embodiment approaches, the study focused on a conflict which emerged during a multi-actor consulting conversation. The episode in question was analyzed via a detailed, micro-level discursive method which focused specifically on the participants’ use of prosodic and nonverbal behaviors.

Changes in prosody were found to have an important role in how the conflict between a consultant and an employee client emerged and was handled. Nonverbal and prosodic means had a central role in creating legitimate space for the employees’ feelings: they helped to validate the feelings and thus led the interlocutors to act in a more constructive manner in their handling of the conflicted situation.

Findings are based on a single case study. Multi-modal analysis proved effective in capturing the relevant interactions in a comprehensive manner.

Conversational “traps” may be observed by becoming alert to interactional patterns involving repeated chains of actions. A nonverbal response, validating the interlocutor as someone who is entitled to her/his feelings, can be sufficient in providing emotional help in consultancy.

Nonverbal elements of interactions are important in handling delicate issues in conflicts.

To the authors’ knowledge, no previous organizational research has provided a detailed description of a conflicted interaction “as it happened” between clients and a consultant.

In this paper, we offer a model of how leaders and managers can create energy for change by influencing patterns of conversation across the organization. We develop the model by linking social constructionist thought with theory from the field of positive psychology. We propose that effective leaders work with others to co-author persuasive narratives of change that generate energy by providing people (including themselves) with a sense of autonomy, competence, and relatedness. Energy is expressed in the form of support, time, money, and resources, which contribute to the success of the work. Continuous attention to crafting persuasive narratives in a collaborative way creates upward spirals of energy, and increases the probability of successful change over time. We illustrate these ideas with a case study of a successful IT change initiative in a Fortune 100 insurance company, and conclude by discussing implications for research and practice.

Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization.

This paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication.

The conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has.

The article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.

The purpose of this paper is to analyse the role of emotions in fieldwork by applying grounded theory (GT). Although analytical guidelines in GT are well documented, the implications of researcher emotions have received much less attention.

This paper provides an “insider” account of the author's experience collecting field data during six months in a department of a Fortune 500 company.

It is argued that methodological emotional reflexivity (MER) as a part of doing GT will increase both awareness and understanding about how emotions influence the research process. MER comprises emotional awareness, empathic understanding and emotions in decision making.

The paper proposes acknowledging that emotions are part of, connected to, and both influence and are influenced by research decisions within GT. It is suggested that MER becomes an integral part of memoing in GT. Memos provides a link between data and evolving insights, and is considered fundamental in GT. The inclusion of MER provides a more transparent and adaptive GT approach.

The goal of this study is to examine how tweets containing distinct emotions (i.e., emotional tweets) and different information types (i.e., misinformation, corrective information, and others) are prevalent during the initial phase of mass shootings and furthermore, how users engage in those tweets.

The researchers manually coded 1,478 tweets posted between August 3–11, 2019, in the immediate aftermath of the El Paso and Dayton mass shootings. This manual coding approach systematically examined the distinct emotions and information types of each tweet.

The authors found that, on Twitter, misinformation was more prevalent than correction during crises and a large portion of misinformation had negative emotions (i.e., anger, sadness, and anxiety), while correction featured anger. Notably, sadness-exhibiting tweets were more likely to be retweeted and liked by users, but tweets containing other emotions (i.e., anger, anxiety, and joy) were less likely to be retweeted and liked.

Only a portion of the larger conversation was manually coded. However, the current study provides an overall picture of how tweets are circulated during crises in terms of misinformation and correction, and moreover, how emotions and information types alike influence engagement behaviors.

The pervasive anger-laden tweets about mass shooting incidents might contribute to hostile narratives and eventually reignite political polarization. The notable presence of anger in correction tweets further suggests that those who are trying to provide correction to misinformation also rely on emotion. Moreover, our study suggests that displays of sadness could function in a way that leads individuals to rely on false claims as a coping strategy to counteract uncertainty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2021-0121/

This study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.

Participants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.

Facebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.

Digital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.

Purpose – This chapter represents a dynamic cycle in a collaborative inquiry conceived some six years ago. The aim of this study is to share some of our reflections, tensions, questions and uncertainties in positioning our own emotional responses as legitimate research data.

Methodology/Approach – We adopted a collaborative second-person methodology within an action research framework in the process of inquiring into our own practice as systemic psychotherapists and women.

Findings – We offer reflections on the positioning of emotion as researchers, tutors and psychotherapists. We discuss three themes from the emotional landscape of the inquiry, research process, research product and gendered voices, in anticipation that they will connect with and be useful to other researchers.

Originality/Value – The chapter introduces our sense-making framework for reflexively exploring the salience of emotion in research. It argues that attenuating, listening and responding to the emotions we feel as researchers both serves as a guide to inquiring into critical social constructs and engenders opportunities to promote social change.