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The purpose of this study is to explore teen-adult dialogic interactions during the co-design of data literacy activities in order to determine the nature of teen thinking, their emotions, level of engagement, and the power of relationships between teens and adults in the context of data literacy. This study conceives of co-design as a learning space for data literacy. It investigates the teen–adult dialogic interactions and what these interactions say about the nature of teen thinking, their emotions, level of engagement and the power relationships between teens and adults.

The study conceives of co-design as a learning space for teens. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity, and Emotional Tone using transcriptions of recorded Data Labs with teens and adults. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity and Emotional Tone using transcriptions of recorded Data Labs with teens and adults.

LIWC-22 scores on the linguistic measures Analytic Thinking, Clout, Authenticity and Emotional Tone indicate that teens had a high level of friendly engagement, a relatively low sense of power compared with the adult co-designers, medium levels of spontaneity and honesty and the prevalence of positive emotions during the co-design sessions.

This study provides a concrete example of how to apply NLP in the context of data literacy in the public library, mapping the LIWC-22 findings to STEM-focused informal learning. It adds to the understanding of assessment/measurement tools and methods for designing data literacy education, stimulating further research and discussion on the ways to empower youth to engage more actively in informal learning about data.

This study applies a novel approach for exploring teen engagement within a co-design project tasked with the creation of youth-oriented data literacy activities.

This study aims to describe and analyze the differences in the linguistic styles of honest and dishonest stories.

This paper uses a descriptive study with a multivariate analysis of linguistic categories according to the story. The research analyzed 37 honest stories and 15 dishonest stories produced during actual legal proceedings through software Linguistic Inquiry and Word Count (LIWC).

The authors find that individuals who engage in deception use a different number of words when they narrate facts. The results suggest a need for additional investigation of the linguistic style approach because of its high applicability and detection accuracy. This approach should be complemented by other types of verbal, nonverbal and psychophysiological deception detection techniques.

Among the limitations, the authors consider length of the stories should be considered and scarce scientific literature in Spanish to compare with outcomes in English.

This research highlights the relevance to include linguistic style in real contexts to differentiate honest and dishonest stories due to objectivity and agility to implement.

Understanding deception as a social behaviour and its psychological processes associated are elements that contribute to people and justice to comprehend it.

Analyzing real statements and discriminate differences in linguistic style, contribute to understand deeply this important behaviour to propose new methodologies and theories to explain it.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

Online reviews are regarded as a source of information for decision-making because of the abundance and ready availability of information. Whereas, the sheer volume of online reviews makes it hard for consumers, especially the older adults who perceive more difficulties in reading reviews and obtaining information compared to younger adults, to locate the useful ones. The main objective of this study is to propose an effective method to locate valuable reviews of mobile phones for older adults. Besides, the authors also want to explore what characteristics of the technology older adults prefer. This will benefit both e-retailers and e-commerce platforms.

After collecting online reviews related to mobile phones designed for older adults from a popular Chinese e-commerce platform (JD Mall), topic modeling, term frequency-inverse document frequency (TF-IDF), and linguistic inquiry and word count (LIWC) methods were applied to extract latent topics and uncover potential dimensions that consumers frequently referred to in their reviews. According to consumers' attitudes towards different popular topics, seven machine learning models were employed to predict the usefulness and popularity of online reviews due to their excellent performance in prediction. To improve the performance, a weighted model based on the two best-performing models was built and evaluated.

Based on the TF-IDF, topic modeling, and LIWC methods, the authors find that older adults are more interested in the exterior, sound, and communication functions of mobile phones. Besides, the weighted model (Random Forest: Decision Tree = 2:1) is the best model for predicting the online review popularity, while random forest performs best in predicting the perceived usefulness of online reviews.

This study’s findings can help e-commerce platforms and merchants identify the needs of the targeted consumers, predict reviews that will get more attention, and provide some early responses to some questions.

The results propose that older adults pay more attention to the mobile phones' exterior, sound, and communication function, guiding future research. Besides, this paper also enriches the current studies related to making predictions based on the information contained in the online reviews.

Today, individuals use social media to express their opinions and feelings, which offers a living laboratory to researchers in various fields, such as management, innovation, technology development, environment and marketing. It is therefore necessary to understand how the language used in user-generated content and the emotions conveyed by the content affect responses from other social media users.

In this study, almost 700,000 posts from Twitter (as well as Facebook, Instagram and forums in the appendix) are used to test a conceptual model grounded in signaling theory to explain how the language of user-generated content on social media influences how other users respond to that communication.

Extending developments in linguistics, this study shows that users react negatively to content that uses self-inclusive language. This study also shows how emotional content characteristics moderate this relationship. The additional information provided indicates that while most of the findings are replicated, some results differ across social media platforms, which deserves users' attention.

This article extends research on Internet behavior and social media use by providing insights into how the relationship between self-inclusive language and emotions affects user responses to user-generated content. Furthermore, this study provides actionable guidance for researchers interested in capturing phenomena through the social media landscape.

This study aims to understand the extent of privacy concerns regarding voice-activated personal assistants (VAPAs) on Twitter. It investigates three key areas: (1) the effect of privacy-related press coverage on public sentiment and discussion volume; (2) the comparative negativity of privacy-focused conversations versus general conversations; and (3) the specific privacy-related topics that arise most frequently and their impact on sentiment and discussion volume.

A dataset of 441,427 tweets mentioning Amazon Alexa, Google Assistant, and Apple Siri from July 1, 2019 to June 30, 2021 were collected. Privacy-related press coverage has also been monitored. Sentiment analysis was conducted using the dictionary-based software LIWC and VADER, whereas text mining packages in R were used to identify privacy-related issues.

Negative privacy-related news significantly increases both negativity and volume in Twitter conversations, whereas positive news only boosts volume. Privacy-related tweets were notably more negative than general tweets. Specific keywords were found to either increase or decrease the sentiment and discussion volume. Additionally, a temporal evolution in sentiment, with general attitudes toward VAPAs becoming more positive, but privacy-specific discussions becoming more negative was observed.

This research augments the existing online privacy literature by employing text mining methodologies to gauge consumer sentiments regarding privacy concerns linked to VAPAs, a topic currently underexplored. Furthermore, this research uniquely integrates established theories from privacy calculus and social contract theory to deepen our analysis.

One of the adverse effects of COVID-19 is on poor economic and financial performance. Such economic underperformance, less demand from the consumer side and supply chain disruption is leading to stock market volatility. In such a backdrop, this paper aims to find the impact of COVID-19 on the Indian stock market by analyzing the analyst’s report.

The sample includes a cross-sectional data set on selected Indian firms that are indexed in BSE 100. The authors calculate the score of disclosure tone by using a textual analysis tool based on the analyst report of selected BSE 100 firms' approach in tackling COVID-19’s impact. The relationship between the tone of the analyst report and stock market performance is examined. This empirical model also survives robustness analysis to establish the consistency of the findings. This study uses both frequentist statistics and Bayesian statistics approach.

The empirical result shows that tone has negative and significant influence on stock market performance. This study indicates that either analysts are not providing value-relevant and incremental information, which can reduce the stock market volatility during this pandemic situation or investors are not able to recognize the optimism of the information.

This study provides an interesting insight regarding retail investors' stock purchasing behavior during the crisis period. Hence, this study also lays out crucial managerial implications that can be followed by preparers while preparing corporate disclosure.

In the concern on pandemic and its impact on the stock market, this study sheds light on investors' preferences during the crisis period. This study uniquely focuses on analyst reports and investors' preference which has not been studied widely. To the best of the authors’ knowledge, this is the first study in the Indian context, which aims to understand retail investors’ investment preferences during a pandemic.

This study aims to help uncover corporate culture and values to attract and retain talent by understanding job reviews written by business-to-business (B2B) salespeople.

Over 40,000 job reviews on Glassdoor.com are analyzed by a dictionary-based content analysis tool, Linguistic Inquiry and Word Count (LIWC2015), to explore the links between corporate culture and linguistics characteristics of reviews as articulated by B2B salespeople. This study adopted a multidimensional scaling approach based on the nine cultural value scores to create a map of corporate profiles. A projection of the LIWC2015 scores on this map uncovers differences in language patterns and emotions expressed across the profiles.

Findings reveal a map of corporate profiles with two dimensions, namely, product-centricity and customer-centricity, that divide salesforce subculture into a 2 × 2 matrix of four types: Empathic Innovators, Product Pioneers, Customer Champions and Commodity Traders.

This study combined two data sets, scores on CultureX’s nine cultural values (agility, collaboration, customer orientation, diversity, execution, innovation, integrity, performance and respect) and job reviews on Glassdoor.com. This research seeks to develop profiles of the organizational culture and to use a blend of qualitative and quantitative methods. This study adds to the literature on salesforce subculture and showcases a solution to the methodological difficulty in categorizing and measuring culture.

The worldwide imposition of lockdown measures to control the 2020 coronavirus disease 2019 (COVID-19) outbreak has shifted most executive communications with external stakeholders online, resulting in quick responses from stakeholders. This study aims to understand how presentational styles exhibited in online communication induce immediate audience responses and empirically test the effectiveness of reactive impression management tactics.

The authors analyze presentational styles using MP3 files containing executive utterances during earnings call conferences held by S&P 100-listed firms after June 2020, the quarter after the World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. Using timestamps, the authors link each utterance to a 1-minute interval change in the ask/bid prices of the stocks that occurs a minute after the corresponding utterance begins.

Exhibiting an informational presentation style in earnings calls leads to positive and immediate audience responses. Managers tend to increase their reliance on promotional presentation styles rather than on informational ones when quarterly earnings exceed market forecasts.

Drawing on organizational genre theory, this research identifies the discrepancy between the presentation styles that audiences positively respond to and those that managers tend to exhibit in earnings calls and provides a reactive impression management typology for immediate responses from online audiences.