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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.

Distinguishing phishing emails from legitimate emails continues to be a difficult task for most individuals. This study aims to investigate the psycholinguistic factors associated with deception in phishing email text and their effect on end-user ability to discriminate phishing emails from legitimate emails.

Email messages and end-user decisions collected from a laboratory phishing study were validated and analyzed using natural language processing methods (Linguistic Inquiry Word Count) and penalized regression models (LASSO and Elastic Net) to determine the linguistic dimensions that attackers may use in phishing emails to deceive end-users and measure the impact of such choices on end-user susceptibility to phishing.

We found that most participants, who played the role of a phisher in the study, chose to deceive their end-user targets by pretending to be a familiar individual and presenting time pressure or deadlines. Results show that use of words conveying certainty (e.g. always, never) and work-related features in the phishing messages predicted higher end-user vulnerability. On the contrary, use of words that convey achievement (e.g. earn, win) or reward (cash, money) in the phishing messages predicted lower end-user vulnerability because such features are usually observed in scam-like messages.

Insights from this research show that analyzing emails for psycholinguistic features associated with computer-mediated deception could be used to fine-tune and improve spam and phishing detection technologies. This research also informs the kinds of phishing attacks that must be prioritized in antiphishing training programs.

Applying natural language processing and statistical modeling methods to analyze results from a laboratory phishing experiment to understand deception from both attacker and end-user is novel. Furthermore, results from this work advance our understanding of the linguistic factors associated with deception in phishing email text and its impact on end-user susceptibility.

As a business executive and philanthropist, Mary Kay Ash is legendary as a glass-ceiling breaker. With the belief that Mary Kay Ash is both modern and relevant, while simultaneously legendary, the overall purpose of this paper is to explore the role of Mary Kay Ash as an influential entrepreneur. This research responds to the call by Cogliser and Brigham (2004) for an increased understanding of how entrepreneurial leaders influence, challenge, inspire and develop followers.

Following on research by Hoppe (2013), this objective was accomplished via a pentadic analysis of Mary Kay Ash’s rhetoric aimed to influence the mental mindset of readers (followers) over the course of generations. Burke’s pentad was the sense-making tool used for examining Ash’s rhetoric of influence as an entrepreneurial leader. The data used in the pentadic analysis were also analyzed via Linguistic Inquiry and Word Count (LIWC) and IBM Watson Emotion Analysis to see where analyses might converge or diverge.

Based on the analysis of her written work, Mary Kay Ash resided at the intersection of leadership and entrepreneurship and, in so doing, was an influencer. Her primary rhetorical approach to influencing was idealism. Interwoven in her writings, she also exhibited both pragmatism and realism. She knew that she had to start the business to have the future she desired and that she needed to train her team appropriately for success to be forthcoming. The motivation in Mary Kay Ash’s rhetoric was that of influencing people so they would be the best that they could be.

Qualitative research brings with it an array of inevitable research problems. Pentadic analysis cannot be judged by the basic objective standards of reliability and validity because objective reality does not exist in personal interpretation. That is, one person as a critic cannot be impartial because the interpretation is only one personal way of viewing the data and another critic might view the same pentads and come up with different ratios. With this subjectivity in mind, however, the data used in the pentadic analysis were also analyzed via LIWC and IBM Watson Emotion Analysis to see where analyses might converge or diverge.

The findings from this research denote clearly that Mary Kay Ash was a forerunner of the modern day influencer. As a primogenitor of the influencer marketing phenomenon, Mary Kay Ash’s entrepreneurial legacy is expected to continue through generations of followers. This finding speaks to the importance of today’s entrepreneurs using the spoken and written word to influence others and create a lasting organizational legacy.

Countless scholars have used pentadic analysis, with a variety of artifacts, to examine the motives behind the rhetoric. However, rhetoric as a means of persuasion and influence has received little attention within the context of the written works by management gurus (Jones et al., 2009), and, aside from the exploration by Berglund and Wigren (2012), the narrative of entrepreneurial influence has not benefitted from close examination.

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.

With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.

XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.

This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.

Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.

Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.

This paper aims to examine the service experience in an online support community of consumers to understand the nature of social support and how it is experienced and enacted by vulnerable consumers.

A netnographic study was conducted to examine vulnerable consumers’ participation in an online support group for weight management. The Linguistic Inquiry Word Count (LIWC) program was used, and additionally data were coded using open coding. A hybrid approach to data analysis was undertaken using inductive and deductive methods.

The findings suggest online social support groups can be used as an online “third place” to support vulnerable consumers, with vulnerable groups engaging with the online support group differently than those in the normal weight group. Social support was also found to be bi-directional in nature.

This study only investigates one online support group. To gain deeper insights, other support groups should be examined over a longer period.

This paper demonstrates that transformative services have the hidden capacity to optimize their services to enable vulnerable consumers to co-create social support in a safe place, thus providing a non-judgmental environment with the end goal of improving their health and well-being.

Findings reveal how services can enable marginalization and stigmatization to be overcome and inspire social action through the use of online support groups.

This research is unique in that it used a netnography approach to examine how vulnerable consumers interact in an online service setting, reducing self-report bias and allowing for a natural research setting, thus allowing a unique understanding of how vulnerable consumers experience and enact social support.

Entrepreneurial communication describes the communication activities of entrepreneurs and is an essential tool for entrepreneurs to build relationships. However, there is a lack of research regarding how entrepreneurs adapt their communication styles in times of crisis. Nevertheless, entrepreneurial communication during a crisis is essential because entrepreneurs must continue communicating with their stakeholders and be visible. In this regard, communication has the central aim of preventing the startup from suffering any damage that may result from the crisis. Thus, the present paper explores potential shifts in the communication styles of entrepreneurs during the first wave of the COVID-19 pandemic.

The authors examined the digital footprints of 780 entrepreneurs based in the USA on the social network Twitter. This study used a longitudinal dataset with the software Linguistic Inquiry and Word Count (LIWC) to analyze 110,283 tweets sent pre-crisis and during the first wave of COVID-19.

The results of the exploratory analysis revealed a connection between crisis and both analytical thinking and emotional responses. In the case of emotions, the results also suggest that entrepreneurs who had already received funding from venture capital investors remained emotionally robust during the crisis, as evidenced by the expression of more positive emotions compared to entrepreneurs without funding.

This study contributes to the understanding of entrepreneurial communication and adds the context of an exogenous shock to this research stream. Furthermore, this study highlights the effects of venture funding on the digital communication style of entrepreneurs, especially in the context of expressed emotions, and suggests emotional robustness for these entrepreneurs.

This paper aims to explore, using the employee lens of business-to-business firms, word use through brand engagement and social media interaction to understand the difference between employees who rate their employer brands highly on social media and those who don't.

We conducted a textual content analysis of posts published on the social media job evaluation site glassdoor.com. LIWC software package was used to analyze 30 of the top 200 business-to-business brands listed on Brandwatch using four variables, namely, analytical thinking, clout, authenticity and emotional tone.

The results show that employees who rate their employer’s brand low use significantly more words, are significantly less analytic and write with significantly more clout because they focus more on others than themselves. Employees who rate their employer’s brand highly, write with significantly more authenticity, exhibit a significantly higher tone and display far more positive emotions in their reviews.

Brand managers should treat social media data disseminated by individual stakeholders, like the variables used in this study (tone, word count, frequency), as a valuable tool for brand insight on their industry, competition and their own brand equity, now and especially over time.

This study provides acknowledgement that social media is a significant source of marketing intelligence that may improve brand equity by better understanding and managing brand engagement.

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.

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.