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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.

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 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 paper aims to present a research framework examining how hotel ratings can be affected by travel purposes (business vs leisure). It explores theoretical foundations, introduces hypotheses and outlines the methodology used in six studies to investigate connections between trip types, work-related factors, hotel ratings and other variables. The goal is to enhance understanding of factors influencing consumer hotel evaluations, with implications for hospitality industry service design and marketing strategies.

The study uses a multi-method approach to investigate the impact of trip type (business vs leisure) on hotel ratings. The theoretical framework is rooted in perceptual frameworks that shape the evaluation of travel experiences. The research proposes and tests several hypotheses through six studies, using diverse data sources and methods for enhanced reliability.

This study shows that business travelers consistently give lower hotel ratings than leisure travelers, with work-related concerns playing a crucial role. Consumers’ perceived lack of hospitality during a business trip diminishes work-related concerns and hotel ratings. Similarly, low empathy from hotel workers leads to reduced work concerns and lower ratings. The research provides actionable insights for hotel management to enhance services for business and leisure travelers, emphasizing the importance of addressing work-related considerations to improve overall satisfaction.

This study uniquely explores the contrasting influences of business and leisure travel on hotel evaluations, focusing on how work-related concerns mediate these relationships. By employing a comprehensive approach that includes secondary data, experiments, and sentiment analysis, it uncovers how business travelers' stressors lead to lower hotel ratings compared to leisure travelers who prioritize relaxation. The research introduces novel insights into how factors like extended stays and staff empathy can mitigate these negative effects, offering actionable recommendations for hotel management. This work enriches the hospitality literature by providing a deeper understanding of how trip types impact guest satisfaction and proposes targeted strategies to enhance service quality for different traveler segments.

本文提出了一个研究框架, 探讨酒店评分如何受旅行目的（商务与休闲）的影响。研究探讨了理论基础, 提出了假设, 并概述了六项研究中使用的方法, 以调查旅行类型、与工作相关的因素、酒店评分及其他变量之间的联系。本研究目标是加深对影响消费者酒店评价因素的理解, 对酒店行业的服务设计和营销策略具有重要意义。

研究采用多方法的路径, 探讨旅行类型（商务与休闲）对酒店评分的影响。理论框架基于感知框架, 以塑造了对旅行体验的评价。研究通过六项研究提出并测试了多个假设, 利用多种数据来源和方法以增强可靠性。

本研究表明, 商务旅客给出的酒店评分一贯低于休闲旅客, 工作相关的忧虑起着关键作用。消费者在商务旅行期间感受到的缺乏热情的款待会加重工作相关的忧虑, 降低酒店评分。同样, 酒店员工的低同理心会加剧工作忧虑, 导致评分降低。研究为酒店管理提供了可操作的见解, 以提升针对商务和休闲旅客的服务, 强调了解决工作相关考虑因素以提高整体满意度的重要性。

研究的优势在于方法多样性和样本量庞大, 增强了研究结果的可靠性和普遍性。多项研究的方法全面探讨了研究问题, 为学术界和酒店行业提供了宝贵的见解。

Objetivo-Este documento presenta un marco de investigación en el que se examina cómo las valoraciones de los hoteles pueden verse afectadas por el tipo de viaje (negocios vs. ocio). Explora los fundamentos teóricos, introduce hipótesis y esboza la metodología utilizada en seis estudios para investigar las conexiones entre los tipos de viaje, los factores relacionados con el trabajo, las valoraciones de los hoteles y otras variables. El objetivo es mejorar la comprensión de los factores que influyen en las evaluaciones hoteleras de los consumidores, con implicaciones para el diseño de servicios y las estrategias de marketing de la hostelería.

El estudio emplea un enfoque multimétodo para investigar el impacto del tipo de viaje (negocios vs. ocio) en las valoraciones de los hoteles. El marco teórico se fundamenta en los marcos de referencia sobre la percepción que conforman la evaluación de las experiencias de viaje. La investigación propone y prueba varias hipótesis a través de seis estudios, utilizando diversas fuentes de datos y métodos para mejorar la fiabilidad.

Este estudio demuestra que los viajeros de negocios dan sistemáticamente valoraciones más bajas a los hoteles que los viajeros de ocio, y que las preocupaciones relacionadas con el trabajo desempeñan un papel crucial. La falta de hospitalidad percibida por los consumidores durante un viaje de negocios disminuye las preocupaciones relacionadas con el trabajo y las valoraciones de los hoteles. Del mismo modo, la escasa empatía de los trabajadores de los hoteles hace que disminuyan las preocupaciones laborales y las valoraciones. La investigación ofrece ideas prácticas para que la dirección de los hoteles mejore los servicios a los viajeros de negocios y de ocio, haciendo hincapié en la importancia de abordar las consideraciones relacionadas con el trabajo para mejorar la satisfacción general.

La importancia de esta investigación reside en la diversidad metodológica y el amplio tamaño de la muestra, que aumentan la fiabilidad y generalizabilidad de los Resultados. El enfoque multiestudio explora de forma exhaustiva las preguntas de la investigación, proporcionando valiosas perspectivas para el mundo académico y la industria de la hostelería.

The purpose of this paper is to begin to explore the strategic priorities of unicorn ventures as pursuers of market disruption. This study approaches this task by drawing on the positive deviance concept for studying outliers with the intent of understanding the strategic priorities of these ventures.

This is a comparison study of the priorities of 75 unicorn ventures, 37 early-stage ventures and 45 Fortune 500 organizations. The authors use computer-aided text analysis to conduct within-sample and between-sample means comparison tests of 12,487 newswires from 2022.

Where early-stage ventures emphasize their mission, and Fortune 500 companies emphasize financial results, unicorn ventures, occupy the middle of the spectrum, balancing their priorities between pursuing market disruption and achieving financial results. These high-growth outliers indicate their priorities by using significantly less positive tone, affective and prosocial language, and focusing less on corporate social responsibility initiatives, compared to early-stage ventures (and using more of this language compared to Fortune 500 ventures). An additional finding emphasizes that public Fortune 500 companies focus significantly more on money than their topic of interest.

This work has implications for understanding the strategic priorities of entrepreneurial ventures in different development stages. The results suggest that unicorn ventures actively work to balance their startup mission, which allows them to experience high-growth and achieve market disruption, with the financial demands of venture capital investors. This novel conclusion demonstrates the value of using positively deviant outlier cases, such as unicorn ventures, as a viable sample for studying market disruption.

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.