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Existing studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to measure quality or only evaluating a single aspect of task performance. To address this gap, this study analyzes the linguistic styles of task descriptions from 2,545 tasks on the Taskcn.com crowdsourcing platform.

An empirical analysis was completed for task description language styles and task performance. The paper used text mining tool Simplified Chinese Linguistic Inquiry and Word Count to extract eight linguistic styles, namely readability, self-distancing, cognitive complexity, causality, tentative language, humanizing personal details, normative information and language intensity. And it tests the relationship between the eight language styles and task performance.

The study found that more cognitive complexity markers, tentative language, humanized details and normative information increase the quantity of submissions for a task. In addition, more humanized details and normative information in a task description improves the quality of task. Conversely, the inclusion of more causal relationships in a task description reduces the quantity of submissions. Poorer readability of the task description, less self-estrangement and higher language intensity reduces the quality of the task.

This study first reveals the importance of the linguistic styles used in task descriptions and provides a reference for how to attract more task solvers and achieve higher quality task performance by improving task descriptions. The research also enriches existing knowledge on the impact of linguistic styles and the applications of text mining.

The study aims to explore language style and gender match as a key part of initial trust among potential donors and how this leads to funding success based on the similarity attraction and homophily theories. Empirical analyses of 160 respondents revealed that people are more concerned about “how it is written” (language style) than “who has written it” (gender).

Crowdfunding (CF) is an internet-based method of funding employed by project founders, allowing individuals to raise funds from the crowd to support their projects. It is important for project founders to attract the crowd’s interest as potential funders commonly have limited information about projects. One of the early cues about a project that can be picked up by the crowd in CF projects is the text description of the proposal. This text description is crucial for giving the crowd an understanding of the project and for promoting the crowd’s trust in the founder, encouraging them to fund the CF project. Based on the similarity attraction and homophily theories, this study sets out to explore language style and gender match as key elements of initial trust among potential donors and how they lead to funding success. A 2 × 2 factorial experimental design (e.g. subject, male and female, × male language style and female language style) was used for the study. To determine the sample size of the experiment, this study applied power and sample size estimations to measure how many respondents were needed for the experiment. Based on the power table of effect size, 128 respondents were considered to be a sufficient number for this experiment to ensure sufficient statistical power of 0.8 and a significance level of 0.05. This study fulfilled the requirement by recruiting 160 respondents, which corresponded to 40 subjects per group based on a 2 × 2 factorial design (the respondents’ gender, male and female, and text language style, male and female). The empirical analysis of 160 respondents revealed that people are more concerned about “how it is written” (language style) than “who has written it” (gender).

This paper contributes to project founders’ understanding and knowledge of the importance of linguistic style, which can determine the success of a CF project. One of the important results of this study is that the crowd can identify the author’s gender based on their writing style. Through an experiment applying factorial analysis (2 × 2), it was found that people are more concerned about “how it is written” (language style) than with “who it is written by” (gender). This means that the project founder, if they know who their audience is, should know how to write the project proposal so that it fits the audience’s preferences. More specifically, the success rate of CF projects can be increased by integrating suitable word dimensions in promotions of projects on CF platforms. Therefore, it can be argued that linguistic style is a powerful agent for building a connection with a target audience. The findings of this study can be used as theoretical guidance, and eventually, the potential antecedents of funding intention can be further explored.

This study is subject to several limitations. The result is limited to donation-based CF. As this study focuses on the language style of project founders when they describe their CF projects, donation-based CF was the most appropriate platform for this research. In donation-based CF, the style words are more emotion-based, compared to other CF platforms. The experiment, however, could also be replicated for other CF types such as reward-based CF. One important part of CF projects is persuading the crowd to fund them. It is worth mentioning that reward-based CF involves individuals pledging to a business in exchange for a reward. Yet, even though reward-based CF offers rewards, it is generally considered a subset of donation-based CF because there is no financial return to the backer. Therefore, it is suggested that future research should also consider case studies in reward-based CF. Second, from the persuading perspective, this study focusses on narrative language style only, as it facilitates the crowd’s understanding of a CF project. Future study can further focus on other information content such as videos in the project proposal. Prior research has found that providing a video in a CF project increases the crowd’s confidence in funding (Mitra, 2012). The study is also supported by previous studies that suggest producing a higher quality of video in the project proposal positively related to the success of CF projects (Mollick (2014)).

The result of this study empirically confirmed that the crowd’s willingness to fund a project proposal and their trust are dependent on the text description of the project proposal. The project founders need to know how to describe the content of a project so that it signals the quality of the project, especially in early start-ups. In other words, the way that a project is created and published through a CF platform will send a valuable signal to the potential donors about the project, and they will either find it acceptable or reject it. If the project appears to lack demand among potential donors, it is easier for project founders to quickly identify that the project will fail, without the need to invest additional capital.

The findings of this study have important social implications that provide guidelines for project founders on establishing a strategy to help the crowd understand their projects. At the same time, the findings can help the crowd to make their funding decisions. First, the text language used in the CF project by the project founder plays an important role in presenting the campaign and all the ideas need to be presented in a clear way so that the crowd understands the project. In CF projects, pitch is everything (Varsamis, 2018). The pitch refers to the text or video that is provided by the project founder to show their project proposal to the crowd. Compared with traditional funding channels (such as venture capital, i.e. banks), CF is more convenient for raising funds. This is because the project founders need to show their ideas in a creative way to the crowd online, rather than preparing a complex plan and racking their brains on how they can persuade investors to participate (Wang and Yang (2019)). This research intends to help project founders understand how they can influence the crowd by improving the text language used in their CF projects.

This paper fulfils an identified need to study how the linguistic style of the project founder would lead to the success rate of crowdfunding projects.

Social media have increasingly gained credibility as information sources in emergencies. Retweeting or resharing nature has made Twitter a popular medium of information dissemination. The purpose of this article is to enhance our understanding of both linguistic style and content properties (i.e. both affective and informational contents) that drives resharing behavior or virality of disaster messages on Twitter. We investigate this issue in the context of natural disaster crisis.

In this study, the authors develop, drawing upon language expectancy and uncertainty reduction theories as an enabling framework, hypotheses about how the language (i.e. style and content) influence resharing behavior. They employ a natural language processing of disaster tweets to examine how the language – linguistic style (concrete and interactive language) and linguistic content (information- and affect-focused language) – affects resharing behavior on Twitter during natural disasters. To examine the effects of both linguistic style and content factors on virality, a series of negative binomial regressions were conducted, particularly owing to the highly skewed count data.

Our analysis of tweets from the 2013 Colorado floods shows that resharing disasters tweets increases with the use of concrete language style during acute emergencies. Interactive language is also positively associated with retweet frequency. In addition, neither positive nor negative emotional tweets drive down resharing during acute crises, while information-focused language content has a significantly positive effect on virality.

Agencies for public safety and disaster management or volunteer organizations involved in disseminating crisis and risk information to the public may leverage the impacts of the linguistic style and language content through the lens of our research model. The findings encourage practitioners to focus on the role of linguistic style cues during acute disasters. Specifically, from the uncertainty reduction perspective, using concrete language in the disaster tweets is the expected norm, leading to a higher likelihood of virality. Also, interactively frame disaster tweets are more likely to be diffused to a larger number of people on Twitter.

The language that people use offer important psychological cue to their intentions and motivations. However, the role of language on Twitter has largely been ignored in this crisis communication and few prior studies have examined the relationship between language and virality during acute emergencies. This article explains the complex and multifaceted nature of information resharing behavior using a multi-theoretical approach – including uncertainty reduction and language expectancy theory – to understand effects of language style and content cues on resharing behavior in the context of natural crisis events.

The purpose of this paper is to explore how linguistic style and message substance influence persuasion in civic crowdfunding marketplaces in which written narrative pitch become a vital communication to attract private contributions to public goods and services. Drawing on the elaboration likelihood model (ELM), the authors operationalize the linguistic style of the narrative pitch as language power and message substance as issue-relevant argument quality. In this paper, the authors examine how characteristics of both style and message are related to the outcome of civic crowdfunded projects.

The data on civic crowdfunding projects were retrieved from Spacehive, the platform that dedicated mainly to civic projects ranging from community programs, social-oriented enterprises, to infrastructure or facility development. Each of the narrative samples is analyzed using a computerized text analysis package called the Linguistic Inquiry and Word Count to extract the features of the linguistic style and message substance in the narratives. The logistic regression models are estimated to assess the impact of both linguistic style and message substance on crowdfunding decisions.

The results show that funding outcomes can be improved with psychological language dimensions (i.e. positive affective and perceptual language). However, extensive use of social language does not help project creators to increase their chance of funding performance; but instead, such language reduces the likelihood of project success. Additionally, message substance or issue-relevant information such as money and risk language influences funding outcome.

Very few empirical studies investigated the differential effects of language style and message substance on funding performance of crowdfunding campaigns. The authors draw upon the dual process of persuasion as a theoretical base to identify a comprehensive set of linguistic style and message substance and to examine the role of such features in an emerging civic crowdfunding market. This study advances the application of the dual process in ELM by identifying and examining distinct persuasive cues originating from linguistics styles and message contents.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.

Begins with a treatment of some general background issues on the nature and function of language. Maintains that English may be thought of as a range of styles which are used according to purpose and audience. Specialized purposes with specialized audiences produce specialized styles, which means that specialist styles will produce some level of difficulty for most people, particularly speakers of other languages. Sets out some implications of this training, and gives some strategies for approaching training in such a context. Describes a number of ways of estimating the readability of text and gives an example of text simplification. Concludes with a brief description of some strategies for actively teaching those aspects of specialist language which the trainer isolates as being important.

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.

Following the significant role of critical thinking (CT), sense of efficacy beliefs and teaching style preferences in the success of teachers and consequently the success of the educational system, this study intended to explore their relationship and the possible influence of these three factors among English as a Foreign Language (EFL) university professors.

To this end, Watson–Glaser Critical Thinking Appraisal-Form A, Teacher Sense of Efficacy Scale (TSES) and Grasha's Teaching Style Inventory (TSI) were administered to 320 Iranian EFL university professors.

The data were examined via path analysis indicated that teachers' CT abilities and sense of self-efficacy beliefs significantly influenced the teaching style preferences. Moreover, it was concluded that Iranian EFL university professors' CT skills positively affect their sense of efficacy beliefs.

Future studies may advance the possible relationships among the sub-components of CT, self-efficacy beliefs and teaching style. Furthermore, further investigations are recommended to study the influence of university professors' CT, self-efficacy beliefs and teaching style preferences in enhancing their learners' achievement.

The implications of the present study may contribute to the field of teacher education in providing opportunities for teachers to develop and practice higher-order thinking and self-assisted skills.

The implications of this study may redound to the advantage of university professors, teacher educators and policy-makers.

This research is original. To the best of the researchers' knowledge, there has been no study investigated the possible relationships between CT, sense of efficacy beliefs and teaching style preferences in higher education.