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This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

This study aims to examine whether senior Enron executive emails celebrated, or at least left a space for, corporate irresponsibility. Engaging with prior organizational-focused research, we investigate how corporate emails sent by senior executives help constitute Enron by communicating to employees senior management’s stance about important topics and social characters.

The study analyzes the 527,356 sentences contained in 144,228 emails sent by Enron senior executives and other employees in the three-year period (1999–2001) before the company’s collapse. Sentences are used as the base-level speech unit because we are interested in identifying the tone and emotions expressed about specific topics and stakeholders. Tone is measured using Loughran and McDonald’s (2016) financial dictionary approach, and emotion is measured using Mohammad and Turney’s (2013) NRC word-emotion lexicon. Least Absolute Shrinkage and Selection Operator (LASSO) regressions are used to explore the determinants of senior management tone and emotions.

The analysis illustrates that while both senior executives and other employees utilized email to help accomplish task-related activities, they employed different evaluative tones to talk about key topics and stakeholders. Also important is what is left unsaid, with a “spiral of silence” emanating from senior management that created a space for corporate irresponsibility.

Combining advanced computerized textual analysis with qualitative techniques, we analyze a unique dataset to explore micro details involved in using email to communicate a tone at the top. The findings illustrate how what is said or not said by senior management contributes to the constitution of an organization.

This study aims to analyze communication from the perspective of social marketing, positive emotions, and the topics chosen by Spanish tourist destinations to show their destination image. Additionally, this research shows a message classification model, based on the aforementioned characteristics, that has generated a greater impact, offering clarity to tourism managers on the type of content they should publish to achieve greater visibility.

The methodology used in this work combines content analysis and data mining techniques. The classification tree using the chi-square automatic interaction detector (CHAID) algorithm was selected to determine predictors of like behaviour.

The results show that the predictor variables have been emotions, social marketing and topics. Also, the characteristics of the messages most likely to have a high impact are those related to emotions of joy or happiness, their purpose is behavioural, and they talk about rural, cultural issues, special dates, getaways, or highlights of a town or city for something specific.

This study is the first to analyze the content of the tweets shared by destination tourism managers from a social marketing, positive emotions, and sustainability perspective, determining the possible predictors of likes on Twitter. The authors contribute to the literature by deepening the understanding of how social marketing and the positive emotions promoted drive a more significant impact in tourism communication campaigns on social media. The authors provide destination managers with a way better to understand the variables relevant to users in tourism content.

This paper aims to determine what the brand performance consequences of corporate social responsibility (CSR) activities would be during times of recession for well-known brands.

Based on signaling theory, this paper investigates if CSR activities serve to signal higher brand value for consumers via perceptions of better quality and greater differentiation, specifically during recessions. This study incorporates a representative longitudinal sample of known US firms for the analyses, which is accomplished through generalized method of moments estimations.

The findings empirically demonstrate that CSR initiatives during recessions are actually associated with increased perceptions of brand value. More specifically, during recessions, CSR initiatives such as charitable contributions provide a signal to customers of higher brand quality.

This study did not control for the costs of doing specific CSR activities that may be less visible to consumers.

While individual firms or managers may not be able to prevent recessions from happening, they can limit the negative impact of recessions on their performance by engaging in CSR activities (or refrain from cutting back) during these times.

Because CSR initiatives during recessions result in more favorable consumer perceptions of the brand, engaging in CSR aligns both social and managerial interests, owing to the economic gains from CSR investments.

During times of recession, some critics indicate that CSR may be an unaffordable luxury. On the contrary, this research shows that managers may want to consider CSR activities as a means of increasing the value of their brands, especially during economic recessions.

There is growing interest in innovative online problem-solving models such as crowdsourcing to serve individuals, firms and the society as a whole. Crowdsourcing is the combination of efforts from various sets of individuals who are either volunteering or working part-time for socioeconomic production, basically in the cyber world. This hybrid work model is already in use by businesses and entrepreneurs; some of the platforms include Amazon mechanical Turk, 99designs, Hit RECORD and Design Crowd.

Much has been parleyed and published, and this is primarily because of the efficient socioeconomic potentials crowdsourcing offers.

This paper addressed the following three questions to help have a better understanding of crowdsourcing: who can perform crowdsourcing? why it is relevant to crowdsource in this present proliferated internet age and if there are going to be some changes in the future and the last but not the least what can be done to promote it in the society?

This paper discusses the three W’s and concludes with challenges facing the crowdsourcing work model.