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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.

The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.

This paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.

The experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.

Based on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.

Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended.

A Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word cloud were used to visualize unstructured big data of textual content. Sentiment analysis was used to automatically classify 29 million tweets. A fixed-effects model was run on the final panel data.

The findings demonstrate that social bots significantly distort brand-related information across all industries and among all brands under study. Moreover, Twitter social bots are significantly more effective at spreading word of mouth. In addition, social bots use volumes and emotions as major effective mechanisms to influence and manipulate the spread of information about brands. Finally, the bot detection approaches are effective at identifying bots.

As brand companies use social networks to monitor brand reputation and engage customers, it is critical for them to distinguish true consumer opinions from fake ones which are artificially created by social bots.

This is the first big data examination of social bots in the context of brand-related user-generated content.

Several major infrastructure projects in the Hong Kong Special Administrative Region (HKSAR) have been delivered by the build‐operate‐transfer (BOT) model since the 1960s. Although the benefits of using BOT have been reported abundantly in the contemporary literature, some BOT projects were less successful than the others. This paper aims to find out why this is so and to explore whether BOT is the best financing model to procure major infrastructure projects.

The benefits of BOT will first be reviewed. Some completed BOT projects in Hong Kong will be examined to ascertain how far the perceived benefits of BOT have been materialized in these projects. A highly profiled project, the Hong Kong‐Zhuhai‐Macau Bridge, which has long been promoted by the governments of the People's Republic of China, Macau Special Administrative Region and the HKSAR that BOT is the preferred financing model, but suddenly reverted back to the traditional financing model to be funded primarily by the three governments with public money instead, will be studied to explore the true value of the BOT financial model.

Six main reasons for this radical change are derived from the analysis: shorter take‐off time for the project; difference in legal systems causing difficulties in drafting BOT agreements; more government control on tolls; private sector uninterested due to unattractive economic package; avoid allegation of collusion between business and the governments; and a comfortable financial reserve possessed by the host governments.

The findings from this paper are believed to provide a better understanding to the real benefits of BOT and the governments' main decision criteria in delivering major infrastructure projects.

The purpose of this paper is to identify and prioritize key performance indicators (KPIs) that can be used for stage-based performance assessment of build-operate-transfer (BOT) projects.

This research was conducted through focus group discussions and face-to-face questionnaires. Firstly, stage-level KPIs for BOT projects were identified by conducting a literature survey. The list of KPIs that can be used for measuring performance at different stages of a BOT project was finalized by conducting focus group discussions with 12 participants. The data related to the importance of identified KPIs were collected via a face-to-face questionnaire in which 30 high-level managers participated. Based on these data, KPIs were prioritized considering eight different stages of a BOT project by using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The research findings reveal that 63 stage-level KPIs can be used for measuring the performance of BOT projects at eight different stages, which are “feasibility study and preliminary plan,” “announcement and submission of application,” “evaluation and selection,” “negotiation and signing of concession agreement,” “design,” “construction,” “operation” and “transfer.” The most important KPIs were determined as “comprehensiveness of project technical feasibility,” “detailed tendering procedure,” “effectiveness of concessionaires' technical knowledge/capability evaluation,” “good relationships between government and concessionaire,” “technology transfer,” “effectiveness of quality control,” “effectiveness of facility management” and “effectiveness of hand-back management” for each stage. The findings can be used by companies to evaluate performance at each stage of a BOT project and, if necessary, take the necessary actions for performance improvement at the stage level.

The main limitation is the size of the sample, which represents the perspectives of 30 Turkish high-level managers on KPIs in BOT projects. Besides, the selected method, namely, TOPSIS, does not provide quality measures related to the outputs; therefore, it is difficult to see the inconsistencies among the experts.

The study findings will help in devising appropriate performance evaluation practices for BOT projects to overcome the shortfalls of the existing practices and systems proposed in the literature and help in achieving the superior performance while developing infrastructure through the BOT route.

This paper proposes a process-based approach for measuring the performance of a BOT project considering eight different stages. It fills a research gap in the public–private partnership literature by focusing on stages rather than phases. The results can be used by practitioners to establish stage-level performance management systems for BOT projects.

The purpose of this paper is to contribute to the understanding of the crucial influence of risks on the capital structure of build-operate-transfer (BOT) projects.

The equity portion of capital injected in a BOT investment is selected as the response variable and its relation with select identified risk factors is examined using a regression analysis on a data set of BOT projects.

Results have pointed out that the level of equity is significantly influenced by several sources of risk. Country, revenue, project and special purpose vehicle-related risks have been shown to have an impact on the size of the equity share of a BOT investment.

The results could support both investors and lenders to better define the financial leverage of BOT projects. In particular, the study could help to have a better understanding of the main factors that influence the equity apportion of capital in BOT investments.

This paper contributes to fulfilling the lack of works addressing the relationship between risk factors and capital structure in BOT projects. In this way, this research leads to a better understanding of the risk factors that influence the capital structure of BOT project and they have therefore been proposed as a base for the establishment of improved methods to design refined capital structures in BOT projects.

In order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.

Choosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.

The findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.

Most of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.