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Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.

An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.

The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.

The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.

This study examines the impact of social media investor sentiment on the stock market performance through qualitative and quantitative proxies.

The authors use a sample of daily stock performance related to S&P 500 Index for the period from December 18, 2017, to December 18, 2018. The social media investor sentiment was assessed through qualitative and quantitative proxies. For qualitative proxies, the study relies on three social media resources”: Twitter, Trump Twitter account and StockTwits. The authors proposed 3 methods to reflect investor sentiment. For quantitative proxies, the number of daily messages published from Trump Twitter account and StockTwits is considered as a signal of investor sentiment. For regression model, the study adopts the autoregressive distributed lagged to determine the relationships between the nonstationary series.

Empirical findings provide evidence that quantitative measures of investor sentiment have significant effects on S&P’500 performances. The authors find that Trump's tweets should be interpreted with caution. The results also show that the number of Trump's tweets on t−1 day have a positive effect on performance on day t.

Social media sentiment contains information for predicting stock returns and transaction activity. Since, the arrival of new information in capital markets triggers investor sentiment on social media.

This study investigates the investors’ sentiment through social media and explores quantitative and qualitative measures. The amount of information on social media reflects more the investor sentiment than content analysis measures.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0818

This paper aims to investigate textual characteristics of customer reviews that motivate companies to respond (sentiment negativity and sentiment deviation) and how aspects of these company responses (response intensity, length and tailoring) affect subsequent customer review quality (comprehensiveness and readability) over time.

Leveraging a large data set from a leading app website (Shopify), the authors combine text mining, natural language processing (NLP) and big data analysis to examine the antecedents and outcomes of online company responses to reviews.

This study finds that companies are more likely to respond to reviews with more negative sentiment and higher sentiment deviation scores. Furthermore, while longer company responses improve review comprehensiveness over time, they do not have a significant influence on review readability; meanwhile, more tailored company responses improve readability but not comprehensiveness over time. In addition, the intensity (volume) of company responses does not affect subsequent review quality in either comprehensiveness or readability.

This paper expands on the understanding of online company responses within the digital marketplace – specifically, apps – and provides a new and broader perspective on the motivations and effects of online company responses to customer reviews. The study also extends beyond the short-term focus of prior works and adds to literature on long-term effects of online company responses to subsequent reviews. The findings provide valuable insights for companies (especially those with apps) to enhance their online communication strategies and customer engagement.

The purpose of this paper is to focus on measuring the contribution generated by social media when used for business purposes, distinguishing between metrics and methods for data collection and data analysis. Organizations worldwide have widely endorsed social media, but available studies on the contribution generated by these technologies for organizations are fragmented. A performance measurement system (PMS) framework to monitor social media is theoretically derived, highlighting the methods for data collection and data analysis and metrics to quantify social media impacts in terms of financials, network structure, interactions, conversations and users’ opinion.

This is a qualitative research based on a literature review of papers in management, information technology, marketing and public relations.

A PMS framework to quantify the contribution of social media is theoretically derived, distinguishing between metrics and methods. PMS metrics support the measurement of the financial and relational impact of social media, as well as the impact of social media conversations and users’ opinions. PMS methods comprise different approaches for data collection and data analysis that range from manual to automated data collection and from content to sentiment analysis techniques.

The PMS framework contributes to the academic literature by integrating a unique model of the available approaches for social media measurement that can serve as a basis for future research directions. The framework also supports practitioners that face necessity to quantify financial and relational contributions of social media as well as the contribution of social media conversation and users’ opinion.

The purpose of this paper is to evaluate published institutional investor research focused on home bias, disposition effect, and herding behavior in recognized journals and to ascertain some substantial gaps with regard to them.

Recently published studies between 2005 and 2014, which intend to examine behavioral biases on institutional investors, have been reviewed through juxtaposing them under the three fundamental titles and figuring them according to the explanation why these biases occurs.

The research examining home bias has identified the presence of this effect on institutional investors and explained it with information or culture. Yet, the existence of disposition effect has not been found in the extant research. These studies have estimated disposition effect through overconfidence and experience. Also, extant studies have provided evidence of herding behavior, attributing this behavior to pursuing same published information and protecting their reputation and career.

Currently, no study, which reviews and evaluates the empirical research body on behavioral biases displayed institutional investors, exists. To the authors’ knowledge, this is the first paper which highlights the empirical evidence on these bias and summarizes the explanations in these studies for these biases exhibited by institutional investors. This could contribute to the researchers focusing on behavioral biases on institutional investors by providing them with a meaningful figuralization regarding their evidence and explanation.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

The paper's aim is to explore consumer perceptions of marketing and test the malleability of those perceptions.

Study 1 is exploratory in nature, and employs a free-response sentence completion to, “marketing is […]”. Study 2 employs an experimental design, testing whether the framing of communications about marketer performance (firm-oriented vs consumer-oriented) influences consumer perceptions of marketing.

Based on free responses to “marketing is […]”, findings indicate that US consumers generally see marketing as something that is bad for them, but good for businesses. However, this asymmetry disappears when marketer performance is communicated with a consumer orientation.

Marketers aim to create relationships with consumers based on value exchange, yet consumers do not see such value exchange. They see the value of marketing for business, but not for consumers themselves. By being more cognizant of how marketer performance is discussed, marketers can overcome such perceptions and build better relationships with consumers.

Other research on attitude toward marketing focuses solely on people's feelings about marketing; here we capture an extra dimension – namely, consumer perceptions of who marketing serves. Further, extant research on consumer attitudes toward marketing tend to describe their current state; this paper does the same but also tests and offers a specific solution for improving perceptions of marketing.

This paper aims to explore the impact of COVID-19 on office space in Johannesburg. This study further explores the role of changing work practices in the office sector in South Africa because of the pandemic and its impact on future office space use planning and management.

To understand the footprints that the COVID-19 pandemic has left on the office space market in Johannesburg, this study uses semi-structured interviews, which were administered to corporate office users, and a thematic analysis was adopted to understand the views, perspectives and expectations of office users.

The study showed that space users perceive COVID-19-induced remote working as having benefits, opportunities and challenges. A notable shift in office space utilization has emerged, with employees increasingly opting for roles that permit remote work. This newfound flexibility, accommodating both on-site and remote work, often makes working from home more appealing than traditional office environments that may no longer align with users’ preferences for office spaces.

The study is limited to Johannesburg, South Africa, and may not apply to other African markets. Ten in-depth interviews were conducted, and analysis and results were deduced; this may be considered a limitation of this study.

The pandemic’s impact has brought about irreversible changes, compelling policymakers and business leaders to strategize and prepare. This proactive stance aims to prevent avoidable challenges for employees and companies during future pandemics. A thoughtful approach to the post-pandemic world can usher positive changes in the office and property sector. This includes the coexistence of both remote work and on-site working models.

This paper provides valuable insight into some of the outcomes of the COVID-19 pandemic in South Africa that are essential for future office space use planning and management. The insights from this study extend the literature and provide novel knowledge based on an office sector in the “global south.”