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The authors design a theoretical perspective that explores how different phases of social mood influence financial risk tolerance (FRT) among investors. Risk is involved in almost all financial decision-making. For a better understanding of risk tolerance behavior, the role played by social mood cannot be ignored. This study aims to explore the linkage between social mood and FRT of investors in Pakistan.

Using qualitative phenomenology as the guiding framework, 22 interviews were conducted to have a deeper understanding of the lived experiences of investors with at least 10 years of investment experience. Thematic analysis was done to analyze data. Audio-recording, bracketing, triangulation and member checking were done to ensure validity and reliability.

A theoretical model is developed using the six themes identified through thematic analysis. This model presents an in-depth analysis of the determinants of social mood, its multiple phases and its impact on risk tolerance behavior. Findings reveal that the level of financial literacy, experience and purpose of investment moderate the effect of social mood on FRT.

Investors can manage risk and increase their profits by controlling the effects of social mood. They can benefit from the market situation by taking more risk when the market is extremely low. The advisors can frame their advice in the light of the model.

According to the authors’ knowledge, this is the first study that explores investors’ risk tolerance in response to variations in social mood in the context of an emerging economy. The paper has contributed conceptually and methodologically. It uses phenomenology as the method and develops a theoretical model that describes how different types of investors adjust their risk tolerance in response to changes in their social mood.

The effectiveness of social marketing communication should depend both on message features and on the psychological characteristics of message recipients. This premise was tested in an experiment focused on why consumers may respond differently to different types of pro-recycling advertisements. The message feature was the way in which the advertisements were framed. The psychological characteristic of the message recipient was the respondent's mood. The paper aims to discuss these issues.

Hypotheses were tested in the context of an experiment focused on paper recycling.

It was hypothesized that congruity between mood and the frame of the message would result in more favorable message evaluations and higher intentions to recycle than incongruity. Supporting the study's hypotheses, the data showed that participants in a negative mood had higher intentions to recycle paper and evaluated pro-recycling advertisements more favorably when the ads emphasized avoiding negative consequences as opposed to attaining desired benefits. Among participants in a positive mood, desired benefit advertisements induced higher intentions to recycle paper and were evaluated more favorably than advertisements framed in terms of avoiding negative consequences.

A clear message placement strategy is suggested to increase the effectiveness of social marketing communication campaigns.

This mood congruity effect has not been reported before in the context of pro-environmental communication. Furthermore, the study provides empirical evidence of the underlying psychological mechanisms that cause the observed interaction. The evidence suggests the data could be predictive of similar response patterns in other social marketing communication domains (e.g. in response to health messages, volunteering, charity, etc.).

The purpose of this research is to investigate how postpartum mothers conduct self-disclosure on social media may obtain social support and therefore improve their depressive mood.

The authors extract variables of self-disclosure by manual coding postpartum mothers' 835 posts from a parenting social media in China. The ordinary least squares model and the binary logistic regression model are used to test the proposed hypotheses.

The study suggests that both mothers' superficial level disclosure and personal level disclosure positively affect online social support received, and the effect of personal level disclosure on social support is much greater than that of superficial level disclosure. Online social support received is related to the content of the post and reduces mothers' depressive mood. The authors further find that the association between personal level disclosure and depressive mood is fully mediated by social support.

The data are collected from a parenting social network. Although it is the major parenting social media with the most users in China, the generalizability of this model and the findings to other social media need additional research.

This study offers implications for researchers and practitioners with regard to social media uses and impacts, which also has important implications for policy and interventions for the mental health of mothers.

This paper makes theoretical contributions to the literature of social penetration theory and social support by (1) dividing self-disclosure into superficial level disclosure and personal level disclosure according to the intimacy of self-disclosure; (2) empirically investigating the direct effect of online self-disclosure on social support and the mediating effect of social support between online self-disclosure and mothers' depressive mood.

Sina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices.

An enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts.

By comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively.

Two sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks.

I draw on psychological and sociological theories of affect to depict the relatively stable set of moods and emotions that an individual experiences in social interactions with a given person (relational affect) as a fundamental engine of social action influencing both how and with whom employees perform assigned tasks. I discuss an approach to define and measure relational affect that complements the typical network approach to affect. I then explore motivational mechanisms through which relational affect influences task tie formation and functioning. I conclude that relational affect contributes directly to individuals’ ability to achieve task goals, and to organizational functioning generally.

This paper aims to expand the emerging literature on COVID-19 and the financial markets by searching for a relationship between the uncertainty of the first phase of the COVID-19 pandemic experienced through social media and the extreme volatility of the Italian stock market.

The authors analyze the relationship between social media and stock market trends during the first phase of the COVID-19 pandemic through the lens of social theory and Baudrillard's simulacra and hyperreality theory. The authors conducted the data analysis in two phases: the emotional and Granger correlation analysis by using the KPI6 software to analyze 3,275,588 tweets for the predominant emotion on each day and observe its relationship with the stock market.

The research results show a significant Granger causality relation between tweets on a particular day and the closing price of the FTSE MIB during the first phase of the COVID-19 epidemic. The results highlight a strong relationship between social media hyperreality and the real world. The study confirms the role of social media in predicting stock market volatility.

The findings have theoretical and practical implications as they reveal the relevance of social media in our society and its relationship with businesses and economies. In an emergency, social media, as an expression of users' feelings and emotions, can generate a state of hyperreality that is strong correlated with reality. Since social media allows users to publish and share messages without any filter and mediation, the hyperreality generated is affected by highly subjective elements.

This research is different from the previous ones on the same topic because unlike previous studies, conducted under normal or simulated scenarios, this study is focused on the first phase of an unpredictable and unforeseen emergency event: the COVID-19 pandemic. This research adopts a multidisciplinary approach and integrates previous studies on the economic and financial effects generated by social media by applying well-known theories to a new and unexplored context. The study reveals the significant impact generated by social media on stock markets during a global pandemic.

Both investors and the stock markets are believed to behave in a perfectly rational manner, where investors focus on utility maximization and are not subjected to cognitive biases or any information processing errors. However, it has been discovered that the sentiment of the social mood has a significant impact on the stock market. This study aims to analyze how did the protest event of Tesla happened in April 2021 have a significant effect on the company's stock performance as well as its competitors, Nio, under the competitive effect.

The research is based on time series data collected from Tesla and Nio by employing 10 days, 15 days and 20 days anticipation and adjustment period for the event study. This study employed a text sentiment analysis to identify the polarity of the sentiment of the protest event using the Microsoft Azure machine learning tool which utilizes MPQA subjective lexicon.

The findings provide further evidence to show that a company-specific negative event has deteriorating effects on its stock performance, while having an opposite effect on its competitors.

The paper argues that negative sentiments through social media word of mouth (SWOM) affect the stock market not just in the short run but potentially in the longer run. Such negative sentiments might create a snowball effect which causes the market to further scrutinize a company's operations and possibly lose confidence in the company.

This study explores how the Tesla's protest event at Shanghai Auto Show 2021 has a significant impact on Tesla's stock performance and prolonged negative impact although Tesla implemented immediate remedial actions. The remedial actions were not accepted positively and induced a wave of negative news which had a more persistent effect.

This paper aims to examine whether social comparison in a prior, nonconsumption circumstance (e.g. in an academic setting) affects consumers’ materialism and subsequent spending propensity, and explores the incidental feeling of envy as the underlying mechanism.

Four experiments have been conducted to test these hypotheses. Study 1 manipulated social comparison in an academic domain, and measured undergraduate students’ materialism after they compared themselves to a superior student or to an inferior student. Study 2 used a recall task to manipulate social comparison and examine the mediating role of envy. Study 3 examined which of the two types of envy (benign or malicious) affected materialism. Study 4 examined the downstream consequences on spending propensity in both public and private consumption contexts.

The results suggest that consumers place greater importance on material goods and are more likely to spend money on publicly visible products after making upward social comparisons than after making downward social comparisons or no comparisons. Furthermore, envy acts as the mediator for the observed effect of incidental social comparison on materialism.

First, this study improves our understanding of the consequences of social comparison and envy by demonstrating that incidental envy (both benign and malicious) experienced in a prior, unrelated social comparison can motivate materialistic pursuits. Second, the present research contributes to the compensatory consumption literature by revealing that, in a social comparison context, envy is the affective underpinning that gives rise to the motivation to engage in compensatory consumer behavior. Third, the findings also enrich materialism research by exploring an important situational antecedent in driving materialistic orientation.

Millions of social media users over the last two decades have become dependent on social media that has negatively affected their lives, both physically and mentally. Earlier attempts on social media addiction (SMA) scale development have not been made in India which makes it essential to undertake such a study in this region. One of the objectives of this study was to explore and identify the relevant dimensions of the SMA in the literature. The main study objective was to develop and validate a scale on SMA in India.

The research design applied in this work was both qualitative and quantitative. Based on the literature review, some themes of SMA were recognized that were used to conduct semi-structured interviews with the concerned subjects. The interviews conducted were then transcribed, and using NVivo software application, content analysis was carried out. This helped in the identification of relevant themes that were used for preparing the questionnaire. After that an exploratory study was conducted to explore the factors of SMA. This was followed by a confirmatory analysis on a different sample that validated the instrument.

This work developed and validated a six-factor measure of SMA. The total numbers of items in the final instrument are 22. Salience, Tolerance, Mood Modification, Relapse, Withdrawal and Conflict are the six factors of the final scale.

The current designed instrument can be exceptionally functional for evaluating the occurrence and level of the SMA. This can also serve to assist in the handling and management of such online addictive behavior. A lot of productivity is lost due to the unwarranted time spent on the social media. By better understanding the dimensions of SMA, one can limit one's time on such networks.

This work can provide valuable insight into this domain and help in understanding and treatment of this malaise. It can also help parents and other stakeholders at large to comprehend the danger linked with the excessive social media use and therefore, contribute towards the society. These dimensions can also help counsellors and psychologists to better understand the addiction behavior and accordingly counsel the concerned individual.

Although many instruments have been developed in the past by researchers to measure SMA,still some aspects of the concept remain under-researched. Moreover, most of such research has been confined to the developed world regions.

The purpose of the chapter is to determine social consequences of crises of economic systems and to evaluate the risks of transition of economic crisis from a stimulator of economic development into source of social problems and, in particular, into social crisis.

The authors use the methods of horizontal and trend analysis, as well as the method of regression analysis for determining the dependence of the index of happiness, calculated by the New Economic Foundation, on the growth rate of GDP in current prices according to the International Monetary Fund (IMF). In addition to this, regression analysis of dependence of unemployment rate according to the IMF on the index of economy digitization, calculated by Cisco, in 2018 is determined. The objects of the research are the leading developed countries (G7) according to the classification of the IMF and countries of BRICS (as representatives of developing countries).

As a result of the research, it is determined that traditionally studied indirect indicator of social consequences of innovational development of economy after the 2008 crisis – unemployment rate – is not very informative and is less studied; the index of happiness – which describes public moods with high precision – showed reverse dependence on growth of GDP in constant prices and on digitization of economy. The example of the 2008 crisis showed that developed and developing countries had new social problems in the period of post-crisis restoration of economy; these problems became more clearly expressed than in the period of decline. In the mid-term (by 2020–2025), crisis might transform from stimulator of economic development into a source of social problems.

The obtained results emphasize priority of sustainability of development of economic systems in the economic and social aspects.