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The present research builds on three complementary theories to explore how social influence processes in interaction bring about opinion and sentiment change: expectation states theory, affect control theory, and social influence network theory.

An experimental study is used to test intersections between the theories and assess how performance expectations, affective impressions of group members, and emergent perceptions of their influence work together to generate opinion and sentiment change.

Respondent opinions shifted in the direction of group leaders’ opinions, regardless of behavioral interchange patterns. Opinion change was greater when a third group member shared the leader’s opinion. Change in affective impressions was shaped by the group leader’s opinion, the assertiveness of their behavior, and the support of a third group member. The perceived influence composition of the group predicted opinion and sentiment change, above and beyond the effects of conditional manipulations. Features of the group interaction led to inferences about status characteristics that reinforced the influence order of the group.

The chapter tests hypotheses from earlier work and explores status signals not yet tested as predictors of opinion change – behavioral interchange patterns and the degree of support for one’s ideas. In addition, it examines inferences about status characteristics following the group discussion, and influence effects on the prevailing definition of the situation.

This chapter contributes to recent integrative work that explores the relationship between performance expectations, affective impressions, and social influence. Synergistic processes forwarded by earlier research are tested, along with several newly proposed linkages.

The economic depression of the mid‐1970s gave reasons to question many presuppositions taken almost for granted earlier. This was the case with the welfare state too, which was seen to be in crisis. This study focuses on one particular aspect of the welfare state, namely, its acceptability or legitimacy among the citizens of Finland.

Public dislike of taxes led to tax revolt and tax reform. Despite the connection between tax attitudes and tax policy, relatively little is known about public attitudes toward taxes over time, and how public opinion either shapes or is shaped by changes in tax policy. We examine the link between opinion and changes in tax policy in Florida, where the public’s view of sales and property taxes was surveyed consistently from 1979-1997, a time when both taxes changed significantly. This combination of tax reform and survey data allows us to observe the pattern of public opinion before, during, and after changes in tax policy, and to draw inferences about whether public opinion leads or lags state action, while examining common explanations for individual differences in opinion. Among other things, our results indicate that the portrait of an anti-tax populace is overdrawn and that the pattern of opinion differs for each tax.

The purpose of this paper is to trace the behaviour of Chinese companies receiving a special treatment (ST) designation in order to determine the extent to which the application of this regulation may have led companies to engage in activities conducive to the removal of the ST designation. In particular, the paper examines evidence of opinion shopping or earnings manipulation by these companies.

Empirical analysis of annual report databases for Chinese‐listed companies, including statistical significance testing relating to ST companies.

Most ST companies have removed the ST status by the third year after the initial ST designation. Compared to non‐ST companies, ST companies losing the ST status are more likely to engage in practices indicating earnings manipulation. Also, compared to non‐ST companies, ST companies are more likely to change auditors after an initial or second year of ST designation. However, while this behaviour suggests opinion shopping, auditor switching for the ST companies is not associated with losses becoming profits nor with improved audit opinions.

The results reported in this paper must be considered in light of the limitations inherent in empirical analyses. That is, the relationships identified in this paper are indicative of potential earnings management or audit opinion shopping; however, the study cannot provide the actual reasons for these empirical results.

The results suggest the ST regulation did not lead to unintended consequences in terms of auditor switching by ST companies to improve either their reported earnings or their audit opinion.

The ST status is unique to China and this paper is the first to report on potential reporting and audit quality implications of this regulation.

Although Human Papilloma Virus (HPV) is the most commonly diagnosed sexually transmitted infection in the USA, much controversy exists with respect to HPV vaccination, especially among parents of adolescents. Previous research has shown that exemplars in the media influence public opinion estimates about controversial social issues. However, little is known about the underlying psychological processes of how exemplars influence public opinion formation. The purpose of this paper is to systematically explore such psychological processes based on the projection theory. To this end, the important yet controversial public health issue, the mandatory HPV vaccination, was chosen.

A two-factor (exemplar vs proportion), between-subject experiment was conducted using online newspaper articles as main stimuli. A total of 138 participants completed the study. The analytical framework comprised the Sobel test with the Bootstrap method and a series of Ordinary Least Square hierarchical regression analyses.

The higher the proportion of exemplars against the HPV vaccination in a news article was, the greater the number of individuals who became opposed to it was. And the high personal opposition translated into negative public opinion change estimation.

The findings indicate that news exemplars may influence individuals’ personal opinion formation, and, in turn, contribute to their estimations of future public opinion climate, as suggested by the projection theory. Theoretical, methodological and practical implications for journalists, health educators and policy makers are discussed.

To examine whether auditor‐client relationships have an effect on the decision by an auditor to remove an audit qualification.

The paper tracks the event history of a sample of firms from the issuance of a first time audit qualification for going concern and non‐going concern contingencies (initial qualification issued between 1983 and 1987, all pre Statement of Auditing Standard (SAS) 58) to the issuance of a clean opinion (up through 1995 when SAS 79 was issued). Attachment theory provides a theoretical framework for the variables analyzed and discrete time survival analysis is used as the statistical method in the analysis so as to evaluate each company year from the initial unclean opinion to the year a clean opinion is issued.

It is found that interpersonal and interorganizational attachment has a significant impact on those opinion decisions that require more auditor judgment (i.e. going concern).

This study examines the linkage between auditor tenure and audit quality in a broader context than has been examined to date. Using attachment theory for the foundation, auditor tenure can be viewed as but one measure of the attachment between auditors and clients. In this study, a number of measures of both interpersonal and interorganizational attachment between auditors and clients are included. Further, auditor opinion judgments are examined as a determinant of auditor quality. Finally, discrete‐time survival analysis is employed which allows the tracking of the entire event history from initial qualification to removal of the qualification, something not possible with most standard statistical techniques.

The outbreak and continuation of COVID-19 have spawned the transformation of traditional teaching models to a certain extent. The Chinese Ministry of Education’s guidance on “keep learning and teaching during class suspension” has made OTC and learning (OTC) become routinized, and the public’s emotional attitudes toward OTC have also evolved over time. The purpose of this study is to segment the emotional text data and introduce it into the topic model to reveal the evolution process and stage characteristics of public emotional polarity and public opinion of OTC topics during public health emergencies in the context of social media participation. The research has important guiding significance for the development of OTC and can influence and improve the efficiency and effect of OTC to a certain extent. The analysis of online public opinion can provide suggestions for the government and media to guide the trend of public opinion and optimize the OTC model.

This paper takes the topic of “OTC” on Zhihu during the COVID-19 epidemic as an example, combined with the characteristics of public opinion changes, chooses Boson emotional dictionary and time series analysis method to build an OTC network public opinion theme evolution analysis framework that integrates emotional analysis and topic mining. Finally, an empirical analysis of the dynamic evolution of the communication network for each stage of the life cycle of a specific topic is realized.

This paper draws the following conclusions: (1) Through the emotional value table and the change trend chart of the number of comments, the analysis found that the number of positive comments is greater than the number of negative comments, which can be inferred that the public gradually accepts “OTC” and presents a positive emotional state. (2) By observing the changing trend of the average daily emotional value of the public, it is found that the overall emotional value shows a stable development trend after a large fluctuation. From the actual emotional value and the fitted emotional value curve, it can be seen that the overall curve fit is good, so ARIMA (12, 1, 6) can accurately predict the dynamic trend of the daily average emotional value in this paper. Therefore, based on the above-mentioned public opinion, emotional analysis research, relevant countermeasures and suggestions are put forward, which is conducive to guiding the development direction of public opinion in a positive way.

Taking the topic of “OTC” in Zhihu as an example, this paper combines Boson emotional dictionary and time series to conduct a series of research analyses. Boson emotional dictionary can analyze the public’s emotional tendency, and time series can well analyze the intrinsic structure and complex features of the data to predict the future values. The combination of the two research methods allows for an adequate and unique study of public emotional polarization and the evolution of public opinion.

The recommendation of the analyst report is not only limited to a small number of ratings, but also biased toward a buy opinion with the absence of sell opinion. As an alternative to this, this paper aims to extract analysts' textual opinions embedded in the report body through text analysis and examine the profitability of investment strategies. Analyst opinion about a firm is measured by calculating the frequency of positive and negative words in the report text through the Korean sentiment lexicon for finance (KOSELF). To verify the usefulness of textual opinions, the author constructs a calendar-time based portfolios by the analysts' textual opinion variable of each stock. When opinion level is used, investment strategy has no significant hedged portfolio return. However, hedged portfolio constructed by opinion change shows significant return of 0.117% per day (2.57% per month). In addition, the hedged return increases to 0.163% per day (3.59% per month) when the opening price is used instead of closing price. This study show that the analysts’ opinion extracted from text analysis contains more detailed spectrum than recommendation and investment strategies using them give significant returns.

In the organizational technology acceptance (TA) decision-making process, stakeholders have many divergence opinions. Sometimes, an opposing stakeholder of a decision can stop the whole process of decision-making. In such a case, consensus may take a long time followed by a high risk. The purpose of this study is twofold. First, to find the best model with the least prediction error for the simulation of the consensus process in TA decisions. Second, to investigate the time required for the consensus process to yield the TA decision in different scenarios and to propose solutions to reduce the required time in a case study.

This study uses real-world data containing 1,186 actual observations. Stakeholders are decision-maker agents, and the observations are derived from survey data and used for simulation. Data were obtained from 126 experts in the Iranian rail freight industry. Opinion dynamics theory was used for agent-based simulation of stakeholders’ behavior. The agents interacted over time and their effects on other agents’ opinions were investigated.

The results illustrate an appropriate opinion changing model, a data-gathering method and a simulation scenario for TA consensus. The suitable model was selected after examining the advantages and disadvantages of and comparing the prediction results for different models with the real database of opinions. To reduce the consensus process time, the results suggest gathering the team members and networking with some leaders as advocators. A large number of advocators with high acceptability and continuous exchanging messages with other agents can improve the acceptance rate and have the most significant impact on other stakeholders’ opinions.

To the best of the authors’ knowledge, previous studies simulate individual TA processes. However, there is a difference between the individual TA and the organizational TA. The organizational TA requires the simultaneous decision-making of different stakeholders. In this research, the organizational TA was investigated.

The purpose of the study was to assess the representation of different world views with respect to climate change in public opinion on the internet.

The authors conducted this world views analysis by means of a content analysis of publicly expressed opinions in the form of online lay reader comments to articles on climate change, published on Dutch newspaper web sites between August 2002 and December 2009. The comments were assigned to the world views of two typologies commonly used in ex ante assessment of climate policies. The classification of an online reader comment was based on world view specific keywords and positions on climate change.

From a set of 2,148 comments to 168 articles found on the web sites of 19 newspapers, 314 comments could be assigned to a particular world view. For both typologies, the distribution of comments over the different world views was highly uneven, with world views characterized as “climate sceptic” scoring more than 90 per cent of the assigned comments. The strong dominance of these “climate sceptic” world views was independent of year, newspaper, and scope of the article.

These findings are in stark contrast with the outcomes of public opinion surveys indicating that only a minority of the population has a preference for a “climate sceptic” world view. The most plausible explanation for this difference is that the contributors of online reader comments are not representative for the Dutch population at large. However, as internet-based opinions have a proven potential to strongly influence the opinion of the general public and politicians on climate change, the authors advise analysts to pay due attention to “climate sceptic” world views in ex ante assessment of the societal support for climate policies.

For a world views analysis, the study is unique both in its focus on internet public opinion and the data source used.