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The purpose of this study is to identify the structural processes that lead citizens to escape their common social circles when talking about politics and public affairs (e.g. “filter bubbles”). To do so, this study tests to what extent political attitudes, political behavior, news media consumption and discussion frequency affect discussion network heterogeneity among US citizens.

Supported by the polling group Nielsen, this study uses a two-wave panel online survey to study the antecedents and mechanisms of discussion network heterogeneity among US citizens. To test the hypotheses and answer the research questions, ordinary least squares (OLS) regressions (cross-sectional, lagged and autoregressive) and mediation analyses were conducted.

The findings imply that political discussion frequency functions as the key element in explaining the mechanism that leads politically interested and participatory citizens (online) as well as news consumers of traditional and online media to seek a more heterogeneous discussion network, disrupting the so-called “filter bubbles.” However, mediation analyses also showed that discussion frequency can lead to more homogenous discussion networks if people score high on political knowledge, possibly reflecting the formation of a close network of political-savvy individuals.

The survey data give important insights into the 2016 pre-election situation, trying to explain why US citizens were more likely to remain in homogenous discussion networks when talking about politics and public affairs. By using two-wave panel data, the analyses allow to draw tentative conclusions about the influential and inhibiting factors and mechanisms that lead individuals to seek/avoid a more heterogeneous discussion network.

Increased access to flexible work arrangements has the prospect of enhancing work-family reconciliation. Under consideration is extent that managers assumed lead roles in initiating discussions, the overall volume of discussions that occurred, and the outcomes of these discussions.

A panel analysis of 950 managers over one and a half years examines factors predicting involvement in a change initiative designed to expand flexible work arrangement use in a company in the financial activities supersector.

The overall volume of discussions, and tendencies for managers to initiate discussions, is positively predicted by managers’ prior experiences with flexibility, training to promote flexibility, and supervisory responsibilities. Managers were more inclined to promote flexibility when they viewed it as a supervisory responsibility and when they believed that it offered career rewards. An experiment demonstrated that learning of professional standards demonstrated outside of one’s own unit increased promotion of flexible work options. Discussions of flexibility led to many more approvals than denials of use, and also increased the likelihood of subsequent discussions occurring, indicating that promoting discussions of flexible work arrangements can be a path toward expanding use.

The study identifies specific factors that can lead managers to support exploration of flexible work arrangement use.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

Traditionally, most readers' news access and consumption were based on direct intentional news seeking behavior. However, in recent years the emergence and popularization of social media platforms have enabled new opportunities for citizens to be incidentally informed about public affairs and politics as by-product of using these platforms. This article seeks to shed light on how socio-political conversation attributes may explain incidental exposure to information.

Drawing on US and UK survey data, the authors explore the role of political discussion and discussion network heterogeneity in predicting individuals' levels of incidental exposure to news. Furthermore, the authors also test the role of social media news use as a moderator. A hierarchical OLS regression analysis with incidental news exposure as dependent variable was conducted as well as analyses of moderation effects (heterogeneity*social media and political discussion*social media) using the PROCESS macro in SPSS.

Findings reveal that heterogeneous networks are positively related to incidental news exposure in the UK, while sheer level of political discussion is a positive influence over incidental news exposure in the US. Social media news use moderates the relationship between political discussion and incidental news exposure in the UK. That is, those who are highly exposed to news on social media and discuss less often about politics and public affairs, they tend to be incidentally exposed to news online the most. Meanwhile, the interaction of social media news and discussion heterogeneity showed significant results in the US with those exhibiting high levels of both also receiving the biggest share of INE.

This study contributes to closing research gaps regarding how and when people are inadvertently exposed to news in two Western societies. By highlighting that beyond the fate of algorithmic information treatment by social media platforms, discussion antecedents as well as social media news use play an integral part in predicting incidental news exposure, the study unravels fundamental conditions underlying the incidental news exposure phenomenon.

The purpose of this paper is to discover the underlying motivations of Chinese wine consumption.

Qualitative focus group interviews were performed on 36 Chinese wine consumers and four focus groups were performed, with participants segmented into groups based on age and gender.

The main findings were that Chinese wine consumers are influenced by face and status. These issues may be affecting their wine consumption behaviours, particularly related to anomalous behaviours such as mixing red wine with lemonade and the rationale for the preference of cork‐closed wine bottles. Furthermore, the notion of wine consumption for health‐related purposes was uncovered and a linkage found with traditional Chinese medicine.

While research has been conducted on Chinese wine consumers, this paper attempts to uncover the underlying motivations for consumption and finds a linkage between wine consumption and traditional Chinese medicine. Furthermore, this paper links the traditions and beliefs of traditional Chinese medicine with a product category other than food or medicine.

There are two main objectives of this paper. The first is to analyze household consumption pattern of soyfood products. The second is to investigate effect of the United States Food and Drug Administration (FDA) allowed health claims on consumption of soyfoods.

The objectives were accomplished in two stages. In the first stage, the role of consumers' perceived attributes of soy‐based foods such as convenience of preparation and consumption, health benefits, and taste in consumers' decisions to consume soy‐based food products was investigated. In the second stage, the study analyzed whether the decision of the Food and Drug Administration to allow food manufacturers to use health claims had influenced consumers' willingness to participate in soy‐based food market or willingness to increase consumption, if they are currently consuming such foods. Lancaster's characteristics model was combined with Fishbein's multi‐attribute model to develop a soybean demand function that included perceived attributes of soyfood. Zero‐inflated negative binomial model (ZINB) was used as an empirical specification to address zero consumption of soyfood products. Data were collected using a convenience sample drawn from a Midwest college town in the United States. Two questionnaires (i.e. one with information about the FDA's decision and the other without it) were given to students taking introductory marketing courses. In total 400 questionnaires were distributed and 315 respondents returned completed questionnaires.

Attributes of soy‐based food products such as convenience and tastefulness had statistically significant effect on the consumption pattern. Consumers who perceived beneficial health attributes in soyfood products were more likely to participate in the soyfood market as well as increase consumption frequency. The results indicated that frequent users of soyfood products who were exposed to the decision of the FDA would be more inclined to increase their consumption of soy‐based foods as compared to those who were not exposed to such information. Yet the information about FDA's decision did not influence the behavioral intentions of infrequent consumers or non‐consumers.

Research evaluating the impact of government allowed health claims on food consumption pattern is scarce. This paper sets up a platform for carrying out the evaluation of such health claims by other food products.

This paper aims to develop a novel theoretical technology acceptance model, namely, for predicting acceptance of the trending technology of intelligent contracts (iContracts) in construction, which aims to integrate the data from emerging cyber-physical systems being introduced to the sector through the industry 4.0 revolution. This model includes main dimensions and critical contributing factors to assess the readiness for the iContract concept within the construction contract environment.

Through an extensive literature review, the structure of a unique theoretical technology acceptance model for iContract implementation, within construction, was developed iContract acceptance model (iCAM). Relevant themes were assessed through the lens of the technology acceptance model framework and the four accepted dimensions of the technology readiness index (TRI) concept. The main components of the model were examined with selected practitioners, with relevant experience and understanding of the iContract concept, with thematic mapping of the discussions correlated back to 12 specific iContract contributing constructs of the four adapted TRI dimensions.

The paper contributes to the body of knowledge by proposing a novel iCAM for a trending technology based on the specific requirements of iContract adoption. The interviews show that while the desire to digitalise the contractual environment exists, the readiness of the sector for such a disruptive change is unknown.

The findings and proposed conceptual iCAM offers a lens for the further development of the iContract concept by assisting practitioners to forecast digital readiness of the contract process in construction.

This study offers a unique and theoretical framework, in an embryonic field, for predicting the success of iContract implementation within construction organisations through the digital, industry 4.0 and revolution.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

Despite the difference in information sets, we are able to compare the asymptotic distribution of volatility estimators involving data sampled at different frequencies. To do so, we propose extensions of the continuous record asymptotic analysis for rolling sample variance estimators developed by Foster and Nelson (1996, Econometrica, 64, 139–174). We focus on traditional historical volatility filters involving monthly, daily and intradaily observations. Theoretical results are complemented with Monte Carlo simulations in order to assess the validity of the asymptotics for sample sizes and filters encountered in empirical studies.

The chapter considers parameter identification, estimation, and model diagnostics in medium scale DSGE models from a frequency domain perspective using the framework developed in Qu and Tkachenko (2012). The analysis uses Smets and Wouters (2007) as an illustrative example, motivated by the fact that it has become a workhorse model in the DSGE literature. For identification, in addition to checking parameter identifiability, we derive the non-identification curve to depict parameter values that yield observational equivalence, revealing which and how many parameters need to be fixed to achieve local identification. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the nonparametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture. Overall, the results suggest that the frequency domain based approach, in part due to its ability to handle subsets of frequencies, constitutes a flexible framework for studying medium scale DSGE models.