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This F.I.D. restricted conference was, from the point of view of the U.D.C., almost too ambitious in its programme, considering that meetings of the Council and of the experts on building classification were held concurrently throughout the week. Inevitably, the more general topics, such as classification rules (section 1) and notation principles (section 2), received rather scant attention, but the results from many of the U.D.C. special Sub‐committee meetings showed that they had laid an excellent basis for greatly improved work and more rapid development in the future.

Recently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare.

The paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings.

Based on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential.

This study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.

Stock markets are highly sensitive to foreign and domestic events. Stock exchange markets react promptly to news and are known as an indicator of good and bad trading conditions. Terrorist attacks leave adverse effects on the economy and cause stock price volatility and, consequently, stock return volatility. Therefore, this paper aims to analyze the spatial effects of terrorism on stock market returns in the Middle East from 2008 to 2019.

This paper uses analytical research design and estimates spatial model. Before estimating the spatial model, the spillover effects were confirmed for the spatial autoregressive model using Moran’s diagnostic test for spatial dependence, Geary’s C test and Akaike statistic.

The results of this study on spatial panel data and based on spatial autoregressive estimator indicated that terrorism and associated neighborhood effects had a negative impact on stock returns in Middle East countries. Also, the corruption index and oil price negatively affected stock market return in these countries, while the democracy index had a positive effect on stock market returns. According to the results, to achieve a high and stable stock market return, it is recommended that high-level consultation is pursued with leaders of involved countries to reduce the devastating effects of terrorist activities, increase political and economic stability, attract stockholders to stock markets and spend corresponding incomes developing the infrastructures in this sector in countries of this region.

Most of the studies have investigated the impact of terrorism on the stock market returns at the national or provincial level. However, the effect of terrorism on the stock market index in the tense region of the Middle East, which is the center of terrorist attacks in the world, has not been dealt with by considering the spatial econometric effects. Thus, to the best of the authors’ knowledge, this research is a first attempt to study the impact of terrorism on the stock market returns in Middle East countries using the spatial econometric approach.

This study examines the association between geopolitical risk (GPR) and corporate tax, which is a major source of revenue for the government and a significant explicit cost for firms. The authors use a comprehensive measure of GPR to study its effects on corporate taxes by using an international sample.

The authors adopt the geopolitical measure constructed by Caldara and Iacoviello (2022) as a proxy for GPR and cash-effective tax rate benchmarked with statutory tax rate to measure corporate tax avoidance. The authors employ panel regression with fixed effects (FEs) to investigate the impact of GPR on corporate tax avoidance. The authors also conduct a battery of robustness tests to ensure the strength of the study’s results.

This study’s empirical results indicate that sample firms increase their tax avoidance amid increasing GPR. Further analyses show that financial constraints incentivize firms to avoid taxes during rising geopolitical tensions. The authors also provide evidence on the role of firm-level and country-level governance in weakening the association between GPR and tax avoidance.

Policymakers and governments may strengthen the enforcement rule to limit aggressive tax practices of corporates during GPR to balance fiscal deficit. In addition, this study sheds light on the debate among administrators and politicians over the efficacy of current tax laws and governance structures in the presence of heightened GPR.

The authors extend the literature on GPR by analyzing its effect on corporate tax avoidance. Unlike existing single-country studies, the authors use a cross-country setup to investigate the impact of GPR on tax avoidance, making this study’s results more generalizable as the authors control for a host of country, industry, and time factors. Apart from political uncertainty, terrorism, and climatic issues, the authors document GPR as a strong macroeconomic driver of corporate tax avoidance. The authors make a new contribution to the literature on the moderating role of governance and institutional factors on the association between tax avoidance and GPR in an international context. The authors also contribute to the literature on macroeconomic determinants of tax avoidance.

This paper tests the degree to which Sunstein's law of group polarization predicts the increase or decrease in polarization among individuals in an out-group during a polarizing event. The authors use the discourse on Parler surrounding the events of January 6th as a case study.

The study includes an overall sentiment analysis, a statistical analysis of emotions, along with eight other feelings, including anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Specifically, the authors measure the differences in feelings related language used in posts as they pertain to Donald Trump and the Make America Great Again (MAGA) movement vs. Trump's Vice President Mike Pence both before and after January 6, 2021. The authors use this empirical analysis to show whether polarization in the Parler community increased or decreased after January 6th.

The authors find evidence that there is more complexity to polarization than Sunstein's theory would predict. The authors would expect a very polarized outed group to become more polarized relative to the general public after a central event; however, the authors see two extremes emerging within the Parler community (both strongly positive and strongly negative feelings toward Trump). The authors do not see unanimous consent across the Parler platform as Sunstein's theory would suggest; the out-group is becoming more polarized relative to the rest of the population. Instead, the authors observe a wide mix in reactions. The results of this study demonstrate that there is dissent even among the Parler echo chamber. For many themes surrounding the January 6th riots, Parler users express strong disagreement with each other and a lack of unity in their feelings for former President Trump.

The results suggest further research into polarization of outed groups and the policy implications of their polarization changes over time.

Increases in group polarization are often a motivator for public policy and are further becoming a major focus for research. Brookings' authors Stephanie Forrest and Joshua Daymude point to polarization as a substantial threat to American society, claiming “reducing extreme polarization is key to stabilizing democracy” (2022). Researchers Diana Epstein and John D. Graham demonstrate that polarized politics has impacted the “substance of rulemaking, judicial decisions, and legislation” along with “complicating long-term policy changes” (2007). The authors study how entrepreneurs have responded to this increase in polarization and its implications for public policy.

Not only does group polarization impact all types of groups, from the social to the economic, but also it has “particular implications for insulated ‘outgroups’” (Sunstein, 1999, p. 21). Groups that are excluded by either coercion or choice from dialog with other groups become even more polarized and extreme (Sunstein, 1999; Turner et al., 1989).

The authors have engaged in an empirical analysis that no other paper has addressed. This paper summarized the Parler sample data set and analyzed various themes associated with the events of January 6th, namely President Trump and MAGA themes and Vice President Pence. The analysis demonstrated a dramatic increase in negative sentiment and emotion related to Vice President Mike Pence after January 6th as well as mixed support for President Trump and an increase in disgust before and after the Capitol riot.

The problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection.

To overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models.

Empirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification.

The FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.

ANY system based on a standard unit capable of being divided by tenths or hundredths for any lesser value enormously simplifies calculations and through a saving of time and labour reduces costs. It is therefore a matter for satisfaction that this country, laggard as usual, will within a matter of days fall into step with other industrial nations. Indeed, as the official guide to the decimal system points out, almost every country in the world uses it. So Britain at last joins the great majority.

This study aims to apply unit root test to investigate the behavior of Chinese firms toward their leverage policy. The study is based on two influential and competing theories of capital structure.

This study applies unit root test to investigate the behavior of Chinese firms toward their leverage policy. The study is based on two influential and competing theories of capital structure. Trade off theory advocates that firms have a target level of leverage ratio and that firms try to achieve that optimal leverage ratio, whereas pecking order theory argues that firms have no target level of leverage and that they follow a specific pattern of leverage. For this purpose, this study applies a Fisher type unit root test to 12,808 firm level observations. The data are unbalanced and cover a period from 1991 to 2014.

The results reveal the presence of a stationary behavior across short-term, long-term and total leverage policies. For short-term leverage policy, 21 per cent firms show stationary behavior, while for long-term, 20 per cent show a targeting behavior; for the total leverage policy 17 per cent of firms are found to follow a tradeoff model. To make the findings more interesting sample was further classified into profit and loss making firms. The study finds that loss making firms do not follow a target level of leverage in China. Furthermore, unit root is applied to all firms before and after crises-2008. It is revealed that stationary behavior is more prevalent before crises-2008.

This study is highly important from the point of view that it quantifies firms into distinct categories of following specific model of capital structure. To the best of the author’s knowledge, the findings of this study add to current research knowledge about Chinese firms with respect to adjustment behavior toward a target capital structure.

This article aims to propose a new measurement method for ultrasonic power based on self-reciprocity theorem which turns the estimation of ultrasonic power to the measurement of first echo current and open-circuit voltage of the driving source.

The formula for ultrasonic power is derived which has corrected the position of pressure reflection coefficient on the interface of water and steel. The diffraction correction for focusing transducers is evaluated using numerical computation of the Rayleigh integral. One way to estimate the reflection coefficient of focusing beams on heterogeneous interface is also depicted.

Comparison experiment with radiation force balance method demonstrates that ultrasonic power measurement using self-reciprocity is sound in theory and feasible in practice.

It has a better capability of anti-environmental interference and, thus, can be extended to low-level and high-frequency power measurements.

Current literature provides limited insights into the supply chain contexts within which e-commerce can create higher value for firms. To address this literature gap, this research explores the value potential, and thus value creation process, of e-commerce initiatives for supply chain distribution channel expansions.

Using secondary data collected from multiple sources, this research conducted an event study to examine the stock market reactions to the announcements of e-commerce initiatives of Chinese firms.

The results indicate that the e-commerce initiatives increase average firm value by CNY 295.29 million in a three-day window around the initiative's announcement date. Moreover, we find that such stock market reactions are more positive for firms with poor operating performance, and more negative when firms deploy initiatives on their own (rather than third-party) platforms. Further, companies that integrate or complement their online sales with an offline sales channel experience more positive stock market reactions.

This study provides new insights into the value creation process of e-commerce from an operation and supply chain process perspective.