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How can governments and survivors prepare for and manage natural disasters? Post-disaster reconstruction researchers advocate that community involvement is needed to help survivors recover effectively. This study aims to propose that cooperative goals between government officials and survivors develop guanxi relationships and constructive controversy wherein survivors voice their opinions to aid disaster recovery.

The authors adopted the critical incident technique (CIT), which has proved especially useful for studying complex issues, as well as site-intensive research for interviews and participant observation. After developing a local reputation and rapport by working in a residential resettlement area for two months, an author used the CIT to ask 118 survivors of the 2008 Sichuan earthquake to describe specific incidents when they interacted with government officials about recovery issues and then to rate survey items that measure independent and dependent variables.

Results, including structural equation analyses, support the reasoning that cooperative goals between government and survivors facilitate guanxi and constructive controversy, which in turn produced effective disaster recovery, as indicated by survivors’ social support, satisfaction, reduced stress and beliefs that government officials led effectively.

The data are self-reported and subject to biases and may not be accurate.

In addition to developing theoretical understanding, the results can have important practical implications for strengthening relationships and constructive controversy between government and survivors.

Results suggest that communities have practical ways to prepare for disasters. Structuring cooperative goals among survivors, encouraging guanxi relationships, and training in constructive controversy should promote effective recovery from natural disasters.

The paper develops theory and research on how leaders can promote community involvement for effective disaster management. The paper proposes that high-quality interpersonal relationships, also referred to guanxi, and the open-minded discussion of opposing ideas, labeled constructive controversy, are major components of effective community involvement. The paper adds to the literature by empirically documenting the utility of the Western-developed theory of cooperation and competition and the concept of constructive controversy for understanding the effectiveness of government leadership for disaster recovery in China.

This study aims to propose a series of numerical and surrogate models to investigate the aerodynamic pressure inside cracks in high-speed railway tunnel linings and to predict the stress intensity factors (SIFs) at the crack tip.

A computational fluid dynamics (CFD) model is used to calculate the aerodynamic pressure exerted on two cracked surfaces. The simulation uses the viscous unsteady κ-ε turbulence model. Using this CFD model, the spatial and temporal distribution of aerodynamic pressure inside longitudinal, oblique and circumferential cracks are analyzed. The mechanism behind the pressure variation in tunnel lining cracks is revealed by the air density field. Furthermore, a response surface model (RSM) is proposed to predict the maximum SIF at the crack tip of circumferential cracks and analyze its influential parameters.

The initial compression wave amplifies and oscillates in cracks in tunnel linings, resulting from an increase in air density at the crack front. The maximum pressure in the circumferential crack is 2.27 and 1.76 times higher than that in the longitudinal and oblique cracks, respectively. The RSM accurately predicts the SIF at the crack tip of circumferential cracks. The SIF at the crack tip is most affected by variations in train velocities, followed by the depth and length of the cracks.

The mechanism behind the variation of aerodynamic pressure in tunnel lining cracks is revealed. In addition, a reliable surrogate model is proposed to predict the mechanical response of the crack tip under aerodynamic pressures.

Background music is considered an intangible element but has a close attachment to emotional reaction and memory. Background music is constantly present in our everyday lives, whether for distraction, recreation or mood enhancement. It can be heard in the supermarket, in lifts, cafés or hotels. Music has been identified as important in the construction of autobiographical memories and emotions of individuals. Many premises use music to enhance customers' emotions, and hoteliers try to use music in their lobby to increase the likelihood of customer experience. The purpose of this chapter is to examine the impact of background music as an intangible element in hotel lobbies on customer satisfaction. More specifically, this study aims to draw a connection between the musical variables (musicscape) in hotel lobbies with regard to the gender and age of guests and how hotel businesses can make use of this intangible element to enhance their guest's satisfaction. Sound marketing is an overlooked area in hospitality and tourism research. A mixed-method approach has been employed in this study, including a questionnaire and online interviews. The result shows that background music in hotel lobbies has a significant impact on customer satisfaction and the time they are willing to spend in the lobby. Moreover, different musical variables have obvious influences on the experience of guests of different ages and genders. The results of this study provide theoretical and managerial recommendations on the importance of sound marketing in a hotel lobby setting.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

The purpose of this paper is to comprehensively identify and synthesize the mental health care provided through diverse community schools implemented in the USA.

Using PubMed, community school model websites and Google Search, we identified 21 community school models that publicly reported outcomes and conducted full review of these models. The authors also conducted e-mail and telephone communication with ten program directors and evaluators to gain insights into successes and lessons learned through implementing community school models based on community partnership.

Provision of mental health care though community schools leads to reducing school suspensions, disciplinary referrals, problem presentation, and risk behaviors, and improving school grades, personal responsibility, future aspiration, and family engagement.

Developing standardized outcome measure for the evaluation of mental health care provided through community school models is important to establish evidence that leads policymakers and practitioners into action. Information toolbox to guide mental health administrators and practitioners about future funding and partnership mechanisms for successful implementation and sustained mental health care through community school models can be useful.

This systematic literature review provides insights into the current practice and future direction in the provision and evaluation of mental health care through community school models and addresses concrete research and practical implications to guide mental health professionals.

This study aims to explore the perception of algorithm accuracy among data professionals in higher education.

Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended questionnaires and six semi-structured interviews. Participants included higher education professionals who have worked with predictive algorithm (PA) recommendations programmed with student data.

Participants are aware of systemic and racial bias in their PA inputs and outputs and acknowledge their responsibility to ethically use PA recommendations with students in historically underrepresented groups (HUGs). For some participants, examining these topics through the lens of social justice was a new experience, which caused them to look at PAs in new ways.

Small sample size is a limitation of the study. Implications for practice include increased stakeholder training, creating an ethical data strategy that protects students, incorporating adverse childhood experiences data with algorithm recommendations, and applying a modified critical race theory framework to algorithm outputs.

The study explored the perception of algorithm accuracy among data professionals in higher education. Examining this topic through a social justice lens contributes to limited research in the field. It also presents implications for addressing racial bias when using PAs with students in HUGs.

The purpose of this paper is to understand consumers’ preferences for answers about sexually transmitted diseases on social question and answer (Q&A) sites by employing message features and information sources as conceptual frameworks.

The study compared best answers selected by questioners with their randomly drawn counterpart non-best answers on Yahoo! Answers as a paired sample (n=180).

The findings indicate that questioners on social Q&A sites were more likely to prefer answers including message features such as numeric information, social norms, optimistic information, and loss-framing, as well as information sources that featured expertise, references, and links to other websites. Pessimistic information was negatively associated with questioners’ preference for answers.

The study extended the discussion of consumers’ selection of best answers to message features and information sources as additional criteria.

The findings suggest that answerers on social Q&A sites communicate more effectively with their audiences by utilizing persuasive communication.

There is a quality issue on social Q&A sites. The findings will be helpful for health professionals to develop answers that are more likely to be selected as best answers, which will enhance overall quality of health information on social Q&A sites.

Consumers’ preference criteria for health information have been investigated using many different approaches. However, no study has used a persuasion framework to examine how consumers appraise answer quality. The present study confirmed consumers’ preference criteria as found in previous social Q&A studies and extended the discussion of consumers’ perceptions of answer quality by applying the frameworks of message features and information sources.

Charting the earnings numbers reported by Korean firms produces a bell curve, but for a sharp discontinuity in the area surrounding zero. The purpose of this paper is to investigate if and how a large segment of Korean managers might manage accounting numbers to produce the observed result.

This study adopts an empirical research method using Korean listed firms as a sample. The primary focus of investigation is on major income statement variables that might produce the observed results in earnings from operations and net income.

Managers of Korean firms opportunistically use almost all income statement variables to influence earnings numbers. They manage revenues and selling, general & administrative expenses to report small positive earnings from operations, but manage non-operating gains (losses) to report small positive net income.

This paper does not answer several questions related to loss avoidance. First, the paper did not examine which actions, such as discretionary accruals, opportunistic business decisions, or bogus transactions, were employed to affect line items on the income statement. Second, the paper did not investigate what specific incentives trigger Korean managers to report small positive earnings. Korean firms have traditionally raised capital by borrowing funds from creditors and governmental agencies. Thus, they may be concerned that reporting losses would reduce their borrowing capacity. Finally, corporate governance, such as CEO tenure and option grants may influence the extent of earnings management to avoid losses, but most corporate governance data for Korean companies must be manually collected. Accordingly, these subjects are left for future studies as well.

This study contributes to accounting literature by reporting how managers of Korean firms artificially coordinate major income statement variables and report small positive earnings figures, noting the differences between earnings management investigating methodology and ones used in previous studies.

The purpose of this paper is to propose a novel decoding algorithm, to decrease the complexity in decoding conventional block turbo codes.

In this algorithm, the signal‐to‐noise ratio (SNR) values of channels are adaptively estimated. After analyzing the relationship between the statistics of the received vectors R and the channel SNR, an adaptive method of tuning the decoding complexity is presented.

Simulation results show that the proposed algorithm has greatly decreased the decoding complexity and sped up the decoding process while achieving better bit error rate performance.

Simulation experiments described in this paper show that the proposed algorithm can decrease the decoding complexity, shorten the decoding time and achieve good decoding performance.