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Using the L. Bebchuk, Cohen, and Ferrell (2009) entrenchment index (E-index), the authors examine the relation between management entrenchment and the probability of a firm being implicated in the stock option backdating scandal. The authors conduct the analysis of this study using logistic regression, and they document a negative relation between the E-index and the probability of a firm being implicated in the stock option backdating scandal. The results of this study are consistent with the view that management entrenchment is advantageous to shareholders as it protects managers from short-term reporting pressures and egregious opportunistic behavior that can be detrimental to firm value.

Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.

Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.

Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1^st April 2010 to 21^st June 2023.

Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.

Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors that may impact users' retweet behavior, namely information dissemination in the online financial community, through machine learning techniques.

This paper crawled data from the Chinese online financial community (Xueqiu.com) and extracted author-related, content-related, situation-related, stock-related and stock market-related features from the dataset. The best information dissemination prediction model based on these features was determined by evaluating five classifiers with various performance metrics, and the predictability of different feature groups was tested.

Five prevalent classifiers were evaluated with various performance metrics and the random forest classifier was proven to be the best retweet prediction model in the authors’ experiments. Moreover, the predictability of author-related, content-related and market-related features was illustrated to be relatively better than that of the other two feature groups. Several particularly important features, such as the author's followers and the rise and fall of the stock index, were recognized in this paper at last.

This study contributes to in-depth research on information dissemination in the financial domain. The findings of this study have important practical implications for government regulators to supervise public opinion in the financial market.

Hedonic social applications have been increasingly popular among health information consumers. However, it remains unclear what motivates consumers to adopt health information in hedonic applications when they have alternative choices of more formal health information sources. Building on the self-determination theory and the affordances lens, this study aims to investigate how different affordances on hedonic social applications affect consumers' basic psychological needs and further influence their intention to adopt health information on such applications.

As TikTok demonstrated great potential in disseminating health information, we developed a model that we analyze using the PLS-SEM technique with data collected from a valid research sample of 384 respondents with health information seeking or encountering experience in TikTok.

The results suggested that health information adoption in hedonic social applications is significantly predicted by the satisfaction of consumers' basic psychological needs, namely autonomy, relatedness and competence. Moreover, the satisfaction of basic psychological needs is positively affected by affordances provided by the hedonic social applications. The hedonic affordances positively influence autonomy satisfaction, while the connective affordances positively affect relatedness satisfaction, and the utilitarian affordances positively support competence satisfaction.

The study indicates that hedonic social applications such as TikTok could be an important channel for consumers to access and adopt health information. The study contributes to the literature by proposing a theoretical model that explains consumers' health information adoption and yields practical implications for designers and service providers of hedonic social applications.

Drawing on Herzberg's motivation-hygiene perspective, this study examines the factors affecting expatriate adjustment and the relationship between expatriate adjustment and subsidiary performance of emerging market multinational enterprises (EMNEs).

Using structural equation modeling, this study tests a proposed conceptual model based on a matched dataset collected from 38 EMNEs.

The findings reveal that perceived organizational support (POS) and family adjustment are positively associated with expatriate adjustment, while remuneration and job burnout have no significant relationships with expatriate adjustment. In addition, expatriate adjustment is positively associated with the foreign subsidiary performance of EMNEs.

This study contributes to expatriate adjustment research in the EMNE context by distinguishing motivators and hygiene factors in affecting the expatriates' attitudes toward international assignments. Empirical evidence of expatriate adjustment-subsidiary performance relationship also enriches the authors’ knowledge of EMNEs' expatriation practices.

The purpose of this paper is to assess how spatial heterogeneity and production diversification have affected the efficiency of households participating in the SLCP.

Based on household survey data collected from three sample counties in the Loess Plateau region, the paper analyzes how spatial heterogeneity and business diversification have affected the production efficiency of households participating in the SLCP. Using four-step bootstrap, input stochastic distance frontiers of multi-inputs and multi-outputs are developed simultaneously with a technical efficiency effects model in which participating in the SLCP is treated as an exogenous variable.

The household production in the three counties is constrained by land, labor, capital, and fixed inputs; thus, it is germane to using a multi-input and multi-output household production technology to evaluate the SLCP. However, the relative importance of each input and output, the productive diversification, and the technological structure are differentiated in the three counties; estimating the regression with pooled data appears to conceal the influences of local factors and blur the specific divergence, compared to running county-based, separate regressions.

The effects of the SLCP on farm household production are location dependent. It is necessary to include spatial heterogeneity within public policy evaluation. In addition to suggesting localized and differentiated schemes of subsidy, this implies that the government may provide means to facilitate different regions to make the economic transition.

This paper intends to make two contributions. First, the analysis will capture and explain the adjustments induced by the SLCP in off-farm and other activities for entire household production, and special attention is given to the diversification of household production outputs. Second, this analysis sheds new light to the significance of spatial differentiation in mediating the effectiveness of a public policy or program.

Based on the error analysis, the authors proposed a new kind of high accuracy boundary element method (BEM) (HABEM), and for the large-scale problems, the fast algorithm, such as adaptive cross approximation (ACA) with generalized minimal residual (GMRES) is introduced to develop the high performance BEM (HPBEM). It is found that for slender beams, the stress analysis using iterative solver GMRES will difficult to converge. For the analysis of slender beams and thin structures, to enhance the efficiency of GMRES solver becomes a key problem in the development of the HPBEM. The purpose of this paper is study on the preconditioning method to solve this convergence problem, and it is started from the 2D BE analysis of slender beams.

The conventional sparse approximate inverse (SAI) based on adjacent nodes is modified to that based on adjacent nodes along the boundary line. In addition, the authors proposed a dual node variable merging (DNVM) preprocessing for slender thin-plate beams. As benchmark problems, the pure bending of thin-plate beam and the local stress analysis (LSA) of real thin-plate cantilever beam are applied to verify the effect of these two preconditioning method.

For the LSA of real thin-plate cantilever beams, as GMRES (m) without preconditioning applied, it is difficult to converge provided the length to height ratio greater than 50. Even with the preconditioner SAI or DNVM, it is also difficult to obtain the converged results. For the slender real beams, the iteration of GMRES (m) with SAI or DNVM stopped at wrong deformation state, and the computation failed. By changing zero initial solution to the analytical displacement solution of conventional beam theory, GMRES (m) with SAI or DNVM will not be stopped at wrong deformation state, but the stress error is still difficult to converge. However, by GMRES (m) combined with both SAI and DNVM preconditioning, the computation efficiency enhanced significantly.

This paper presents two preconditioners: DNVM and a modified SAI based on adjacent nodes along the boundary line of slender thin-plate beam. In the LSA, by using GMRES (m) combined with both DNVM and SAI, the computation efficiency enhanced significantly. It provides a reference for the further development of the 3D HPBEM in the LSA of real beam, plate and shell structures.

The purpose of this paper is to compare the effectiveness of the Buy-and-Hold (BNH), Fee-for-Service (FFS), and Direct-to-pharmacy (DTP) agreements for the US pharmaceutical industry and its individual participants. There have been mixed responses to these agreements and the industry is currently under debate as to which contract would be best for the industry and its individual participants. The question is answered by comparing the agreements and settling the industry debate regarding the impact of these distribution agreements.

The model features multi-period production-inventory planning with time varying parameters in a decentralized setting. Under each distribution agreement, mathematical programming models are formulated to determine the profit maximizing production, inventory, and ordering decisions for the manufacturer and the wholesaler in a finite time horizon. The applicability of the model in the US pharmaceutical industry using real-world data is demonstrated.

It is shown that the DTP agreement always outperforms the BNH and FFS agreements. Furthermore, the DTP agreement is flexible because it allows the manufacturer and the wholesaler to split the additional profit in an arbitrary way. The findings reveal that the DTP agreement can improve total profit by about 0.08 - 1 percent (relative to FFS) and 5 percent (relative to BNH).

Considering the size of the pharmaceutical industry, efficient distribution agreements are imperative. Unfortunately, the existing literature provides insufficient guidance to help managers make this important decision. This knowledge gap is addressed in literature, and provides important insight for practitioners on what agreement is most beneficial for this industry.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.