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This study investigated the impact of corporate ownership structure on agency costs in the insurance industry.

The study sample included 23 insurance companies listed on the Amman Stock Exchange (ASE) from 2010 to 2019. Panel regression was used to account for the firm- and time-specific unobservable variables and system-GMM estimation was used to address endogeneity concerns.

The results show that managerial ownership positively (negatively) affects selling, general and administrative (SG&A) expenses (assets turnover), implying that unmonitored managers engage in activities that serve their own interests rather than those of shareholders. The largest shareholder's ownership has no impact on agency costs, implying that the ownership of the largest shareholder is irrelevant. However, as the wedge between the percentage of capital owned by the largest shareholders and managers increases, SG&A expenses (efficiency ratio) decrease (increases), indicating that the existence of large non-management shareholders reduces agency costs. After accounting for the endogeneity problem, the impact of ownership structure on agency costs measured by asset turnover remains robust.

To the best of the authors' knowledge, this study is the first to provide unique evidence and useful insights into the determinants of agency costs from a frontier market in the Middle East and North Africa (MENA), with a focus on the insurance sector. Additionally, this study uses a new measure of separation between ownership and control by calculating the wedge between managers' and large shareholders' ownership.

The purpose of this study is to find out the awareness, attitude and career preference of commerce students (undergraduate (UG) and post-graduate (PG)) for the insurance industry in India.

The data were collected from 800 commerce students (400 male students and 400 female students) through a structured questionnaire. The questionnaire had 18 items related to awareness, attitude and career preference for insurance. The reliability of the tool was assessed by Cronbach’s alpha. To establish the relationship between variables, cross-tabulation techniques that involved Chi-square tests were used. The conclusion was drawn based on probability values (p-value) taking the critical as 0.05 (Bivariate). The data was analyzed using SPSS.

The results revealed that commerce students-UG and PG were aware of the basics of insurance, irrespective of their gender and family income. The students have a positive attitude toward insurance, but lack awareness regarding career options in the insurance industry.

This study included only UG and PG commerce students of Allahabad University and its constituent colleges, hence its findings cannot be generalized for the entire country.

This study can be beneficial to insurance companies in framing their policies as India has a huge young population. There is a need to make the students in higher education aware of the benefits of insurance to cover any unforeseen economic loss and also to make them aware of the career options in the insurance industry.

The present study bridges the gap between existing studies regarding awareness, attitude and career preference of students with gender and family income. To date, no study has been done to find the awareness and attitude of students toward the insurance industry, neither in terms of their becoming prospective customers nor in terms of career preference.

The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method.

In this paper, the authors adopted the incurred claims of Egyptian non-life insurance market as a dependent variable during a 10-year period. MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate the parameters of interest. However, the authors used the R package to estimate the parameters of the linear regression using the above techniques.

These procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.

In this paper, the authors will estimate the parameters of a linear regression model using MCMC method via R package. Furthermore, MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate parameters to predict future claims. In the same line, these procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.

Despite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The Z-score measure, which has traditionally been used as a proxy of individual risk in the banking sector, may be a useful tool when applied in the insurance sector. However, different methods for calculating this indicator have been proposed in the literature. This paper compares six different Z-score approaches to examine which one best fits insurance companies. The authors use a final dataset of 183 firms (1,382 observations) operating in the Spanish insurance sector during the period 2010–2017.

In the first stage, the authors opt for a root mean squared error (RMSE) criterion to evaluate which of the various mean and SD estimates that are used to compute the Z-score best fits the data. In the second stage, the authors estimate and compare the explanatory power of the six Z-score measures that are considered by using an ordinary least squares (OLS) regression model. Finally, the authors report the results of the baseline equation using the system-GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) for dynamic panel data models.

The authors find that the best formula for calculating the Z-score of insurance firms is the one that combines the current value of the return on assets (ROA) and capitalization with the SD of the returns calculated over the full sample period.

The main limitation of the research is that it addresses only the Spanish insurance sector, and consequently, the implications of the findings must be framed in this institutional context. However, the authors think that the results could be extrapolated to other countries. Future research should consider including different countries and analyzing the usefulness of aggregated insurer-level Z-scores for macroprudential monitoring.

The Z-score may be a useful early warning indicator for microprudential supervision. In addition to being an indicator of the soundness of insurers simpler than those established in the current regulation, the information provided by this accounting-based measure may help analysts and investors obtain a better understanding of insurance firms' risk factors.

To the best of the authors’ knowledge, this study is the first to examine and compare different approaches to calculating Z-scores in the insurance sector. The few available results on the predictive power of the Z-score are mixed and focus on the banking sector.

雖然在償付能力標準II 內已建立了精密的監管制度，但分析人員應可以考慮以不太複雑的指標，來分析保險公司的穩健程度。Z-分數的估量在銀行業一向作為是個體風險的代理而使用，而Z-分數如應用於保險業，或許會成為有用的工具。唯在文獻裏，學者和研究人員提出了不同的方法來計算這個指標。本文比較六個不同的Z-分數估量方法，以研究出最適合保險公司的方法。我們使用一個最終數據集，包括在2010年至2017年期間在西班牙保險業界營運的183間公司（1382 個觀察）。

在首個階段，我們選擇使用一個方均根誤差(RMSE) 標準來衡量用來計算Z-分數的各個平均值和標準差估量中哪個最適合使用於有關的數據。在第二個階段， 我們以普通最小平方 (OLS) 迴歸模型，去估計並比較被考慮的六個Z-分數估量的解釋力。最後，我們以Arellano與Bover (1995), 以及Blundell與Bond (1998) 為動態追蹤資料模型而發展出來的系統-廣義動差估計推定量，來發表我們基線方程式的結果。

我們發現，計算保險公司Z-分數的最佳公式是把資產收益率及資本總額的現值，和在整個樣本期間計算出來的囘報的標準差結合起來的公式。

我們研究主要的局限為：研究只涉及西班牙的保險業；因此，研究結果的含意，必須在這個體制的背景框架下來闡釋。唯我們相信研究結果或許可外推至其它國家。未來的研究，應考慮納入不同國家作為研究對象，並分析保險公司層面的集成Z-分數的功用，以求達到宏觀審慎監控的目的。

Z-分數或許就微觀審慎監管而言是一個有用的早期警告器。這些以會計為基礎的估量而提供的資訊，除了較現時規例内已建立顯示保險公司穩健程度的各個指標更簡單外，還會幫助分析人員和投資者更了解保險公司的風險因素。

據我們所知，本研究為首個研究，去探討並比較保險業內的Z-分數的計算方法。以前關於Z-分數預測能力的，為數不多並可供取閱的研究結果均不統一；而且，這些研究都聚焦探討銀行業。

This paper aims to examine the types of user misbehaviours in the sharing economy (SE) context. SE offers a fruitful study setting due to the scope of potential misbehaviour and the expanded role of consumers.

The study drew on online archival data from the AirbnbHell.com website, where people share their stories about their Airbnb-related negative experiences. The authors reviewed 405 hosts’, guests’ and neighbours’ stories and coded the identified forms of misbehaviours into categories. The typology thus developed was validated in the context of the Uber Rides service.

User misbehaviours in the SE context can be distinguished based on the domain in which the user role is violated and the nature of violated norms. These two conceptual distinctions delineate a four-fold typology of user misbehaviours: illegal, unprofessional, unbefitting and uncivil behaviours.

The trustworthiness of the stories could not be assessed.

The presented typology can be used as a mapping tool that facilitates detection of the full scope of misbehaviours and as a managerial tool that provides ideas for effective management of misbehaviours that correspond to each category.

The paper presents the first empirically derived comprehensive typology of user misbehaviours in SE settings. This typology enables classification of a broad set of misbehaviours, including previously overlooked unprofessional behaviours carried out by peer-service providers. The study also puts forward a revised definition of consumer misbehaviours that encompasses the impact of misbehaviours on parties not directly involved in the SE-mediated exchange.