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The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

This paper generalizes the quadratic framework introduced by Le Courtois (2016) and Sumpf (2018), to obtain new credibility premiums in the balanced case, i.e. under the balanced squared error loss function. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.

In the actuarial field, credibility theory is an empirical model used to calculate the premium. One of the crucial tasks of the actuary in the insurance company is to design a tariff structure that will fairly distribute the burden of claims among insureds. In this work, the authors use the weighted balanced loss function (WBLF, henceforth) to obtain new credibility premiums, and WBLF is a generalized loss function introduced by Zellner (1994) (see Gupta and Berger (1994), pp. 371-390) which appears also in Dey et al. (1999) and Farsipour and Asgharzadhe (2004).

The authors declare that there is no conflict of interest and the funding information is not applicable.

This work is motivated by the following: quadratic credibility premium under the balanced loss function is useful for the practitioner who wants to explicitly take into account higher order (cross) moments and new effects such as the clustering effect to finding a premium more credible and more precise, which arranges both parts: the insurer and the insured. Also, it is easy to apply for parametric and non-parametric approaches. In addition, the formulas of the parametric (Poisson–gamma case) and the non-parametric approach are simple in form and may be used to find a more flexible premium in many special cases. On the other hand, this work neglects the semi-parametric approach because it is rarely used by practitioners.

There are several examples of actuarial science (credibility).

In this paper, the authors used the WBLF and a quadratic adjustment to obtain new credibility premiums. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.

The study's objective is to estimate the association of specific perceived employer-provided benefits on employees' intention to leave in different age cohorts during coronavirus disease 2019 (COVID-19). Informed by the psychological theories of ageing, the authors propose three age-cohort-specific hypotheses in three motivational domains: security and health benefits, flexible work arrangement and education-related benefits.

The authors use a large survey of employees in Estonia (n = 7,209) conducted in 2020 and test the association of specific benefits and their interactions with age on employees' intention to leave.

The results show that older cohorts are generally less prone to leave their jobs. Benefits that employers could use during the COVID-19 crisis generally had negative associations with the intention to leave, but age-specific differences were negligible; only the perceived provision of flexible work arrangements reduced the younger cohort's intention to leave relatively more.

This study is one of the few that allows us to make inferences regarding the benefits preferences amongst the working population during an unprecedented health crisis.

This study aims to empirically analyze the asset allocation capabilities of Outsourced Chief Investment Officers (OCIOs) in Korea. The empirical analysis used data from 35 funds that were evaluated by the Ministry of Strategy and Finance from 2012 to 2020. The results of the analysis are summarized as follows. First, this study found that funds that adopted OCIO improved their asset allocation performance. Second, the sensitivity between risk-taking and performance decreased for funds that adopted OCIO. Third, it is found that OCIO adoption improves a fund's asset management execution (tactical capabilities). This study has methodological limitations in which the methodology used in this study is not based on theoretical prior research, but on practical applications. However, considering the need to clearly analyze the capabilities of OCIO and the timeliness of the topic, this study is valuable and can provide meaningful information to funders who are considering adopting OCIO in the future.

While previous literature has emphasized the causal relationship from liquidity to capital, the impact of interbank network characteristics on this relationship remains unclear. By applying the interbank network simulation, this paper aims to examine whether the causal relationship between capital and liquidity is influenced by bank positions in the interbank network.

Using the sample of 506 commercial banks established in 28 European countries from 2001 to 2013, the author adopts the generalized method of moments simultaneous equations approach to investigate whether interbank network characteristics influence the causal relationship between bank capital and liquidity.

Drawing on a sample of commercial banks from 28 European countries, this study suggests that the interconnectedness of banks within interbank loan and deposit networks shapes their decisions to establish higher or lower regulatory capital ratios in the face of increased illiquidity. These findings support the implementation of minimum liquidity ratios alongside capital ratios, as advocated by the Basel Committee on Banking Regulation and Supervision. In addition, the paper underscores the importance of regulatory authorities considering the network characteristics of banks in their oversight and decision-making processes.

This paper makes a valuable contribution to the current body of research by examining the influence of interbank network characteristics on the relationship between a bank’s capital and liquidity. The findings provide insights that add to the ongoing discourse on regulatory frameworks and emphasize the necessity of customized approaches that consider the varied interbank network positions of banks.

Renal failure is an end-stage consequence after persistent hyperglycemia during diabetic nephropathy (DN), and the etiology of DN has been linked to oxidative stress. The purpose of this research was to determine the beneficial synergistic effects of S-Allyl Cysteine (SAC) and Taurine (TAU) on oxidative damage in the kidneys of type 2 diabetic rats induced by hyperglycemia.

Experimental diabetes was developed by administering intraperitoneal single dose of streptozotocin (STZ; 65 mg/kg) with nicotinamide (NA; 230 mg/kg) in adult rats. Diabetic and control rats were treated with SAC (150 mg/kg), TAU (200 mg/kg) or SAC and TAU combination (75 + 100 mg/kg) for four weeks. The estimation of body weight, fasting blood glucose (FBG), oral glucose tolerance test (OGTT), oxidative stress markers along with kidney histopathology was done to investigate the antidiabetic potential of SAC/TAU in the NA/STZ diabetic group.

The following results were obtained for the therapeutic efficacy of SAC/TAU: decrease in blood glucose level, decreased level of thiobarbituric acid reactive substances (TBARS) and increased levels of GSH, glutathione-s-transferase (GST) and catalase (CAT). SAC/TAU significantly modulated diabetes-induced histological changes in the kidney of rats.

SAC/TAU combination therapy modulated the oxidative stress markers in the kidney in diabetic rat model and also prevented oxidative damage as observed through histopathological findings.

This paper examines whether financial distress is associated with tax avoidance and whether the COVID-19 pandemic moderates such association.

The sample covers 38,958 firm-year observations from 32 countries during the period 2015–2020. Financial distress is measured using the ZSCORE by Altman (1968), while tax avoidance is based on the book-tax difference.

Financially distressed firms exhibit low tax avoidance pre- and during the pandemic periods. The authors find higher tax avoidance during the pandemic compared to the pre-pandemic period, but the pandemic enhances the negative relationship between financial distress and tax avoidance.

The study offers evidence on how financial distress drives firms to engage in more tax avoidance when firms globally encountered various levels of financial difficulty sparked by the economic challenges of the COVID-19 pandemic.

The findings provide insights to policymakers on the need to monitor and incentivise financially distressed firms, especially during economic challenges due to pandemic.

This study adds to the limited, albeit important, evidence on the joint effect of the COVID-19 pandemic and financial distress on tax avoidance.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

Knowing the students' readiness for the fourth industrial revolution (4IR) is essential to producing competent, knowledgeable and skilled graduates who can contribute to the skilled workforce in the country. This will assist the Higher Education Institutions (HEIs) to ensure that their graduates own skill sets needed to work in the 4IR era. However, studies on students' readiness and preparedness for the 4IR in developing countries such as the Sultanate of Oman are still lacking. Therefore, this study investigates students' readiness level and preparedness for the 4IR. The findings of this study will benefit the HEIs policymakers, administration, faculties, departments, industries and society at large since they will be informed of the student's readiness and preparedness toward industry 4.0.

The authors adopted the measures from the same context as previous studies in this study. The questionnaire was divided into three sections; the first part described the purpose and introduction of the search with the surety to keep the data confidential. The second part consisted of demographical information like gender, education. The last parts consisted of four subsections, question items in these parts are based on the related previous study. Characteristics consisted of 14 items, knowledge consisted of 18 items related to 4IR technologies, Organizational Dimension comprised of four items related to academic programs, curriculum and training. Preparedness contained two items. The participants have rated all the items in 5-Likert scale.

Results from structural equation modeling showed that students' characteristics, knowledge of 4IR technologies and organizational dimensions significantly impact their preparedness for the 4IR. The study also found that organizational dimensions have the highest impact on students' preparedness. Furthermore, the organizational dimension significantly influences students' knowledge of 4IR technology. Moreover, students' characteristics related to 4IR are significantly affected by their knowledge of 4IR technology and organizational dimension. The findings suggest that HEIs are responsible for increasing the adoption of 4IR, and therefore organizational dimensions such as the academic programs, training, technological infrastructure and others are all critical for preparing students for a better future and should be given a priority.

This study has used academic programs and training to measure the organizational dimension. However, other important factors should be considered, such as technological infrastructure and leadership and governance of HEIs. Second, the current research depends on quantitative data, so future research should implement a mixed methodology (questionnaires, depth interviews, document analysis and focus group) to understand the factors affecting students' readiness for 4IR clearly. Finally, although the 4IR has numerous benefits, it also has challenges in its implementation, so future studies should focus on challenges encountered by different stakeholders in implementing 4IR-related technologies.

The curriculum must include mandatory courses related to IT infrastructure design, user experience programming, electronic measurement and control principles, and programming for data science. HEIs should also foster interdisciplinary knowledge by integrating IT, Engineering, Business and Sciences. Furthermore, the HEIs should develop their infrastructure to have smart campuses, labs, classrooms and libraries to make HEIs a space where knowledge can be generated and innovative solutions can be proposed. This entails HEIs offering necessary hardware, software and technical support because if the HEIs improve their technological resources, students will be capable of using 4IR-related technologies effectively.

The advancement of technology has resulted in the emergence of the Fourth Industrial Revolution (4IR), such as artificial intelligence, blockchain, robotics, cloud computing, data science, virtual reality and 3D printing. It is essential to investigate students' readiness for 4IR. However, there is no study as per researchers' knowledge talked about students readiness in HEIs in the Arab world. This study could be a basis for more research on students' perception of the 4IR covering students from various backgrounds and levels.