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Interest rate risk, i.e. the risk of changes in the interest rate term structure, is of high relevance in insurers' risk management. Due to large capital investments in interest rate sensitive assets such as bonds, interest rate risk plays a considerable role for deriving the solvency capital requirement (SCR) in the context of Solvency II. This paper seeks to address these issues.

In addition to the Solvency II standard model, the author applies the model of Gatzert and Martin for introducing a partial internal model for the market risk of bond exposures. After introducing calibration methods for short rate models, the author quantifies interest rate and credit risk for corporate and government bonds and demonstrates that the type of process can have a considerable impact despite comparable underlying input data.

The results show that, in general, the SCR for interest rate risk derived from the standard model of Solvency II tends to the SCR achieved by the short rate model from Vasicek, while the application of the Cox, Ingersoll, and Ross model leads to a lower SCR. For low‐rated bonds, the internal models approximate each other and, moreover, show a considerable underestimation of credit risk in the Solvency II model.

The aim of this paper is to assess model risk with focus on bonds in the market risk module of Solvency II regarding the underlying interest rate process and input parameters.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet unexploited capabilities of PLS-SEM are a useful tool in the often explorative state of research in management accounting. After reviewing eleven top-ranked management accounting journals through the end of 2013, 37 articles in which PLS-SEM is used are identified. These articles are analysed based on multiple relevant criteria to determine the progress in this research area, including the reasons for using PLS-SEM, the characteristics of the data and the models, and model evaluation and reporting. A special focus is placed on the degree of importance of these analysed criteria for the future development of management accounting research. To ensure continued theoretical development in management accounting, this article also offers recommendations to avoid common pitfalls and provides guidance for the advanced use of PLS-SEM in management accounting research.

This paper generalises the GM(1,1) direct modeling method with a step by step majorizing grey derivative's whiten values to unequal time interval sequence modeling, and proves that the new method still has linear transformation consistency of the old method. The example indicates that the new method still has gradual approaching white exponential law coincidence property. With this new method, we then model the high precision soft foundation settlement.

A relatively recent advance in analyzing longitudinal data, structural equation modeling with structured means, for examining the impact of organizational change and development interventions, is presented. Some of the limitations of current approaches to analyzing data collected from “experimental” and “control” groups are discussed, along with why structural modeling is particularly useful for real‐world experiments and quasi‐experiments. An illustration is then given, applying this approach to data collected from a team‐building intervention which involved 2,331 employees in 16 plants of a large garment manufacturer. Implications of the research are briefly considered.

The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.

By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.

Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).

Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.

To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

Lean six sigma (LSS) has attained a prominent position in mature organizations but small- and medium-sized enterprises (SMEs) are struggling in the proper implementation of LSS in their core business. This study aims to make a comprehensive analysis of LSS implementation barriers in SMEs so that LSS execution can be much fluent in SMEs.

This research work is carried out based on investigation of LSS barriers through extensive literature review. For validating the identified barriers, a questionnaire survey was conducted, and out of 400 samples, 260 responses received back. The collected responses are analyzed statistically and found 16 significant barriers. The finalized barriers are modeled using interpretive structural modeling (ISM) and clustered them through matrice d’impacts croisés-multiplication appliquée a un classement (MICMAC) analysis. Furthermore, to check the consistency of results, ISM-MICMAC outcomes are validated through structural equation modeling (SEM).

The result reveals that 16 LSS implementation barriers are finalized through expert’s opinion and validated through statistical reliability test with Cronbach’s alpha value of 0.820. The ISM model reveals that the management relevant barriers are exhibiting the leading role to influence the implementation of LSS in SMEs. Moreover, the obtained results validated through SEM are found in good agreement.

During pairwise comparisons, there may be some prejudice and subjectivity as human judgments are engaged.

This study provides impetus to practitioners and consultant for the initiation of LSS in the business organization through tackling the LSS barriers as per their driving and dependence power.

In the past, limited studies had explored the LSS barriers, but a few studies analyzed the mutual relationship between barriers. No such study is reported in literature that validates the mutual interaction model of LSS barriers. Hence, this paper presents the original research work of identification and modeling of barriers associated with LSS implementation in SMEs through hybrid ISM-SEM approach.

This paper seeks to explore how moderation can and should be modeled in cross‐national/cultural contexts. A multi‐national study of consumer involvement is utilized to demonstrate proper methods for modeling the different types of moderation.

Using data from a consumer survey regarding wine purchasing preferences conducted in Australia, New Zealand and the USA, the paper demonstrates how to identify moderators of form and of strength. A form moderator is modeled using multiplicative interactions while a strength moderator is modeled using multi‐group analyses in structural equation modeling (SEM). Differences in consumers across the three countries are examined from the results.

This study suggests that search behavior is positively influenced by involvement in New Zealand and the USA but not in Australia. It also shows that perceived risk of occasion decreases involvement in all three countries, while partial support for the positive effects of importance of tradition on involvement is found. Furthermore, “perceived risk of occasion,” identified as a moderator of form, is found to significantly moderate the relationship between importance of tradition and involvement in the US sample only. Finally, the results demonstrate significant differences across the three samples in relationships among importance of tradition, perceived risk of occasion, involvement, and search behavior, indicating that the country variable has significant moderator effects.

Understanding form vs strength moderation is important when evaluating multi‐national/cultural differences so that proper methodology can be utilized. This paper provides international marketing researchers with guidelines on how to model interactions and multi‐group comparisons using SEM.