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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.

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.

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.

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.

The purpose of this paper is to explore the effects that customer structured and unstructured information sharing (IS) can have on customer operational and strategic coordination and on supply chain performance (SCP). In addition, the study examines how customer IS influences customer coordination under various levels of demand uncertainty (DU).

The conceptual model for this study is designed on the basis of information-processing theory (IPT). Using data collected from 622 manufacturers in mainland China and Taiwan, the theoretical model is tested using the structural equation modeling method.

The authors find that both customer structured IS and unstructured IS are positively associated with customer strategic coordination. Customer structured IS increases customer operational coordination, but customer unstructured IS does not. DU positively moderates the relations between customer unstructured IS and strategic coordination, and between customer structured IS and operational coordination. Also, DU negatively moderates the relationship between customer structured IS and strategic coordination. Customer strategic coordination is positively related to SCP and to operational coordination. Customer operational coordination has no significant impact on SCP.

This study deepens our understanding of customer IS by distinguishing between customer structured and unstructured IS. The study also provides a greater understanding of customer coordination by making a distinction between the customer strategic and the operational coordination. The findings extend the empirical application of IPT. In addition, this study’s findings direct SC managers to apply varied customer IS practices that can enhance specific kinds of customer coordination activities, thereby enabling improved SCP.

This study examines the need for an accounting curriculum to evolve in response to the changing business landscape. Specifically, this study aims to explore the relationship between various market forces, the shifting dynamics of business, and the importance of adapting accounting education to these changes.

This study is a combination of both quantitative and qualitative research. A structured questionnaire was administered to 320 professionals to gather data, and the study employed descriptive analysis, one-sample t-tests, and structured equation modeling to analyze the relationship between the variables.

The findings reveal a strong correlation between business transformation and the imperative to adapt accounting education. This study emphasizes the significance of modifying accounting curricula to align with the current market trends. Furthermore, this study addresses the pressing concern of sustainability and the triple bottom line (TBL), advocating specialized education in sustainability accounting programs.

Despite its contributions, this study acknowledges a limitation in its focus solely on the perceptions of professionals and academicians regarding the impact of business transformation on accounting education, without directly examining the prevailing accounting education system. Future research should address this limitation by undertaking a qualitative exploration of the actual accounting education landscape and market requirements.

The implications of the study span the theoretical, regulatory, environmental, and social domains, stressing the need for educational institutions, regulatory bodies, and industry to collaborate in shaping competent and future-ready accounting professionals. A systematic approach would validate and extend the findings of this study, providing deeper insights into the transformative processes necessary to enhance accounting education in response to evolving business landscapes and environmental dynamics.

The outcome of the study assists educational institutions and regulatory bodies in framing policies to adapt accounting education to the evolving business landscape by updating the accounting curriculum as per the changes in the market forces to make graduates relevant and competent.

The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM).

Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established.

The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM.

The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community.

The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM.

MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

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.

The purpose of this paper is to determine the stakeholders’ acceptance on blockchain and to investigate the model fit by using “Technology Acceptance Model” with special reference to corporate governance through cryptography to resolve the decades-old problems of financial record-keeping.

The whole analysis has been performed in the two steps, i.e. confirmatory factors analysis and structural equation modeling, to prove model fit between behavioral intention and actual behavior for using blockchain technology. Total 223 respondents have been selected, and the selection of the respondent is primarily on the basis of their previous experience with trading corporate equities.

The study determines empirically all the mentioned relationships of attitude, perceived ease of use and perceived usefulness with the behavioral intention as per the conceptual model to prove the relationship. The results of the manuscript shows the model fit indexes for various constructs are prove the model fit as per the theorized model. The values of the various indexes are found to be under the permissible range which explains the relationship of various constructs based on the theorized model.

Despite, the limitations in terms of selection of sampling methods, outcome and the interpretation, the results proves the fit with the theoretical framework. The major implication is to understand the real-time use of blockchain technology for the transfer of shares from one party to other.

Stakeholders in corporate governance namely customers, creditors, suppliers, community, employees, owners, investors, trade unions and social activists could benefit in different ways. Investors could benefit from being able to purchase equity at low price and to sell them into a market with greater liquidity, but they would found it difficult to camouflage their trades.

The study opines that virtually all aspects of the corporate governance can be improved through the adoption of this technology resulting in greater transparency, improved liquidity and lowering costs.

This study will be a reference for global players in the financial industry that have started investing in this innovative technology vis-à-vis recent announcement of adoption of blockchain by global exchanges including NASDAQ, NYSE and Deutsche Borse, as a new method for trading, tracking ownership and monitoring systemic risk for strengthening corporate governance mechanism. This study will have a significant index for future reference where the technology adoption will be tested to have better corporate governance which will be useful for academics and professionals.

The purpose of this paper is to develop and validate a model for key barriers to lean implementation in the Indian coal mining industry.

Interpretive structural modeling (ISM) has been used to develop a proper hierarchy and contextual relationship of key barriers to lean implementation in the Indian coal mining industry through literature review and expert opinion which is followed by the classification of barriers using Matrice d’ Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) and questionnaire-based survey to validate the ISM model using structural equation modeling (SEM).

In this study, 14 key barriers to lean implementation in the Indian coal mining industry have been identified, modeled and analyzed. The lack of top management commitment, financial constraints and lack of inter-departments co-ordination are found to be the most important barriers to lean implementation in the mining industry. The ISM-based model is validated using the SEM.

The analysis of data represents that relatively more participants were from the mines located in eastern India and the maximum participants were managers and executives holding different levels (lower, middle or upper), although key participants in different mines were encouraged to distribute the survey to other employees also.

This model on lean barriers would help the decision makers, researchers and practitioners to anticipate potential barriers to lean implementation and support the existing academic research on lean. Accordingly, the focus on the lean barriers can be prioritized for the better utilization of the available resources for eliminating or minimizing the barriers.

This paper is an original contribution of analysis of the lean barriers in Indian mining industry using the integrated ISM–MICMAC and SEM approach.