Search results

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.

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.

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.

Overall quantitative research aims to observe certain fundamental principles of logic and scientific frame of reasoning. There continues to be challenges on how quantitative research is conducted in the field of information systems.

Structured equation modelling (SEM) research identifies concerns about the standard of scientific enquiry method, the issue of the misconception of sustaining the consequent and the issue of collective validity. Therefore, rigor and robustness in instrument validity, constructs validity and path analysis validity maybe better achieved by attending to these three concerns. Measuring a multiple mediator construct in a hypothetical model continues to be a challenge for researchers in information systems research and related fields.

This paper aims to provide a thoughtful assessment of the contemporary issues of structural equation modelling methodology (SEMM), by providing rigid and robust SEMM that has several stages in specifying valid multiple mediators construct and the process to measuring in a path analysis model. This paper attempts to develop each stage of the methodology using relevant research to construct a methodology specified to test effects in multiple mediators in SEM using AMOS software. The methodology developed contains the two main phases; first is prior to data collection phase and the second phase is after the data collection, the use of this methodology design, for implementation, intended to support high methodological standards and subsequent quality in MIS research findings.

The research paper provides SEMM that has several stages in specifying valid multiple mediators construct and the process to measuring in a path analysis model.

The purpose for this paper is to determine the various dimensions of employer brand in the IT sector of India and analyse their impact on the final intent of the candidates to join an organisation. A five-factor employer brand (EB) model and three-factor intent to join (ITJ) model have been tested for reliability and validity through confirmatory factor analysis (CFA). A structural model is empirically tested with EB as independent variable and ITJ as dependent variable through structured equation modelling.

The items for the independent variable EB are generated with the help of literature and semi-structured interviews with final-year student placement coordinators of B.Tech and MCA, studying in central, state and deemed universities of India. For ITJ, the variables are adopted from the literature and confirmed through CFA in the Indian context. Structured equation modelling has been used to analyse the relationship between EB and ITJ.

Five dimensions of EB were explored as growth and development opportunity, company’s reputation, acceptance and belongingness, work–life balance and ethics and CSR. ITJ was found to be composed of intent to pursue, employer’s attractiveness and employer’s reputation. EB has been found to be an effective contributor to ITJ.

The current study has been conducted in the IT sector, and other sectors have not been included. The universities considered for the study were limited to central, state and deemed universities of India. Apart from intention to join, there are other attitudinal and behavioural aspects that have not been included in the current study.

This study gives empirical evidence on EB to be an important antecedent of ITJ from the perspective of prospective employees of a developing nation.

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.

This study investigates the relationship between lean manufacturing practices, management accounting and performance measurement (MAC & PM) practices, organizational strategy, structure, and culture, and facility performance. We extended past research by examining the relationships between lean manufacturing, MAC & PM practices and performance in a broader organizational context. Our study was performed using survey data provided by managers and executives at 368 facilities that had contacted the Shingo Institute for information or that had entered a Shingo Prize competition. Consistent with past research we found a significant positive association between lean manufacturing practices and lean MAC & PM practices. We found that greater employee empowerment, use of process performance measures, and use of lean accounting practices were driven primarily by lean strategy and secondarily by the extent of lean manufacturing practices. We also found that changes in organization structure to support lean are driven primarily by lean strategy and secondarily by lean manufacturing practices. Change toward lean culture, on the other hand, is driven by the extent of lean manufacturing practices. Further, we found that emphasizing process performance measures does not reduce emphasis on results performance measures and emphasizing results performance measures leads to improved financial performance. Process and results measures are being used in tandem and value stream costing has not replaced traditional accounting. The results of our study provide important insights for managers of companies engaged in lean transformation and for academics who teach or research lean accounting.

Evaluating and optimizing e‐government services is imperative for governments especially due to the capacity of e‐services to transform public administrations and assist the interactions of governments with citizens, businesses and other government agencies. Existing widely applied evaluation approaches neglect to incorporate citizens' satisfaction measures. The purpose of this paper is twofold: to contribute to the understanding of citizen‐centric e‐government evaluation and unify existing key performance indicators (KPIs); and to propose a reference process model of a novel evaluation approach that uses the unified KPIs to facilitate the creation of a “know‐how” repository.

The authors adopt a quantitative research approach for the evaluation of e‐government services that is based on data envelope analysis (DEA). A survey was conducted for the empirical investigation and data were collected from 13 e‐government services in Turkey. Based on the empirical application of the e‐government evaluation method, a reference process model is designed.

The proposed evaluation method was proved valid and able to provide assessment with richer explanations than traditional statistical measurements. DEA enabled the identification of insufficient e‐government services and the provision of suggested improvements.

The reference process model is constructed based on the experience gained by applying the method to a sole cultural setting;, i.e. e‐government services in Turkey.

The proposed evaluation method, in comparison to other user‐oriented ones, provided assessments with richer explanations than traditional statistical measurements, such as structured equation modelling. The reference process model constructed based on the empirical research is expected to accelerate the citizen‐oriented evaluation of e‐government and promote impact‐oriented indicators.

This is the first application of DEA in the e‐government field, although it has been widely applied for performance measurement in other fields, especially operations research. The novelty of DEA is that the assessment results provide suggestions for strategic improvement of the e‐services.

Following the theory of emotional events, this paper aims to use the bright triad and the dark tetrad as representations to investigate the role of positive and negative personality patterns in achieving positive and negative innovation. The study also examines the mediating role of emotional intelligence and abusive supervision and the interactive role of emotional exhaustion in understanding the relationship between positive and negative personality patterns and positive and negative innovation.

To test the hypotheses of the study model, a set of questionnaires was distributed to a sample of 500 medical officers working in different departments of public hospitals in southern Iraq. The data were analysed using the structured equation model.

The results of the current study confirm previous studies on emotional intelligence because the bright triad negatively associates with negative innovation and positively associates with positive innovation. Meanwhile, the dark tetrad positively associates with negative innovation through abusive supervision, and that emotional exhaustion reinforces the negative side and weakens the positive side of the relationships.

This study contributes to the literature by emphasising that the values represented by the bright triad have a strong readiness to show positive innovation and immunity to negative influence caused by abusive supervision. Meanwhile, the negative emotions of the dark tetrad pattern result in negative patterns because they correlate with negative innovation and the avoidance of positive behaviour, which is escalated by abusive supervision.