Search results

This paper aims to recommend a two‐step approach to destination segmentation that incorporates the views both of multiple stakeholders and of tourists.

Step one applies a case study approach incorporating semi‐structured interviews with 13 destination stakeholders. Step two involves segmenting tourists to the destination based on a questionnaire survey developed from the semi‐structured interviews. The study compares and contrasts the result with the current DMO approach.

The two‐step approach produces three segments for the destination under study using four segmentation bases and ten variables. The DMO approach also utilizes all four segmentation bases but produces six segments with five different variables. The DMO approach captures fewer tourists visiting the destination.

This study considers one regional Australian destination. Future research is recommended in a range of alternative destinations to further understand the two‐step segmentation approach. It is recommended that the two‐step approach should be extended to destination branding and positioning.

Segmentation guides positioning and branding strategies and the proposed two‐step approach may assist destination stakeholders to reach more of the types of tourists who are likely to visit the destination.

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.

The purpose of this paper is to: first, illustrate how market segmentation using two-step cluster analysis can be used to identify segments in the context of physical activity; second, identified segments are used to offer practical implications for social marketers working in the area of physical activity.

A total of 1,459 respondents residing within 20 kilometres of the Melbourne Central Business District participated in an online survey. The questions in the survey included items relating to respondents’ health perceptions, health knowledge, attitudes, intentions to start a new physical activity, demographics, place of residence and self-reported physical activity. Two-step cluster analysis using the log-likelihood measure was used to reveal natural groupings in the data set.

This research has identified four distinctive segments in the context of physical activity, namely: Young Disinteresteds, Successful Enthusiasts, Vulnerables and Happy Retirees.

The study was conducted in March and some sports were not in season at the time of the study, therefore future research should extend the current sample to take seasonality and geography into account and to ensure the clusters are fully representative of the Australian population.

This paper contributes to the literature by outlining a two-step cluster analytic approach to segmentation that can be used by social marketers to identify valuable segments when developing social marketing programmes.

This paper aims to present an approach for calibrating the numerical models of dynamical systems that have spatially localized nonlinear components. The approach implements the extended constitutive relation error (ECRE) method using multi-harmonic coefficients and is conceived to separate the errors in the representation of the global, linear and local, nonlinear components of the dynamical system through a two-step process.

The first step focuses on the system’s predominantly linear dynamic response under a low magnitude periodic excitation. In this step, the discrepancy between measured and predicted multi-harmonic coefficients is calculated in terms of residual energy. This residual energy is in turn used to spatially locate errors in the model, through which one can identify the erroneous model inputs which govern the linear behavior that need to be calibrated. The second step involves measuring the system’s nonlinear dynamic response under a high magnitude periodic excitation. In this step, the response measurements under both low and high magnitude excitation are used to iteratively calibrate the identified linear and nonlinear input parameters.

When model error is present in both linear and nonlinear components, the proposed iterative combined multi-harmonic balance method (MHB)-ECRE calibration approach has shown superiority to the conventional MHB-ECRE method, while providing more reliable calibration results of the nonlinear parameter with less dependency on a priori knowledge of the associated linear system.

This two-step process is advantageous as it reduces the confounding effects of the uncertain model parameters associated with the linear and locally nonlinear components of the system.

Knowledge of the dependence structure between financial assets is crucial to improve the performance in financial risk management. It is known that the copula completely summarizes the dependence structure among multiple variables. We propose a multivariate exponential series estimator (ESE) to estimate copula densities nonparametrically. The ESE has an appealing information-theoretic interpretation and attains the optimal rate of convergence for nonparametric density estimations in Stone (1982). More importantly, it overcomes the boundary bias of conventional nonparametric copula estimators. Our extensive Monte Carlo studies show the proposed estimator outperforms the kernel and the log-spline estimators in copula estimation. It also demonstrates that two-step density estimation through an ESE copula often outperforms direct estimation of joint densities. Finally, the ESE copula provides superior estimates of tail dependence compared to the empirical tail index coefficient. An empirical examination of the Asian financial markets using the proposed method is provided.

This chapter reviews the methods available to hospitality and tourism researchers to perform moderation analysis with continuous variables in partial least squares structural equation modeling (PLS-SEM), with the objective of enhancing understanding and encouraging the use of these techniques in future papers. The product term method is presented first, followed by an empirical example/application in the context of hospitality and tourism. Two extensions, namely the two-stage approach that can help cope with formative and higher-order constructs, and the orthogonalizing approach that can help generate more accurate results and overcome multicollinearity among tourism variables in the presence of a continuous moderator variable, are then presented and discussed. The chapter concludes by presenting guidelines and recommendations for improving the use of interaction effects in analyses of tourism variables, as well as highlighting ongoing developments in both the product term method and PLS-SEM software.

Purpose: Following the theoretical literature on growth model with externalities, the chapter aims at finding empirical evidence of the main sources of economic interdependencies in Europe.

Methodology/approach: A two-step econometric procedure is adopted. In the first step, in order to evaluate growth spillovers in Europe, cointegration relationships between indexes of industrial production per capita are estimated for 15 European countries. The estimated coefficients, interpreted as long-run elasticities between European countries, appear to be different between countries and unstable over time. In the second step, these coefficients are explained by trade, specialization, research and development (R&D), and macroeconomic variables.

Findings: Panel estimations show strong evidence in favor of a positive relationship between openness, country size, knowledge accumulation, and the long-run sensitivity to European income. European income spillovers are not explained by the specialization of trade and production. We conclude that countries that benefit the most from economic integration are the largest and those that invest the most in R&D.

Originality/value of chapter: The two-step approach adopted in this chapter is original and allows for measuring the impact of various determinants of externalities at the same time.

This study aims to capture the “persistence effect” of credit risk in Indian banking industry using the bank-level data spanning over the period of 19 years from 1998/1999 to 2016/17. Alongside, the study explored how the bank-specific, industry-specific, macroeconomic variables alongside regulatory reforms, ownership changes and financial crisis affect the bank's asset quality in India.

Using two-step system generalized method of moment (GMM) approach, the study derives key factors that affect the bank's asset quality in India.

The empirical results confirm the time persistence of credit risk among Indian banks during study period. This reflects that bank defaults are expected to increase in the current year, if it had increased past year due to time lag involved in the process of recovery of past dues. Further, higher profitability, better managerial efficiency, more diversified income from nontraditional activities, optimal size of banks, proper credit screening and monitoring and adherence regulatory norms would help in improving the credit quality of Indian banks.

The practical implication drawn from the study is that nonaccumulation of nonperforming loans (NPLs), higher profitability, better managerial efficiency, more diversified income from nontraditional activities, optimal size of banks, proper credit screening and monitoring and adherence regulatory norms would help in improving the credit quality of Indian banks.

This study is probably the first one that identifies in addition to the current year, whether lag of bank industry-macroeconomic affects the level of NPLs of Indian banks. So far, such an analysis has received less attention with respect to Indian banking industry, especially immediate aftermath of the global financial crisis.

This article deals with how to test for and evaluate interdependence among control practices in a management control system using structural equation modeling. Empirical research on the levers of control (LOC) framework is used as an example. In LOC research, a path model approach to interdependence has been developed. The appropriateness of this approach is evaluated, developed, and compared with the correlation of residuals approach (seemingly unrelated regression) implemented in the wider complementarity literature. Empirical examples of the different models are shown and compared by using a data set on LOC of 120 SBUs in Sweden. The empirical results show that modeling interdependence among control practices in a management control system as non-recursive (bi-directional) paths or as residual correlations evidently affects the conclusions drawn about interdependence in terms of both presence and magnitude. The two models imply different views on how to conceptualize interdependence and are not statistically and empirically comparable. If using non-recursive path models, several model specification issues appear. To be able to identify such models, this needs to be carefully considered in the theory and research design prior to data collection.