Separate Ratio Estimators for the Population Variance in Stratified Random Sampling

We propose separate ratio estimators for population variance in stratified random sampling. We obtain mean square error equations and compare proposed estimators about efficiency with each other. By these comparisons, we find the conditions which make proposed estimators more efficient than others. It has been shown that proposed classes of estimators are more efficient than usual unbiased estimator. We find that separate ratio estimators are more efficient than combined ratio estimators for population variance. The theoretical results are supported by a numerical illustration with original data. A simulation study is also carried out to investigate empirical performance of estimators.     

The relative efficiency of the restricted estimators in linear regression models

This paper deals with the problem of multicollinearity in a multiple linear regression model with linear equality restrictions. The restricted two parameter estimator which was proposed in case of multicollinearity satisfies the restrictions. The performance of the restricted two parameter estimator over the restricted least squares (RLS) estimator and the ordinary least squares (OLS) estimator is examined under the mean square error (MSE) matrix criterion when the restrictions are correct and not correct. The necessary and sufficient conditions for the restricted ridge regression, restricted Liu and restricted shrunken estimators, which are the special cases of the restricted two parameter estimator, to have a smaller MSE matrix than the RLS and the OLS estimators are derived when the restrictions hold true and do not hold true. Theoretical results are illustrated with numerical examples based on Webster, Gunst and Mason data and Gorman and Toman data. We conduct a final demonstration of the performance of the estimators by running a Monte Carlo simulation which shows that when the variance of the error term and the correlation between the explanatory variables are large, the restricted two parameter estimator performs better than the RLS estimator and the OLS estimator under the configurations examined.     

Estimation in a linear regression model with stochastic linear restrictions: a new two-parameter-weighted mixed estimator

The present paper considers the weighted mixed regression estimation of the coefficient vector in a linear regression model with stochastic linear restrictions binding the regression coefficients. We introduce a new two-parameter-weighted mixed estimator (TPWME) by unifying the weighted mixed estimator of Schaffrin and Toutenburg [1] and the two-parameter estimator (TPE) of Özkale and Kaç?ranlar [2]. This new estimator is a general estimator which includes the weighted mixed estimator, the TPE and the restricted two-parameter estimator (RTPE) proposed by Özkale and Kaç?ranlar [2] as special cases. Furthermore, we compare the TPWME with the weighted mixed estimator and the TPE with respect to the matrix mean square error criterion. A numerical example and a Monte Carlo simulation experiment are presented by using different estimators of the biasing parameters to illustrate some of the theoretical results.     

Countervailing institutional forces: corporate governance in Turkish family business groups

This longitudinal study investigates the impact of national and international pressures to improve corporate governance in an emerging economy from an institutional theory perspective. Recently, family business groups (FBGs), the dominant form of organizing in emerging economies, have been criticized by policy makers for their poor governance structures. A common recommendation to FBGs has been increasing the number of independent, outsider directors on their boards. Thus, change in the board compositions of the quoted subsidiaries of the six biggest FBGs is analyzed over 2002–2006. One-way-ANOVA and t-tests were used as statistical tools. Findings reveal that there has not been a statistically significant change in board compositions over the research period. FBGs were found to resist institutional pressures through ‘avoidance’, ‘defiance’ and “manipulation” strategies due to the absence of coercive pressures and multiple forms of ‘institutional work’ for change.     

Ethical leadership,intrapreneurship, service innovation performance and work engagement in chambers of commerce and industry

ABSTRACT

The main purpose of this study which examines how it can be more effective in terms of organizational and managerial aspects with chambers of commerce and industry affiliated to The Union of Chambers and Commodity Exchanges of Turkey is to analyze the relationships between ethical leadership, work engagement, intrapreneurship, and service innovation behavior. In addition, it is to determine the role of mediation of work engagement effecting on intrapreneurship and service innovation behavior. For this purpose, quantitative analysis methods have been adopted. The data collected from 568 employees who were employed in chambers of commerce and industry operating in Turkey were analyzed. For the validity and reliability of the measurement tools, confirmatory factor analysis, the model’s good fit values and the AMOS Structural Equation Model were used. It was found that ethical leadership had statistically significant effect on employee engagement, intrapreneurship and the subscales of service innovation behavior (employee service innovation behavior and new service development). The results showed that work engagement partially mediated with ethical leadership effecting on intrapreneurship, ethical leadership effecting on the subscales of service innovation behavior. It is thought that the study is important in terms of providing empirical contributions to the ethical leadership, service innovation, intrapreneurship and work engagement literature. Finally, there are some limitations of the study and some suggestions are offered for future studies.     

On recursive solutions to simple allocation problems

We propose and axiomatically analyze a class of rational solutions to simple allocation problems where a policy-maker allocates an endowment $E$ among $n$ agents described by a characteristic vector c. We propose a class of recursive rules which mimic a decision process where the policy-maker initially starts with a reference allocation of $E$ in mind and then uses the data of the problem to recursively adjust his previous allocation decisions. We show that recursive rules uniquely satisfy rationality, c-continuity, and other-c monotonicity. We also show that a well-known member of this class, the Equal Gains rule, uniquely satisfies rationality, c-continuity, and equal treatment of equals.     

Supplier Selection for Framework Agreements in Humanitarian Relief

In this study, we consider the supplier selection problem of a relief organization that wants to establish framework agreements (FAs) with a number of suppliers to ensure quick and cost‐effective procurement of relief supplies in responding to sudden‐onset disasters. Motivated by the FAs in relief practice, we focus on a quantity flexibility contract in which the relief organization commits to purchase a minimum total quantity from each framework supplier over a fixed agreement horizon, and, in return, the suppliers reserve capacity for the organization and promise to deliver items according to pre‐specified agreement terms. Due to the uncertainties in demand locations and amounts, it may be challenging for relief organizations to assess candidate suppliers and the offered agreement terms. We use a scenario‐based approach to represent demand uncertainty and develop a stochastic programming model that selects framework suppliers to minimize expected procurement and agreement costs while meeting service requirements. We perform numerical experiments to understand the implications of agreement terms in different settings. The results show that supplier selection decisions and costs are generally more sensitive to the changes in agreement terms in settings with high‐impact disasters. Finally, we illustrate the applicability of our model on a case study.