Why do Emerging Market Firms Engage in Voluntary Environmental Management Practices? A Strategic Choice Perspective

In this paper, we investigate firms’ decisions to engage in voluntary environmental management (VEM) practices within an emerging market context. Drawing on the strategic choice and the resource‐based view perspectives, we report results from a survey of VEM practices – a specific form of self‐governance – drawing on a sample of 519 Turkish firms from various industries to identify important strategic antecedents of firms’ decisions to engage in such practices. We find that as firms become more customer focused, more inclined to pursue a differentiation strategy and subject to a higher level of strategy‐oriented stakeholder focus, they tend to implement higher levels of VEM practices, with important implications for research, policy and practice for both emerging and developed markets.     

Moment and IV Selection Approaches: A Comparative Simulation Study

We compare three moment selection approaches, followed by post-selection estimation strategies. The first is adaptive least absolute shrinkage and selection operator (ALASSO) of Zou (2006 Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101:1418–1429.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), recently extended by Liao (2013 Liao, Z. (2013). Adaptive GMM shrinkage estimation with consistent moment selection. Econometric Theory FirstView:1–48. [Google Scholar]) to possibly invalid moments in GMM. In this method, we select the valid instruments with ALASSO. The second method is based on the J test, as in Andrews and Lu (2001 Andrews, D. W. K., Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics 101(1):123–164.[Crossref], [Web of Science ®] , [Google Scholar]). The third one is using a Continuous Updating Objective (CUE) function. This last approach is based on Hong et al. (2003 Hong, H., Preston, B., Shum, M. (2003). Generalized empirical likelihood based model selection criteria for moment condition models. Econometric Theory 19(06):923–943. [Google Scholar]), who propose a penalized generalized empirical likelihood-based function to pick up valid moments. They use empirical likelihood, and exponential tilting in their simulations. However, the J-test-based approach of Andrews and Lu (2001 Andrews, D. W. K., Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics 101(1):123–164.[Crossref], [Web of Science ®] , [Google Scholar]) provides generally better moment selection results than the empirical likelihood and exponential tilting as can be seen in Hong et al. (2003 Hong, H., Preston, B., Shum, M. (2003). Generalized empirical likelihood based model selection criteria for moment condition models. Econometric Theory 19(06):923–943. [Google Scholar]). In this article, we examine penalized CUE as a third way of selecting valid moments.

Following a determination of valid moments, we run unpenalized generalized method of moments (GMM) and CUE and model averaging technique of Okui (2011 Okui, R. (2011). Instrumental variable estimation in the presence of many moment conditions. Journal of Econometrics 165(1):70–86.[Crossref], [Web of Science ®] , [Google Scholar]) to see which one has better postselection estimator performance for structural parameters. The simulations are aimed at the following questions: Which moment selection criterion can better select the valid ones and eliminate the invalid ones? Given the chosen instruments in the first stage, which strategy delivers the best finite sample performance?

We find that the ALASSO in the model selection stage, coupled with either unpenalized GMM or moment averaging of Okui delivers generally the smallest root mean square error (RMSE) for the second stage coefficient estimators.     

Low glomerular filtration rate as an associated risk factor for sarcopenic muscle strength: is creatinine or cystatin C-based estimation more relevant?

Introduction: We aimed to evaluate the association of a decreased glomerular-filtration-rate (GFR?<60?ml/min/1.73?m²), estimated using Modification of Diet in Renal Disease (MDRD), creatinine- and cystatin C-based (CKDEPI-CR and CKDEPI-CC) Chronic Kidney Disease Epidemiology Collaboration equations with handgrip strength (HGS).

Methods: Community-dwelling males aged?≥60 years admitted to outpatient clinic were included. We used MDRD, CKDEPI-CR, and CKDEPI-CC formulas for GFR estimation and corrected these for body surface area. Muscle strength was assessed by HGS.

Results: 209 men (mean age 67.8?±?6.4) were enrolled. Sixty-two patients (29.7%) had sarcopenic HGS. Subjects with sarcopenic HGS were older, had higher rate of a GFR?2, had lower mid-upper arm circumference; tended to have lower creatine kinase, albumin, CKDEPI-CC-GFR levels; and higher BUN/creatinine ratio and cystatin C. Multivariate logistic regression analysis revealed a CKDEPI-CC lower than 60?ml/min/1.73 m² as the only independent factor underlying sarcopenic HGS. Higher age tended to have an independent association. Only higher age was independently associated with low HGS when other estimations were used (p?=?0.013 and p?=?0.021 when MDRD and CKDEPI-CR were used, respectively).

Conclusions: There is a strong association of a GFR level of <60?ml/min/1.73 m² with sarcopenic HGS, when CKDEPI-CC formula is used.     

The disassembly line: balancing and modeling,by S.M. McGovern and S.M. Gupta

This note addresses a problem faced by an actual firm. The problem is to decide on the optimal level of product quality. In performing the economic analysis to determine product quality level, the firm considers revenue, production costs, and research and development costs. However, this note shows that ignoring inventory costs in the analysis will lead to suboptimal product quality levels. Also, including inventory costs in the analysis will lead to reduced production lot sizes. Numerical examples are used to illustrate the results of the model.     

The Impact of Organizational Structure on Mass Customization Capability: A Contingency View

This study investigates the role of organizational structure in facilitating the development of mass customization (MC) capability in various manufacturing settings. Specifically, three dimensions of organizational structure are considered—flatness, centralization, and employee multifunctionality. We model organizational structure as a second‐order factor whose value is captured on a mechanistic‐organic continuum, where the organic form is characterized by a flat, decentralized structure with a wide use of multifunctional employees. We propose that a positive relationship exists between the organic organizational structure and MC capability. Additionally, building upon contingency theory, we argue that this positive relationship is moderated by mass customizer type—full mass customizers, which customize products at the design or fabrication stage of the production cycle, versus partial customizers, which customize products only at the assembly or delivery stages. Based on a study of 167 manufacturing plants from three industries and eight countries, we find that, for the overall sample, organic structure plays a significant role in enabling firms to pursue MC capability. However, an analysis of full versus partial mass customizers shows that the positive impact of organic structure on MC capability is statistically significant only for full mass customizers, not for partial mass customizers.     

Bayesian inference for bivariate generalized linear models in diagnosing renal arterial obstruction

Generalized linear models are well-established generalizations of the linear models used for regression and analysis of variance. They allow flexible mean structures and general distributions, other than the linear link and normal response assumed in regression. Further enhancements using ideas from multivariate analysis improve power and precision by modelling dependencies between response variables. This paper focuses on the specific case of regression models for bivariate Bernoulli responses and investigates their analysis using a Bayesian approach. The important problem of renal arterial obstruction is considered, as a medical application of these models.     

Scheduling Methods for Efficient Stamping Operations at an Automotive Company

We consider scheduling issues at Beyçelik, a Turkish automotive stamping company that uses presses to give shape to metal sheets in order to produce auto parts. The problem concerns the minimization of the total completion time of job orders (i.e., makespan) during a planning horizon. This problem may be classified as a combined generalized flowshop and flexible flowshop problem with special characteristics. We show that the Stamping Scheduling Problem is NP‐Hard. We develop an integer programming‐based method to build realistic and usable schedules. Our results show that the proposed method is able to find higher quality schedules (i.e., shorter makespan values) than both the company's current process and a model from the literature. However, the proposed method has a relatively long run time, which is not practical for the company in situations when a (new) schedule is needed quickly (e.g., when there is a machine breakdown or a rush order). To improve the solution time, we develop a second method that is inspired by decomposition. We show that the second method provides higher‐quality solutions—and in most cases optimal solutions—in a shorter time. We compare the performance of all three methods with the company's schedules. The second method finds a solution in minutes compared to Beyçelik's current process, which takes 28 hours. Further, the makespan values of the second method are about 6.1% shorter than the company's schedules. We estimate that the company can save over €187,000 annually by using the second method. We believe that the models and methods developed in this study can be used in similar companies and industries.     

Do Parent Units Benefit from Reverse Knowledge Transfer?

Emerging market multinationals resort to knowledge acquisitions from their overseas subsidiaries to springboard and realize their global ambitions. Drawing from the knowledge‐based view and social capital perspective, this study explores the effects of organizational collaboration and tacitness on multiple dimensions of reverse knowledge transfer (RKT). Data were collected through a survey, from senior and middle level managers of parent Indian multinationals, pertaining to RKT from their overseas subsidiaries. The hypotheses are analysed using partial least squares modelling. The results demonstrate positive effects between the extent and benefits of RKT. Collaboration was found to have a positive influence on both dimensions of RKT. Tacitness also has a positive impact on the benefits from RKT. The implications of the findings and the limitations of the study are discussed along with suggestions for future research.     

Hazard Rate Properties of Systems with Two Components

In this article, we study the hazard rate ordering of lifetimes of two-component systems (series and parallel) by considering some bivariate distributions for the joint distribution of component lifetimes. Such that, we aim to investigate the lifetimes of systems with stochastically dependent and with stochastically independent components, the lifetimes of the components, and a stochastic ordering relation between these lifetimes. In addition to these, the mononotonicity of the hazard rates of the parallel and the series systems for the bivariate Farlie–Gumbel–Morgenstern (FGM) family is studied.     

Testing,Estimation in GMM and CUE with Nearly-Weak Identification

In this article, we analyze Generalized Method of Moments (GMM) and Continuous Updating Estimator (CUE) with strong, nearly-weak, and weak identification. We show that with this mixed system, the limits of the estimators are nonstandard. In the subcase of GMM estimator with only nearly-weak instruments, the correlation between the instruments and the first order conditions decline at a slower rate than root T. We find an important difference between the nearly-weak case and the weak case. Inference with point estimates is possible with the Wald, likelihood ratio (LR), and Lagrange multiplier (LM) tests in GMM estimator with only nearly-weak instruments present in the system. The limit is the standard χ² limit. This is important from an applied perspective, since tests on the weak case do depend on the true value and can only test simple null. We also show this in the more realistic case of mixed type of strong, weak, and nearly-weak instruments, Anderson and Rubin (1949 Anderson , T. W. , Rubin , H. ( 1949 ). Estimation of the parameters of a single equation in a complete system of stochastic equations . Annals of Mathematical Statistics 20 : 46 – 63 .[Crossref] , [Google Scholar]) and Kleibergen (2005 Kleibergen , F. ( 2005 ). Testing parameters in GMM without assuming that they are identified . Econometrica Forthcoming . [Google Scholar]) type of tests are asymptotically pivotal and have χ² limit.