Does improving employee happiness affect customer outcomes? The current study attempts to answer this question by examining the impact of employee satisfaction trajectories (i.e., systematic changes in employee satisfaction) on customer outcomes. After accounting for employees’ initial satisfaction levels, the analyses demonstrate the importance of employee satisfaction trajectories for customer satisfaction and repatronage intentions, as well as identify customer-employee contact as a necessary conduit for their effect. From a macro perspective, employee satisfaction trajectories strongly impact customer satisfaction for companies with significant employee–customer interaction, but not for companies without such interaction. From a micro perspective, employee satisfaction trajectories influence customer repatronage intentions for frequent customers, but not for infrequent customers. These effects are robust to controlling for previous customer evaluations and recent employee evaluations. Overall, these findings extend the dominant view of examining static, employee satisfaction levels and offer important implications for the management of the organizational frontline.
This article studies inference of multivariate trend model when the volatility process is nonstationary. Within a quite general framework we analyze four classes of tests based on least squares estimation, one of which is robust to both weak serial correlation and nonstationary volatility. The existing multivariate trend tests, which either use non-robust standard errors or rely on non-standard distribution theory, are generally non-pivotal involving the unknown time-varying volatility function in the limit. Two-step residual-based i.i.d. bootstrap and wild bootstrap procedures are proposed for the robust tests and are shown to be asymptotically valid. Simulations demonstrate the effects of nonstationary volatility on the trend tests and the good behavior of the robust tests in finite samples. 相似文献
Reduced rank regression (RRR) models with time varying heterogeneity are considered. Standard information criteria for selecting cointegrating rank are shown to be weakly consistent in semiparametric RRR models in which the errors have general nonparametric short memory components and shifting volatility provided the penalty coefficient Cn→∞ and Cn/n→0 as n→∞. The AIC criterion is inconsistent and its limit distribution is given. The results extend those in Cheng and Phillips (2009a) and are useful in empirical work where structural breaks or time evolution in the error variances is present. An empirical application to exchange rate data is provided. 相似文献