Determinants of Relationship Quality and Loyalty in Personalized Services

The study adopts a relationship perspective to examine and better understand customers' interactions with service firms' contact personnel, physical environment and customer environment and their influence on the relationship quality, that is trust and satisfaction, and consequently loyalty. The conceptual model, which integrates relational and evaluative perspectives, is validated in the Asian context of personalized service through findings from a survey of 325 respondents. The findings at the aggregate level indicate that while the physical environment is an important determinant of both trust and satisfaction in personalized service encounters, the customer environment influences only satisfaction but not trust. Service contact personnel's friendliness and knowledge of customers strongly influence relationship quality; while expertise, similarity and disclosure influence trust but not satisfaction. Both relationship trust and satisfaction were found to significantly influence loyalty to the service provider. At the disaggregate level, trust was found to be a stronger predictor of loyalty for high-end service providers while satisfaction was a stronger predictor of loyalty for low-end service providers. The implications for high-end and low-end service providers to enhance loyalty by strategically leveraging on key customer-firm antecedents of relationship trust and satisfaction are discussed.     

On extremes of bivariate residual lifetimes from generalized Marshall–Olkin and time transformed exponential models

We study here extremes of residuals of the bivariate lifetime and the residual of extremes of the two lifetimes. In the case of generalized Marshall–Olkin model and the total time transformed exponential model, we first present some sufficient conditions for the extremes of residuals to be stochastically larger than the residual of the corresponding extremes, and then investigate the stochastic order of the residual of extremes of the two lifetimes based on the majorization of the age vector of the residuals.     

AN ANALYSIS OF FACTORS AFFECTING CROSS DOCKING OPERATIONS

Using simulation, we examine the decisions in moving freight between inbound and outbound trailers in a cross dock. The decisions examined include direct versus indirect handling of pallets, number of open receiving doors, door layout, number of forklifts, size of cross dock and freight mix. This study provides useful insights on handling freight in a cross dock.     

Causality in the context of analytical models and numerical experiments

Intuition tends to guide model formulation, as it is generally impossible to consider all dimensions of a problem. The ability to surprise, heightening the focus on paradox and the contradiction of reality, is therefore more useful than a literal representation of reality. While numerical experiments are useful in exploring patterns not well suited to analytic approaches, features of the model that underlies the experiment determines the experiments’ ability to provide insight and offer surprise.     

Exact inference for progressively Type-I censored exponential failure data

For reasons of time constraint and cost reduction, censoring is commonly employed in practice, especially in reliability engineering. Among various censoring schemes, progressive Type-I right censoring provides not only the practical advantage of known termination time but also greater flexibility to the experimenter in the design stage by allowing for the removal of test units at non-terminal time points. In this article, we consider a progressively Type-I censored life-test under the assumption that the lifetime of each test unit is exponentially distributed. For small to moderate sample sizes, a practical modification is proposed to the censoring scheme in order to guarantee a feasible life-test under progressive Type-I censoring. Under this setup, we obtain the maximum likelihood estimator (MLE) of the unknown mean parameter and derive the exact sampling distribution of the MLE under the condition that its existence is ensured. Using the exact distribution of the MLE as well as its asymptotic distribution and the parametric bootstrap method, we then discuss the construction of confidence intervals for the mean parameter and their performance is assessed through Monte Carlo simulations. Finally, an example is presented in order to illustrate all the methods of inference discussed here.     

Dispersive ordering of fail-safe systems with heterogeneous exponential components

Let X ₁, . . . , X _n be independent exponential random variables with respective hazard rates λ₁, . . . , λ _n , and Y ₁, . . . , Y _n be independent and identically distributed random variables from an exponential distribution with hazard rate λ. Then, we prove that X _2:n , the second order statistic from X ₁, . . . , X _n , is larger than Y _2:n , the second order statistic from Y ₁, . . . , Y _n , in terms of the dispersive order if and only if

$\lambda\geq \sqrt{\frac{1}{{n\choose 2}}\sum_{1\leq i < j\leq n}\lambda_i\lambda_j}.$

We also show that X _2:n is smaller than Y _2:n in terms of the dispersive order if and only if

$ \lambda\le\frac{\sum^{n}_{i=1} \lambda_i-{\rm max}_{1\leq i\leq n} \lambda_i}{n-1}. $

Moreover, we extend the above two results to the proportional hazard rates model. These two results established here form nice extensions of the corresponding results on hazard rate, likelihood ratio, and MRL orderings established recently by Pǎltǎnea (J Stat Plan Inference 138:1993–1997, 2008), Zhao et al. (J Multivar Anal 100:952–962, 2009), and Zhao and Balakrishnan (J Stat Plan Inference 139:3027–3037, 2009), respectively.

     

A synthesis of exact inferential results for exponential step-stress models and associated optimal accelerated life-tests

A reliability experimenter is often interested in studying the effects of extreme or varying stress factors such as load, pressure, temperature and voltage on the lifetimes of experimental units. Accelerated life-tests allow the experimenter to vary the levels of these stress factors in order to obtain information on the parameters of the lifetime distributions more rapidly than under normal operating conditions. Step-stress tests are a particular class of accelerated life-tests which allow the experimenter to change the stress levels at pre-fixed times during the life-testing experiment. One of the prominent models assumed in step-stress tests is the cumulative exposure model which connects the lifetime distribution of units at one stress level to the lifetime distributions at preceding stress levels. Under such a cumulative exposure model and the assumption that the lifetimes at different stress levels are exponentially distributed, we review in this article various developments on exact inferential methods for the model parameters based on different forms of censored data. We also describe the approximate confidence intervals based on the asymptotic properties of maximum likelihood estimators as well as the bootstrap confidence intervals, and provide some comparisons between these methods. Finally, we present some examples to illustrate all the inferential methods discussed here.     

The prognostics of diversifying acquisitions

The paper offers a possible explanation for the discrepancy between the observed increase in the number of diversified firms in the U.S. and the evidence from finance studies which at best offers only weak support for value creation in diversifying acquisitions. It is argued that the acquisition could be the culmination of a series of related strategic moves by the acquiring firm to enter a new industry, and therefore a significant fraction of the gains from synergy could have been anticipated by the capital market well ahead of the acquisition. Results from an event study of the stock market's reactions to the antecedents of the recent acquisition of Rolm Inc. by IBM lend support to this argument.     

Technical change,competition and vertical integration

This paper starts with a survey of the received theories of vertical integration. We then extend these theories by arguing that while uncertainty in general will make integration more effective, a particular type of uncertainty, the possibility of technological obsolescence, works the other way. After making this point at a conceptual level, we build a model to study how the frequency of technological change interacts with the intensity of competition to influence the optimal level of integration. The predictions of the model are then tested and very strongly supported by data from 93 industries.