Psychological bonds and their impact on behavioral loyalty in auditor-client relationships

Purpose – The purpose of this paper is to examine the effect of the psychological bond on behavioral loyalty (word-of-mouth, continuance and non-audit services) to audit firms providing services to companies listed on the Polish Stock Exchange. Design/methodology/approach – A model is proposed and tested using structural equation modeling with LISREL. Data were collected from top executives of companies listed on the Warsaw Stock Exchange. Findings – The psychological bond has a positive effect on word-of-mouth, continuance and non-audit services. Research limitations/implications – The current research is limited to the investigation of the psychological bond as an antecedent of behavioral loyalty in Poland. Future research should identify and assess other antecedents and replicate these across different countries. A longitudinal survey across different points in time might reveal more useful information about auditor-client relationships. Practical implications – Auditors need to consider ways in which they can develop the psychological bond with their clients. This bond is the basis for the client believing the audit firm is superior to others, which has been found to lead to behavioral loyalty in this study. In particular, management of the auditor brand and reputation, personal experience of the audit firm and alumni relations are discussed as ways of enhancing the psychological bond among client executives. Originality/value – This study is the first to examine the impact of the psychological bond on behavioral loyalty in auditor-client relationships.

Collaborating components in mesh-based electronic packaging

From the model geometry creation to the model analysis, the stages in between such as mesh generation are the most manpower intensive phase in a mesh-based computational mechanics simulation process. On the other hand the model analysis is the most computing intensive phase. Advanced computational hardware and software have significantly reduced the computing time - and more importantly the trend is downward. With the kind of models envisaged coming, which are larger, more complex in geometry and modelling, and multiphysics, there is no clear trend that the manpower intensive phase is to decrease significantly in time - in the present way of operation it is more likely to increase with model complexity. In this paper we address this dilemma in collaborating components for models in electronic packaging application.

Pseudo-spectral solutions for fluid flow and heat transfer in electro-metallurgical applications

The pseudo-spectral solution method offers a flexible and fast alternative to the more usual finite element and volume methods, particularly when the long-time transient behaviour of a system is of interest. The exact solution is obtained at grid collocation points leading to superior accuracy on modest grids. Furthermore, the grid can be freely adapted in time and space to particular flow conditions or geometric variations, especially useful where strongly coupled, time-dependent, multi-physics solutions are investigated. Examples include metallurgical applications involving the interaction of electromagnetic fields and conducting liquids with a free surface. The electromagnetic field determines the instantaneous liquid volume shape, which then affects the electromagnetic field. A general methodology of the pseudo-spectral approach is presented, with several instructive example applications: the aluminium electrolysis MHD problem, induction melting in a cold crucible and the dynamics of AC/DC magnetically levitated droplets. Finally, comparisons with available analytical solutions and to experimental measurements are discussed.

A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.