Suresh K. Tadisina

An exploratory study into factors of service quality for application service providers

An examination of the services offered by application service providers (ASPs) indicates that their current service quality needs to be improved to satisfy current and future clients. This study attempted to fulfill this need by identifying the dimensions of service quality for the ASP industry through both qualitative and quantitative approaches. As a result, seven dimensions were identified (features, availability, reliability, assurance, empathy, conformance, and security). In addition, an initial checklist was developed to help ASPs assess and diagnose their service quality performance. Some insights and implications to the ASP practitioners were also discussed.

Development of e-service quality measure for internet-based financial institutions

In the early stage of internet markets, internet-based financial institutions competed with traditional financial institutions by providing services at lower prices. However, today, more and more companies are participating in internet markets, nullifying the initial price advantage and resulting in low prices becoming a minimum qualification to compete in the market. Therefore, companies operating in internet markets need something else in order to attract customers. What becomes important for internet-based financial institutions is to provide high quality ‘service’. This study raises a research question: ‘how do customers evaluate the quality of e-services provided by internet-based financial institutions?’ This study reveals six valid and reliable dimensions measuring perceived e-service quality: Trust, Customised communications, Ease of use, Website content and functionality, Reliability and Speed of delivery. These dimensions are validated using confirmatory factor analysis and criterion-related validity.

Collaboration Technology Support for Knowledge Conversion in Virtual Teams: A Theoretical Perspective

Collaboration technologies (CTs) are making it feasible for organizations to rapidly bring together dispersed Corkers into virtual teams that perform a variety of tasks. Group support systems (GSS) are a type of CT that is often deployed to facilitate virtual team operations, which typically involve an exchange of knowledge among team members. This paper builds on Nonakas theory of organizational knowledge creation, and other research, to present a theoretical framework that links the factors that are most relevant to knowledge conversion in GSS-aided virtual teams. The paper advances some research propositions that suggest a positive contribution of GSS to knowledge conversion and the levels of shared understanding and performance that could emerge in virtual teams. In addition, the propositions indicate positive relationships between member perceptions of the virtual work environment, their willingness to share knowledge, and the processes of knowledge conversion in those teams

The Role of E-Service Quality and Information Quality in Creating Perceived Value: Antecedents to Web Site Loyalty

This theory-testing study is an examination of the influences of perceived information quality and perceived e-service quality in determining perceived value, and the influence of these three constructs in determining web site loyalty intentions. The results indicate that (1) perceived e-service quality and perceived information quality similarly influence loyalty intentions, (2) perceived e-service quality more strongly influences perceived value than does perceived information quality, and (3) perceived information quality partially mediates the relationship between perceived e-service quality and perceived value. The model was significant in explaining loyalty intentions.

Simulating Internet-based collaboration: A cost-benefit case study using a multi-agent model

The relationship between the adoption of Internet-based communication technology (ICT) and coordination performance has not been explored in sufficient depth in the literature. We argue that the impact of ICT on a coordination system typically needs to be studied on a case-by-case basis. A case study utilizing multi-agent simulation to support decision-making is described. We built the simulation system based on a theoretical model and a real world case. Through a proper verification process, we demonstrate that a multi-agent simulation experiment is a valid decision support tool for IT investment decisions. From the particular context of our case, we observe that the adoption of ICT is not a determinant of productivity. To save time and to cut cost, decision makers should focus more on technology utilization and business process redesign.

The Impact of Failure Severity, Prior Failure, and Company Control on Service Recovery Outcomes

This study investigates the effects of differential levels of failure severity, prior service failures, and perceived company control on postrecovery satisfaction and negative word-of-mouth. The authors develop a theoretical model and hypotheses drawing on the prevention aspect of regulatory focus theory. The authors test the model via a scenario-based experimental design using the context of academic advising within a university setting. They find that that failure severity and company control are influential factors in the service recovery process, although prior failures did not affect satisfaction or negative word-of-mouth in a meaningful way. Theoretical and practical implications of these findings are discussed.

The analytic hierarchy process as a model base for a merit salary recommendation system

This paper describes how the Analytic Hierarchy Process (AHP), as developed by T.L. Saaty and others, can be used in a merit salary increase Decision Support System (DSS). The basic rationale for the system is that merit increases should be proportional to, or at least be correlated with, the relative value of contribution during the period for which the reward is relevant. Details of AHP are provided as needed for the benefit of the reader not familiar with this widely used tool of multi-attribute decision theory. The proposed merit salary recommendation system is explained, and illustrated using hypothetical data.

Linear programming system identification

We define a version of the Inverse Linear Programming problem that we call Linear Programming System Identification. This version of the problem seeks to identify both the objective function coefficient vector and the constraint matrix of a linear programming problem that best fits a set of observed vector pairs. One vector is that of actual decisions that we call outputs. These are regarded as approximations of optimal decision vectors. The other vector consists of the inputs or resources actually used to produce the corresponding outputs. We propose an algorithm for approximating the maximum likelihood solution. The major limitation of the method is the computation of exact volumes of convex polytopes. A numerical illustration is given for simulated data.

Aggregating multiple expert data for linear case valuation models using the MDE principle

Abstract The Maximum Decisional Efficiency Principle is a recently developed technique for parameter estimation based on actual expert decisions. This paper illustrates how the method may be used to aggregate multiple expert data such as for developing expert system logic for binary classification problems. Previously the method has only been applied to derive a scoring function of Leontief type. In this paper the method is applied to the more important problem of deriving a linear scoring function. Hence the results here are more directly comparable to Linear Discriminant Analysis. Also an example is developed to illustrate the performance of the method when experts disagree on one or more cases to be classified.

Linear programming system identification: The general nonnegative parameters case

Abstract By linear programming system identification, we mean the problem of estimating the objective function coefficient vector π and the technological coefficient matrix A for a linear programming system that best explains a set of input–output vectors. Input vectors are regarded as available resources. Output vectors are compared to imputed optimal ones by a decisional efficiency measure and a likelihood function is constructed. In an earlier paper, we obtained results for a simplified version of the problem. In this paper, we propose a genetic algorithm approach for the general case in which π and A are of arbitrary finite dimensions and have nonnegative components. A method based on Householder transformations and Monte Carlo integration is used as an alternative to combinatorial algorithms for the extreme points and volumes of certain required convex polyhedral sets. The method exhibits excellent face validity for a published test data set in data envelopment analysis.