Presenting Modified Servqual Model to Evaluate Flight Attendant Services: Iran Air case study

Purpose: The investigation of the customer`s viewpoints and utilizing their ideas and expectations in improving the quality of the products, has turned out to be one of the key concerns of the companies working in all fields. Airline industry as one of the most extensive modes of transportation should be strictly accurate in providing services to their customers. This article attempts to investigate important dimensions from passengers' point of view and present a model which would evaluate the flight attendant's performance. Design/methodology: In order to achieve this purpose, on the basis of the literature and experts' and passengers' point of view, the SERVQUAL model has been modified with 26 factors in 6 dimensions. Then 300 data have been gathered for the Factor Analysis Model. Findings: As a result, a model with high goodness of fit has been developed which could be used as a tool of measuring quality of service with a high degree of certainty. In the end, running the model in Iran air, most differences were as below: 1-Improvement 2-Responsiveness 3-Empathy 4-Assurance 5-Tangibles 6-Reliability. Originality/value: In general, in this model, all services with which employees are involved are observed. However, the main issue in this research is related to attendant team. Consequently, considering that many of these indicators have different meanings in assessing customer satisfaction in relation to the performance of flight attendant; modifications should be made to the dimensions of the SERVQUAL model.Peer Reviewe

Quality measures for ETL processes: from goals to implementation

Extraction transformation loading (ETL) processes play an increasingly important role for the support of modern business operations. These business processes are centred around artifacts with high variability and diverse lifecycles, which correspond to key business entities. The apparent complexity of these activities has been examined through the prism of business process management, mainly focusing on functional requirements and performance optimization. However, the quality dimension has not yet been thoroughly investigated, and there is a need for a more human-centric approach to bring them closer to business-users requirements. In this paper, we take a first step towards this direction by defining a sound model for ETL process quality characteristics and quantitative measures for each characteristic, based on existing literature. Our model shows dependencies among quality characteristics and can provide the basis for subsequent analysis using goal modeling techniques. We showcase the use of goal modeling for ETL process design through a use case, where we employ the use of a goal model that includes quantitative components (i.e., indicators) for evaluation and analysis of alternative design decisions.Peer ReviewedPostprint (author's final draft

A QFD framework for quality, innovation and high-tech product development dynamics

The customer mostly chooses a product on the base of its quality, which therefore arises as the main cause of its commercial success. In a nearly axiomatic drawing, it follows that the effect of innovation is the improvement of quality, which itself becomes the aim of innovation. Even though the previous statement relates quality and innovation, it still does not explain their dynamics. To stress them, the ‘quality' concept must be analyzed in more detail. In fact, in addition to the ‘perceived quality', the quality ensured through `design, manufacturing and marketing' combined domains should be dealt with. This paper enhances this issue taking advantage of principles and models made available by control theory schemes coupled with quality function development (QFD) and best practice software modeling based on unified modeling language (UML

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comment: Expert Elicitation for Reliable System Design

Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]Comment: Published at http://dx.doi.org/10.1214/088342306000000529 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Using quality models in software package selection

The growing importance of commercial off-the-shelf software packages requires adapting some software engineering practices, such as requirements elicitation and testing, to this emergent framework. Also, some specific new activities arise, among which selection of software packages plays a prominent role. All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages' capabilities. There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback.Peer ReviewedPostprint (published version