Service quality measurement is vital for service providers’ development and growth. However, assessing service quality is inherently challenging due to well-established service characteristics such as intangibility, heterogeneity, and inseparability. Extant research has focused on this area and developed various models. The majority used models such as SERVQUAL predominantly utilising surveys to collect customers’ subjective feedback [1].

While substantial research has examined appropriate survey contents and structures, the measurement scales employed in these surveys remain largely unchanged. Specifically, single-point scales, such as the 5- or 7-point Likert scales, are predominantly utilised. These scales, which restrict participants to selecting single predefined values, limit customers’ authentic expression [2].

From a computer science perspective, a possible solution is the employment of Interval-Valued Scales (IVSs). IVSs enable respondents to select an interval by positioning an ellipse on a straight line with polar adjectives at each end. Consequently, interval-valued responses offer richer, more detailed information compared to single-point scales. They provide researchers with greater insight into respondent perceptions and uncertainty. Moreover, as IVSs do not have prefixed choices, respondents have complete freedom in expressing their evaluations, capturing individual differences [3] [4] [5].

This PhD research investigates the potential of IVSs for measuring the UK rail industry's service quality. Rail transportation, regarded as one of the safest, most effective, and most environmentally friendly ways to travel, is an important mode of transport in Great Britain. Thus, comprehending rail service quality from the customer's viewpoint is critical. To achieve this, the government, in collaboration with organisations like Transport Focus, conducts weekly and yearly surveys with passengers. This PhD research aims to improve the current survey methods, capturing richer information from UK rail customers by using IVSs, and thereby delivering an enhanced understanding of rail service quality. It has the potential to assist the rail industry in better identifying customer needs and developing accordingly.

References

[1] Seth, N., Deshmukh, S. G., & Vrat, P. (2005). Service quality models: a review. International journal of quality & reliability management, 22(9), 913-949.

[2] Chen, Z. S., Liu, X. L., Chin, K. S., Pedrycz, W., Tsui, K. L., & Skibniewski, M. J. (2021). Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China. Information Fusion, 69, 22-39.

[3] Wagner, C., Miller, S., Garibaldi, J. M., Anderson, D. T., & Havens, T. C. (2014). From interval-valued data to general type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 23(2), 248-269.

[4] Ellerby, Z., Wagner, C., & Broomell, S. B. (2021). Capturing richer information: On establishing the validity of an interval-valued survey response mode. Behavior Research Methods, 1-23.

[5] Lubiano, M. A., Montenegro, M., Pérez-Fernández, S., & Gil, M. Á. (2022). Analyzing the Influence of the Rating Scale for Items in a Questionnaire on Cronbach Coefficient Alpha. Trends in Mathematical, Information and Data Sciences: A Tribute to Leandro Pardo, 377-388.