Service Quality Measurement in Information Systems: An Expectation and Desire Disconfirmation Approach

Service Quality Measurement in Information Systems: An Expectation and Desire Disconfirmation Approach

Ankit Kesharwani, Venkatesh Mani, Jighyasu Gaur, Samuel Fosso Wamba, Sachin S. Kamble
Copyright: © 2021 |Pages: 19
DOI: 10.4018/JGIM.20211101.oa30
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Traditionally measurements of service quality have followed the expectation-disconfirmation approach. Further, previous studies have shown that negative disconfirmation is more influential than positive disconfirmation. In this research, we hypothesized information systems(IS) service quality scales based on the dimensionality of the expectation-disconfirmation (ED) and desire-disconfirmation (DD) approach. Using the SERVQUAL+ instrument and data collected from 321 IS users, we developed ED and DD based IS service quality scales using contemporary methods, such as LISREL-based CFA. We have proposed and empirically validated the following two new IS service quality constructs: Service Adequacy (difference of expected service and perceived service) and Service Superiority (difference of desired service and perceived service). Our results indicate that both measures have shown better predictive power than earlier scales like SERVQUAL+ and the IS ZOT scales. We have outlined several implications of ED and DD scales to practice and research.
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1. Introduction

The timely assessment of information system (IS) service quality can help the firms meet end-user requirements and instill satisfaction. Therefore, all IS firms have adopted the approaches to measure the IS users' perception of service quality dimensions as an integral part of their IS success evaluation. Many researchers have opined on the use of expectation disconfirmation theory (EDT) as an effective way to gauge users' satisfaction with IS usage (Hossain, 2019). The EDT theory holds that consumer satisfaction is related to the magnitude and direction (positive or negative) of the discrepancies (or disconfirmation) between prior expectations and perceived performance (Churchill and Surprenant, 1982; Gorla and Somers, 2014). Three forms of disconfirmation may occur: a) expectations are confirmed when perceived performance meets expectation, b) expectations are negatively disconfirmed when perceived performance falls short of expectations, and c) expectations are positively disconfirmed when perceived performance is better than expected performance (Rouf et al., 2019; Zamani and Pouloudi, 2020).

Studies have highlighted that expectations-based disconfirmation alone may not provide a complete picture as desires-based disconfirmation can also impact consumers' satisfaction (Gorla and Somers, 2014; Hossain, 2019). As pointed out in previous studies, the gap measures of service quality possess superior diagnostic capabilities as they are grounded in EDT, linked to user satisfaction (Hogreve et al., 2017). For example, considering the perceived services of individual users, tangibles could have the lowest performance ratings. If the perception-minus-expectation measures are considered, reliability could have the largest negative disconfirmation across individual users (Chen et al., 2018). Using perception-only scores, the company may pay more attention to tangibles than reliability with the largest shortfalls of service, thereby incorrectly diagnosing service deficiencies (Parasuraman et al. 1994). Thus, instruments that capture disconfirmation of expectations need to be different from those designed based on perception-only measures (Kettinger and Lee, 2005).

Therefore, there are several advantages of disconfirmation-based measures over alone perception-based measures: First, the use of performance-only based scales results in misguided diagnostics of service deficiencies, leading to wrong resource allocation decisions by managers. Second, the dual expectation measures of service are more realistic than single expectation measures and are being used in industry because of their importance in satisfaction research. Previous instruments in IS service quality did not capture individual users' service disconfirmations concerning expectations and desires (Chen et al., 2018). Third, previous research in IS service quality paid little attention to the unidimensionality property, which is the critical and basic assumption in measurement theory (Kettinger and Lee, 2005). Our study aims to address the above research gaps in IS service quality by developing IS service quality scales based on expectation-disconfirmation and desire-disconfirmation approaches.

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