Understanding the consequences of information systems service quality on IS service reuse

Information & Management 46 (2009) 335–341

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Information & Management journal homepage: www.elsevier.com/locate/im

Understanding the consequences of information systems service quality on IS service reuse William J. Kettinger a,*, Sung-Hee ‘‘Sunny’’ Park b, Jeffery Smith c a

Management Information Systems Department, Fogelman College of Business & Economics, University of Memphis, Memphis, TN 38152, USA Department of Business, Kettering University, Flint, MI 48504, USA c Department of Marketing, College of Business, Florida State University, Tallahassee, FL 32306, USA b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 10 May 2006 Received in revised form 14 September 2008 Accepted 28 March 2009 Available online 21 June 2009

IS researchers have normally assumed that satisfaction is the key factor influencing IS customers’ reuse of services; however, a focus on customer satisfaction does not always guarantee customer retention. We synthesized customer satisfaction and dissatisfaction models from prior service quality research to provide a comprehensive model predicting the behavioral intentions of customers to reuse IS services. Five research hypotheses were empirically tested by using a field study of 263 users of an IS service department. Our findings placed IS service quality in a causal network leading to IS service reuse and highlighted the relative importance that service quality value played in predicting behavioral intention to reuse the service. ß 2009 Elsevier B.V. All rights reserved.

Keywords: IS service reuse IS service quality IS service value Behavioral intentions Customer satisfaction or dissatisfaction processes

1. Introduction Customer retention is critical to the survival of an organization’s IS functions (ISF). With the growth of decentralization and outsourcing, the ISF has to serve customers who have substantial discretion in their use of IS services. This situation is becoming paramount as users take advantage of outsourced IS applications through application services providers and content delivered services over the Internet. Today’s IS managers are less concerned with customers’ initial acceptance of IT and more worried about customer defection. Even given these trends, IS research offers little insight into the factors predicting whether an IS customer will or will not reuse an IS service. Service quality research and the SERVQUAL instrument have focused on its measurement and many organizations have incorporated the instrument and management approach as a means for improving service quality. The underlying assumption is that improvements in quality positively affect customer retention. However, the link between service quality, customer satisfaction, and customer retention is neither straightforward nor simple. As a consequence, IS managers have been left without proof of the value of improved quality.

* Corresponding author. Tel.: +1 901 233 9404; fax: +1 901 678 4151. E-mail address: [email protected] (W.J. Kettinger). 0378-7206/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2009.03.004

The goal of our study was to place IS service quality in a comprehensive behavioral intention model that could highlight the role it plays in customer intentions to reuse IS services. A review of customer satisfaction/dissatisfaction (CS/D) processes indicated that several models helped in understanding IS service reuse. We synthesized these models with theoretical and empirical findings of prior research to provide a hybrid model that might predict the intentions of IS customers to reuse IS services; five hypotheses were tested via a field study of 263 users of an IS service department. 2. Conceptual background 2.1. Establishing the predictive power of IS SERVQUAL Service quality has been widely studied since the early work of Zeithaml et al. [13]. An area of particular importance was the difference between consumer expectations of service quality and their perception of it after delivery. The original SERVQUAL measure instrument identified five perceptual dimensions:  Tangibles: physical facilities, equipment, and appearance of personnel.  Reliability: ability to perform the promised service dependably and accurately.  Responsiveness: willingness to help customers and provide prompt service.

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 Assurance: knowledge and courtesy of employees and their ability to inspire trust and confidence.  Empathy: caring, individualized attention provided to customers. In 1994, use of the SERVQUAL instrument was introduced into the IS context [7], establishing the validity and reliability of a four dimension measure (reliability, responsiveness, assurance, and empathy). As it gained acceptance among IS practitioners, researchers seemed willing to accept the causal inference that higher levels of service quality would lead to customer retention, despite a lack of evidence to support this. In the field of marketing, researchers have examined the relationships between service quality and behavioral consequences through a measure of customers’ intentions, suggesting that behavioral intentions (BI) are indicators that signal whether customers will remain with a service provider. Fig. 1 depicts a conceptual model in which service quality leads to BI, which in turn, leads to customer retention. 2.2. Models of customer satisfaction or dissatisfaction processes Customer satisfaction/dissatisfaction (CS/D) models effectively predict customer behavior in a variety of contexts [9]. The expectation–disconfirmation model viewed post-purchase attitude as a function of the difference between anticipated and received satisfaction: a person’s BI was determined primarily by satisfaction (SAT) with prior use of a product or service. SAT has been characterized as a post-use evaluation of product or service quality given pre-use expectations. Perceived service value is the customer’s overall utility assessment. In marketing, service quality value has been identified as the tradeoff between the salient ‘‘gets’’ characteristics one receives versus the sacrifice made to acquire a service (‘‘gives’’) component, which may include not only direct monetary cost but also such non-pecuniary costs as time, effort, and risk. Prior marketing research has modeled service value as a mediator between service quality and BI to reuse a service [3]. Thus, a person’s BIs are determined by the perceived service value (SV). 2.3. A BI model of SERVQUAL for the IS function Although each of the CS/D models appears different, there are interrelationships among service quality, service value, satisfaction and service reuse intentions. By synthesizing the models, satisfaction and service value provide a bridge between service quality and BI to reuse a service. To investigate the overall affects of these constructs, it was necessary to generate a nomological network in which all the relationships existed: see Fig. 2. This model hypothesizes that IS service quality (SQ) is an underlying determinant of IS service value (ISV) and SAT with an IS

Fig. 1. Conceptual model of behavioral consequences of service quality.

Fig. 2. Proposed research model.

function, which in turn, influences BIs to continue to use its services. Through their processes, IS users: (i) form expectations of the IS service prior to use, (ii) perceive a level of quality after consumption, and (iii) may confirm or disconfirm perceived quality based on pre-use expectations. As TRA was adapted to develop TAM, we adapted CS/D to develop our model proposing that IS service value and IS satisfaction mediate the effect of IS service quality on BI for service reuse. SAT was included as it is one of the most commonly used measures of effectiveness in the IS field. Also, the proposed effect of service quality on satisfaction is consistent with service quality research results [6]. Accordingly: Hypothesis 1. Customers’ perceived service quality of an information services function has a positive effect on their satisfaction. Service value has often been neglected in the IS field. In our model, it is defined as the cognitive comparison between what IS customers sacrifice (time, money, or other resources for an IS service) and the benefits received (emotional, social, monetary, and performance). Thus, service quality of the ISF results in service value. Therefore: Hypothesis 2. Customers’ perceived IS service quality of an information service function has a positive effect on their perceptions about IS service value. Attitude has been theorized and validated as an important predictor of intentions concerning IS use. Studies of IS success support relations between user satisfaction and behavioral consequences [4] as well as with the causal relationship between satisfaction and IS continuance behavioral intention [1]. These studies provide adequate support in the IS context for a relation between SAT and BI to reuse a service. Hence: Hypothesis 3. Customers’ satisfaction with an information services function has a positive effect on their behavioral intentions to reuse an IS service. Prior marketing studies indicated that service quality indirectly influenced BIs through value and satisfaction; however, this relationship has yet to be determined in an IS context. The costbenefit component denotes a cognitive tradeoff between effort and the quality of the decision while innovation diffusion addresses perceived usefulness (PU) in terms of relative advantage and image. These are conceptually similar to the IS service value construct (emotional, social, monetary, and performance value). Accordingly, IS service value is represented by PU, which is posited to influence BIs through a direct effect as well as an indirect effect through attitude. IS adoption studies have confirmed the relationship between perceived usefulness and BIs (e.g., [2]). Using perceived usefulness as an indicator of IS service value (ISV), we hypothesize ISV-BI and ISV-SAT associations. Thus:

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Hypothesis 4. Customers’ perceived IS service value of an information services function has a positive effect on their behavioral intentions to reuse an IS service. Hypothesis 5. Customers’ perceived IS service value of an information services function has a positive effect on their satisfaction. 3. Research methodology 3.1. Study context and sample A survey methodology was employed, targeting ISF customers. The College of Business Administration at a US university was chosen as the test site. The college has an independent IS department which is responsible for delivering service to over 4000 computer users. The services include, maintenance and management of 17 servers and over 500 personal computers; network administration of a more than 1000 node LAN with wireless connectivity available anywhere in the college; instructional support, etc. Services are delivered in various ways including an information center, direct consulting, and through application development and IT infrastructure support. Anonymous and self-administered questionnaires were distributed to 494 MBA students who were registered users of the college’s computing services. In order to promote participation, subjects were offered free snacks and drinks as well as a lottery ticket with an opportunity to win small cash prizes. The subjects were told that participation in the study was purely voluntary. In total, 263 useable surveys were returned (a 53% response rate). The respondents ranged in age from 22 to 50 (mean of 27.7 years) and were 69.6% male. The respondents had diverse levels of work experience ranging from 2 to 22 years (mean of 5.1 years). Almost all respondents (99%) owned a laptop computer due to the MBA program requirement. Additionally, they had been registered users of the college’s computing services from 3 to 27 months (mean of 15 months). All MBA student subjects paid a compulsory technology fee at the beginning of each semester but were not required to use the computer services of the college as most had facilities provided elsewhere (employers or different university locations). We measured subjects’ perceived voluntariness with raw data indicating that the students had substantial discretion (average of 4.2 on a 0–10 scale). We also tested voluntariness as a control variable and did not find a significant impact. All subjects had at least a modest level of face-to-face interaction with the college’s computing services staff when they established their network, email, and software user authorization. Therefore, the sample can be described as motivated by either class or work responsibilities to make use of the IS resources and to obtain IS support. Overall, the sample included individuals who had a reasonable level of work and computer services experience, indicating that they are capable of providing valid responses to the questionnaire.

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perception-only SERVQUAL measures to explain variance in the dependent constructs [8]. IS service value was assessed using four items from a multidimensional perceived value scale (PERVAL) that can be used to assess customers’ perceptions of value [11]. Four distinct value dimensions emerged: (i) emotional (from the feeling that a service generates), (ii) social (from the service enhancing one’s self-concept), (iii) ROI (from the service due to the reduction of perceived costs), and (iv) performance/quality (from the perceived quality and expected performance of the product). Some items were altered slightly to fit our research setting. BI to reuse was measured using existing items. Satisfaction was assessed via an 11-point semantic differential scale (to stay consistent with prior research indicating that an overall satisfaction scale was more efficient than bipolar evaluative dimensions). 4. Results To establish the validity of the dynamics between SERVQUAL, IS service value, satisfaction and BI to reuse an IS service, we used a two-stage SEM approach in which the psychometric properties of all scales were first assessed through confirmatory factor analysis using LISREL 8.80 and then through examination of the structural relationships. 4.1. Measurement model Descriptive statistics are shown in Table 1. LISREL’s maximum likelihood estimates of the measurement model’s standardized parameter estimates are presented in Table 2 for the nine latent variables, which included one second-order factor with five firstorder factors, and 35 observed variables with their factor loadings, corresponding t values, and R2 values. All but three items (one for SERVQUAL—responsibility and two for BI) had large (>0.70) and significant loadings on their corresponding factors, indicating evidence of good construct validity. The R2 values ranged from 0.46 to 0.95, indicating acceptable internal consistency and reliability for all items. For the latent variables, standard structural coefficients could be interpreted as indicators of validity of the latent factors as components of the two second-order constructs. With t values above 2.0 considered significant, all factors had large and significant structural coefficients, indicating acceptable construct validity. R2 values for each of the eight latent factors ranged from 0.56 to 0.94, indicating acceptable reliability for all factors. 4.2. The structural model All constructs were modeled as reflective and measured using multiple indicators. To test the hypothesized relationships, we used LISREL 8.80 to provide structural equation modeling. The correlation matrix is shown in Table 3. The goodness-of-fit of the model using the maximum likelihood technique was assessed in a

3.2. Operationalization of research variables Four constructs were measured: service quality, IS service value, satisfaction, and BI to reuse an IS Service. All four were measured using multi-item scales (see Appendix A). Satisfaction items were based on 11-point semantic differential scales while the remaining scale items used 11-point Likert-type scales. Scales for service quality were adapted from previous IS SERVQUAL studies. Performance perceptions were used as measures of service quality as opposed to gap scores. This was done because (i) the main purpose of this study was to improve the predictive power of the SERVQUAL model, and (ii) past research supported the use of

Table 1 Descriptive statistics. Constructs

Mean

SD

SERVQUAL: reliability (SQR) SERVQUAL: responsiveness (SQP) SERVQUAL: assurance (SQA) SERVQUAL: empathy (SQE) SERVQUAL: tangibles (SQT) Satisfaction (SAT) IS service value (ISV) IS service reuse—behavioral intentions (BI)

5.55 5.86 6.02 6.06 5.25 5.07 5.19 4.42

2.7 2.6 2.5 2.4 2.6 2.5 2.6 3.0

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Table 2 Standardized parameter estimates and t-values for the measurement model. Observed variables

Latent variables 2

Latent factor

Path coefficient

R2 (reliability)

Service quality Reliability

0.83 (13.2)

0.69

Responsiveness

0.89 (12.2)

0.79

0.74 0.61 0.53 0.74

Assurance

0.97 (13.8)

0.94

0.83a 0.88 (19.1) 0.91 (20.1) 0.87 (18.6) 0.82 (16.9)

0.69 0.77 0.83 0.76 0.67

Empathy

0.89 (15.0)

0.79

SQT1 SQT2 SQT3 SQT4 SQT5

0.75a 0.82 (14.0) 0.78 (13.4) 0.76 (13.0) 0.77 (13.2)

0.56 0.67 0.61 0.58 0.59

Tangibles

0.83 (12.3)

0.69

SAT1 SAT2 SAT3 SAT4

0.97a 0.97 (47.8) 0.96 (44.2) 0.95 (40.7)

0.94 0.94 0.92 0.90

Satisfaction

0.72

ISV1 ISV2 ISV3 ISV4

0.94a 0.93 (31.2) 0.93 (31.2) 0.94 (32.6)

0.88 0.86 0.86 0.88

Value

0.56

BI1 BI2 BI3 BI4

0.87 (13.5) 0.89 (13.9) 0.69a 0.68 (10.8)

0.76 0.79 0.48 0.46

BIs

0.78

Item

Factor loading

R (reliability)

SQR1 SQR2 SQR3 SQR4 SQR5

0.83 (16.0) 0.89 (17.6) 0.91 (18.0) 0.80 (15.2) 0.79a

0.69 0.79 0.83 0.64 0.62

SQP1 SQP2 SQP3 SQP4

0.69a 0.88 (14.1) 0.88 (14.2) 0.94 (15.0)

0.48 0.77 0.77 0.88

SQA1 SQA2 SQA3 SQA4

0.86 (14.8) 0.78 (13.3) 0.73a 0.86 (14.7)

SQE1 SQE2 SQE3 SQE4 SQE5

Note: Factor loading t-values are reported in parentheses; fit statistics: x2 = 1773 (df = 532), SRMR = 0.063, RMSEA = 0.01, CFI = 0.98, NNFI = 0.97. a No t-value was calculated as one indicator had to initially be set to a value of 1.0.

two-step approach. The Chi-square statistic was 2427 (df = 550), which was significant at the 0.01 level, indicating an inadequate model fit. The problem with using the Chi-square statistic was that it is sensitive to sample size where large samples are used and indicate a statistically significant difference even when the practical difference is relatively small. Accordingly, it has been argued that simultaneous investigation of fit indices should be used to compensate for the sensitivity of statistics to large sample sizes [5]. Following this suggestion, we analyzed the relevant fit statistics to assess model fit with the indices being denoted at the bottom of Fig. 3. Initially, we found the standardized RMS residual (SRMR) to be 0.08, which meets the suggested cutoff value (0.08 or less) of a good-fitting model. Secondly, we investigated three alternate fit indices; the comparative (CFI), the Tucker–Lewis (NNFI), and the RMS error of approximation (RMSEA). The CFI was 0.97 and the NNFI was 0.97 (both exceed the recommended value of 0.90 or greater). Also, the RMSEA value was 0.11, which indicated a moderate fit as the cutoff for a good-fitting model is usually considered to be 0.10 or less. These indicated that our model accurately measured the constructs. The service quality, as measured by SERVQUAL, explained 56% of the variance in IS service value. Service quality and IS service

value together explained 72% of the variance in satisfaction, while service quality alone explained 28% of the variance in satisfaction. The addition of IS service value increased the explained variance by 42%. IS service value and satisfaction together explained 78% of the variance in BI, while the addition of IS service value contributed to an increase in explained variance of 51% above that explained by satisfaction alone. As shown in Fig. 3, the SEM results indicated statistically significant support for all five research hypotheses that service quality would influence BI to reuse an IS service through IS service value and satisfaction. Interestingly, the impact of service quality on BI to reuse an IS service was mediated mainly by IS service value, which had been largely ignored previously in explaining IS users’ BI to reuse an IS service. 5. Discussion and implications We extended the IS SERVQUAL instrument by adding predictive ability. Specifically, we empirically examined a nomological network that encompassed service quality, IS service value, satisfaction and BI to reuse an IS service at the individual customer level. Our findings have implications for both managers and researchers.

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Table 3 Correlation matrix.

SQR1 SQR2 SQR3 SQR4 SQR5 SQP1 SQP2 SQP3 SQP4 SQA1 SQA2 SQA3 SQA4 SQE1 SQE2 SQE3 SQE4 SQE5 SQT1 SQT2 SQT3 SQT4 SQT5 SAT1 SAT2 SAT3 SAT4 ISV1 ISV2 ISV3 ISV4 BI1 BI2 BI3 BI4

SQT1 SQT2 SQT3 SQT4 SQT5 SAT1 SAT2 SAT3 SAT4 ISV1 ISV2 ISV3 ISV4 BI1 BI2 BI3 BI4

SQR1

SQR2

SQR3

SQR4

SQR5

SQP1

SQP2

SQP3

SQP4

SQA1

SQA2

SQA3

SQA4

SQE1

SQE2

SQE3

SQE4

SQE5

1.000 0.720 0.764 0.657 0.693 0.479 0.541 0.460 0.514 0.622 0.617 0.397 0.565 0.460 0.409 0.428 0.447 0.529 0.426 0.566 0.485 0.452 0.516 0.710 0.714 0.695 0.684 0.624 0.658 0.609 0.570 0.547 0.561 0.343 0.355

1.000 0.875 0.694 0.670 0.581 0.661 0.546 0.604 0.685 0.608 0.415 0.536 0.490 0.508 0.470 0.490 0.546 0.492 0.513 0.441 0.439 0.514 0.624 0.628 0.640 0.600 0.604 0.628 0.610 0.578 0.503 0.574 0.417 0.375

1.000 0.694 0.710 0.537 0.604 0.509 0.551 0.641 0.574 0.361 0.518 0.439 0.446 0.446 0.434 0.523 0.417 0.540 0.408 0.403 0.497 0.686 0.691 0.686 0.656 0.622 0.657 0.606 0.576 0.506 0.535 0.385 0.359

1.000 0.679 0.614 0.714 0.650 0.693 0.688 0.587 0.471 0.708 0.643 0.589 0.621 0.595 0.636 0.554 0.548 0.490 0.528 0.558 0.634 0.634 0.649 0.614 0.624 0.630 0.630 0.596 0.529 0.589 0.380 0.376

1.000 0.572 0.586 0.442 0.503 0.578 0.584 0.319 0.494 0.403 0.351 0.386 0.447 0.483 0.466 0.633 0.483 0.402 0.579 0.738 0.754 0.755 0.715 0.680 0.718 0.627 0.608 0.577 0.556 0.366 0.339

1.000 0.739 0.556 0.603 0.569 0.522 0.441 0.531 0.520 0.512 0.501 0.493 0.489 0.496 0.532 0.439 0.459 0.534 0.540 0.551 0.564 0.512 0.520 0.551 0.548 0.504 0.457 0.477 0.324 0.311

1.000 0.743 0.827 0.645 0.602 0.575 0.649 0.696 0.625 0.650 0.601 0.579 0.609 0.574 0.523 0.504 0.490 0.573 0.568 0.577 0.523 0.556 0.573 0.590 0.565 0.473 0.538 0.404 0.371

1.000 0.885 0.641 0.550 0.711 0.702 0.741 0.789 0.694 0.678 0.641 0.516 0.448 0.370 0.536 0.391 0.490 0.485 0.501 0.454 0.455 0.450 0.457 0.438 0.418 0.487 0.424 0.407

1.000 0.644 0.571 0.645 0.706 0.734 0.703 0.697 0.665 0.656 0.539 0.490 0.451 0.536 0.438 0.542 0.543 0.551 0.514 0.534 0.522 0.542 0.512 0.492 0.562 0.409 0.388

1.000 0.743 0.577 0.749 0.660 0.647 0.672 0.644 0.688 0.476 0.526 0.492 0.604 0.572 0.636 0.613 0.638 0.618 0.567 0.612 0.598 0.580 0.552 0.607 0.429 0.416

1.000 0.536 0.633 0.540 0.502 0.582 0.580 0.592 0.456 0.521 0.445 0.484 0.552 0.600 0.596 0.621 0.572 0.545 0.573 0.544 0.545 0.516 0.543 0.385 0.367

1.000 0.739 0.688 0.784 0.682 0.643 0.624 0.522 0.382 0.342 0.575 0.381 0.424 0.416 0.410 0.368 0.335 0.343 0.379 0.369 0.347 0.415 0.396 0.393

1.000 0.723 0.725 0.761 0.655 0.732 0.553 0.510 0.466 0.604 0.545 0.565 0.543 0.547 0.518 0.491 0.517 0.547 0.513 0.488 0.554 0.431 0.377

1.000 0.794 0.798 0.683 0.646 0.497 0.423 0.380 0.543 0.399 0.519 0.494 0.510 0.466 0.437 0.455 0.448 0.427 0.412 0.476 0.427 0.432

1.000 0.846 0.782 0.685 0.527 0.414 0.354 0.591 0.398 0.477 0.465 0.498 0.443 0.437 0.436 0.460 0.428 0.416 0.498 0.440 0.457

1.000 0.822 0.736 0.479 0.453 0.380 0.562 0.412 0.531 0.514 0.541 0.507 0.442 0.454 0.502 0.465 0.461 0.516 0.441 0.453

1.000 0.762 0.513 0.409 0.389 0.576 0.447 0.553 0.544 0.557 0.538 0.459 0.485 0.483 0.454 0.456 0.509 0.354 0.382

1.000 0.589 0.526 0.471 0.654 0.544 0.578 0.553 0.582 0.556 0.509 0.511 0.532 0.505 0.487 0.525 0.391 0.402

SQT1

SQT2

SQT3

SQT4

SQT5

SAT1

SAT2

SAT3

SAT4

ISV1

ISV2

ISV3

ISV4

BI1

BI2

BI3

BI4

1.000 0.649 0.620 0.589 0.560 0.501 0.485 0.508 0.477 0.510 0.519 0.477 0.491 0.392 0.464 0.349 0.310

1.000 0.725 0.624 0.630 0.663 0.662 0.686 0.640 0.643 0.692 0.592 0.604 0.545 0.556 0.401 0.410

1.000 0.620 0.632 0.480 0.477 0.527 0.512 0.489 0.555 0.519 0.495 0.396 0.439 0.303 0.281

1.000 0.606 0.484 0.480 0.512 0.467 0.460 0.506 0.494 0.461 0.432 0.465 0.321 0.366

1.000 0.586 0.584 0.620 0.580 0.539 0.576 0.529 0.502 0.510 0.516 0.277 0.308

1.000 0.955 0.951 0.943 0.742 0.798 0.711 0.701 0.700 0.664 0.505 0.497

1.000 0.946 0.944 0.731 0.789 0.693 0.673 0.696 0.648 0.467 0.476

1.000 0.939 0.735 0.798 0.691 0.670 0.679 0.649 0.487 0.505

1.000 0.719 0.782 0.686 0.662 0.620 0.598 0.466 0.478

1.000 0.915 0.870 0.893 0.736 0.749 0.541 0.532

1.000 0.832 0.819 0.725 0.721 0.527 0.521

1.000 0.888 0.683 0.751 0.616 0.583

1.000 0.703 0.775 0.591 0.548

1.000 0.812 0.524 0.543

1.000 0.607 0.561

1.000 0.844

1.000

Implications for practice: Our results supported the assumption that satisfaction and service value of the ISF were salient mediators between service quality and service reuse. The extension of the SERVQUAL instrument using BI measures has important practical implications. First, our model can be a useful tool for predicting customer retention. Second, IS managers can use surveys eliciting reuse intent as an early warning system to identify customers in danger of defecting and take corrective actions. While customer satisfaction significantly influences retention, ignoring the potential impact of service value could have serious consequences. The overall findings offer strong empirical support for the idea that improving service quality increased customer retention. Thus, our conceptual model can be used to understand the impact of service quality by highlighting the complex relationships between service quality, service value, satisfaction, and IS service reuse.

Moreover our model can be a diagnostic tool for identifying specific problem areas, corrective actions, and evaluating improvements. In building a comprehensive model through the synthesis of marketing and IS studies, we believe the integration of the two disciplines contributes to the existing body of knowledge on the dynamics of service quality and the subsequent outcomes. Limitations: The primary limitation of our study was the use of students as respondents [10]. In our study, we expected this to have minimal effect since the participants were MBA students with ample experience to crystallize their perception and attitude in evaluating the computing service. Such an MBA sample has been used in past service quality research [12] with results showing high consistency with measures tested in non-educational settings. Our findings also showed a similar pattern to those previously reported in business settings.

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2. 3. 4. 5.

Providing services at the promised time; Providing services as promised; Dependability in handling students’ service problems; Maintaining reliable technology and system. Responsiveness (4 items)

1. Keeping students informed about when services will be performed; 2. Prompt service to students; 3. Willingness to help students; 4. Readiness to respond to students’ request. Assurance (4 items)

Fig. 3. LISREL results.

Practical follow-up with sample organization’s service provider: To better understand our findings, we met with the College’s Director of Computing Services. We first asked why customers’ continuance intention was relatively low compared to their satisfaction. The director explained that the technology fee had recently been increased by 15% and that this presumably led to a pre-disposed negative attitude. Second, a few service areas (e.g., useful support materials, visually appealing facilities, and maintaining reliable technology) were identified as inferior, possibly leading to customer defection. These service areas were also noted in the written comments of customers: for example, complaints about the lack of user manuals, the messy reception area, and email system and network malfunction. In general, the director was pleased with the analyses and recommendations. His reaction was positive regarding the finding that service value mediated the link between service quality and his IS customers’ reuse of the IS services offered.

1. Computing service employees who instill confidence in students; 2. Making students feel safe in their computer transactions; 3. Computing service employees who are consistently courteous; 4. Computing service employees who have the knowledge to answer students’ questions. Empathy (4 items) 1. Giving students individual attention; 2. Computing service employees who deal with students in a caring fashion; 3. Our college’s computing services has employees who give you personal attention; 4. Having the students’ best interests at heart. Tangible (5 items) 1. 2. 3. 4. 5.

Convenient operating hours; Up-to-date technology; Visually appealing facilities; Computing service employees who appear professional; Useful support materials (such as documentation, guides, training video, etc.).

6. Conclusions Customer retention is critical: it is more expensive to attract new customers than to retain old ones. The goal of our study was to position the SERVQUAL instrument in a BIs model, in light of the increased importance of customer retention. A new model of SERVQUAL was compiled from prior service quality research. Data from a field survey of customers of an ISF provided empirical support for the model. Apparently service value and satisfaction mediate the relationship between service quality and intention to reuse an IS service. While satisfaction leads to customers’ service reuse, IS service value, interestingly, had a stronger effect on service reuse. Thus, the original SERVQUAL instrument alone may not be sufficiently robust to predict customer retention and should be augmented with measures of IS service value and satisfaction. The resultant model can be useful for both academics and practitioners.

A.2. IS service reuse—behavioral intentions Based on my experiences with the computing service. . . 1. Consider our computing services your first choice when you want to use computing related services (Strongly disagree . . . Strongly agree); 2. Use more of our computing services (Strongly disagree . . . Strongly agree); 3. I intend to continue using our college’s computing services rather than discontinue its use (Strongly disagree . . . Strongly agree); 4. My intentions are to continue using our college’s computing services rather than use any alternative means (Strongly disagree . . . Strongly agree).

Appendix A. Questionnaire items

A.3. Satisfaction

A.1. SERVQUAL (perceived service quality)

How do you feel about your overall experience of using our computing services?

My perception of computing services performance is (Very low. . . Very high), when it comes to. . . Reliability (5 items) 1. Performing services right the first time;

1. 2. 3. 4.

Very dissatisfied . . . Very satisfied; Very displeased . . . Very pleased; Very frustrated . . . Very contented; Absolutely terrible . . . Absolutely delighted.

W.J. Kettinger et al. / Information & Management 46 (2009) 335–341

A.4. Service value 1. Compared to what I had to give up (e.g., technology fees, time, energy, and effort), the overall ability of computing services to satisfy my wants and needs is (Very low . . . Very high); 2. Compared to other computing service providers, the value of the computing services is (Very low . . . Very high); 3. The value of the computing services in functional terms is (Very low . . . Very high); 4. Overall, the value of the computing services to me is (Very low . . . Very high).

References [1] A. Bhattacherjee, Understanding information systems continuance: an expectation–confirmation model, MIS Quarterly 25 (3), 2001, pp. 351–370. [2] L. Chen, M. Gillenson, D. Sherrell, Enticing online consumers: an extended technology acceptance perspective, Information & Management 39 (8), 2002, pp. 705–719. [3] J.J. Cronin, M.K. Brady, G.T.M. Hult, Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments, Journal of Retailing 76 (2), 2000, pp. 193–218. [4] W.H. DeLone, E.R. McLean, The DeLone and McLean model of information system success: a ten-year update, Journal of Management Information Systems 19 (4), 2003, pp. 9–30. [5] L. Hu, P.M. Bentler, Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives, Structure Equation Modeling 6, 1999, pp. 1–55. [6] J.J. Jiang, G. Klein, S.M. Crampton, A note on SERVQUAL reliability and validity in information system service quality measurement, Decision Sciences 31 (3), 2000, pp. 725–744. [7] W.J. Kettinger, C.C. Lee, Zones of tolerance: alternative scales for measuring information systems service quality, MIS Quarterly 29 (4), 1997, pp. 607–621. [8] Q. Ma, J.M. Pearson, S. Tadisina, An exploratory study into factors of service quality for application service providers, Information & Management 42 (8), 2005, pp. 1067–1080. [9] R.L. Oliver, Satisfaction: A Behavioral Perspective on the Consumer, McGraw-Hill, New York, NY, 1997. [10] R.A. Peterson, On the use of college students in social science research: insights from a second-order meta-analysis, Journal of Consumer Research 28 (3), 2001, pp. 450–461.

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[11] J.C. Sweeney, G.N. Soutar, Consumer perceived value: the development of a multiple item scale, Journal of Retailing 77 (2), 2001, pp. 203–220. [12] J. Van Iwaarden, T. Van Der Wiele, L. Ball, R. Millen, Perceptions about the quality of web sites: a survey amongst students at Northeastern University and Erasmus University, Information & Management 41 (8), 2004, pp. 947–959. [13] V.A. Zeithaml, L.L. Berry, A. Parasuraman, A behavioral consequences of service quality, Journal of Marketing 60 (2), 1996, pp. 31–46. William J. Kettinger is the FedEx Endowed Chair in MIS at the Fogelman College of Business and Economics at The University of Memphis. He previously served as a professor of IS and Moore Foundation Fellow at the School of Business of the University of South Carolina. Bill has also taught at IMD in Switzerland, Wirtschaftsuniversita¨t Wien in Austria and at the Tecnologico de Monterrey in Mexico. He publishes in such journals as MIS Quarterly, Information & Management, JMIS, JAIS, Decision Sciences, and Sloan Management Review. He currently serves, or has served, on the editorial boards of MIS Quarterly, ISR, and JAIS. Sung-Hee ‘‘Sunny’’ Park is an assistant professor of business at Kettering University in Flint Michigan. He received his PhD in MIS from the University of South Carolina in 2007. He has considerable prior consulting experience in Asia and in the United States which he brings to bear in both his teaching and pragmatic research. His scholarly interest include: Information Technology Adoption, Information Technology Management, Electronic Commerce, and IS Service Quality. He has published in numerous high quality proceedings and the International Journal of Operations & Production Management. Jeffery S. Smith is an assistant professor of marketing at the Florida State University College of Business. He received his PhD from University of South Carolina in 2006. His research interest includes: Service Operations, Service Recovery, Service Design, Technology Implementation and Supply Chain Management. He has published in the International Journal of Production Economics and the International Journal of Operations & Production Management.