2012 IEEE/ACIS 11th International Conference on Computer and Information Science
Integrating Focal Determinants of Service Fairness into Post-Acceptance Model of IS Continuance in Cloud Computing Montri Lawkobkit
Mark Speece
Dhurakij Pundit University Bangkok, Thailand e-mail:
[email protected]
University of Alaska Southeast Alaska, USA e-mail:
[email protected]
Fairness, then, helps shape perceptions of satisfaction. In practice, IS service providers in a competitive market seek to meet or exceed customer satisfaction levels, which encourages customers to continue to use their systems. Customer retention is critical to long-term profitability in services [e.g. 3]. Customer satisfaction is influenced by numerous variables. Among these are organizational fairness variables, which influence customer satisfaction by exerting influence upon individual satisfaction.
Abstract—This study integrates the focal determinants of service fairness into a post-acceptance model of information system continuance. This study added structural and social service fairness constructs based on Greenberg’s (1993) focal determinants of organizational justice. The research model seeks to be useful in predicting satisfaction, which enhances continued usage of an IS. The results show that perceived usefulness and satisfaction influence continuance intention, as the post-acceptance model predicts. Structural service fairness significantly enhances satisfaction. However, social service fairness is not significant.
This research examines two interrelated research streams, integrating focal determinants of fairness from Greenberg’s (1993) taxonomy of organizational justice or fairness into PAM. In PAM, perceived usefulness and satisfaction are two constructs that directly influence the intention to continue using an IS. This research demonstrates the relationship of the focal determinants of service fairness with satisfaction. The two distinct fairness constructs are structural and social fairness. Enhancing satisfaction through service fairness would then improve IT continuance intention through the PAM relationships. Fig. 1 presents the conceptual model and the hypothesized relationships.
Keywords— structural and social service fairness; information system use; satisfaction; post-acceptance model
I. INTRODUCTION Customer satisfaction in information technology (IT) service support has a major impact on the intention to maintain contact with service providers who manage and provide service technologies. There is a subtle distinction between continuing to use a service technology versus continuing to obtain the service from a particular service provider, and a similar distinction between satisfaction with a service technology versus satisfaction with the technology’s service provider. This research focuses on satisfaction with service providers in a context where the service is provided through a technology. While most prior information system (IS) research has attempted to explain user acceptance of new IT, recent work has focused on IS continuance or continued usage. The technology acceptance model (TAM) and expectation confirmation theory (ECT) are the dominant theoretical frameworks explaining user acceptance and continuance of IT [1]. In addition, research on continuance intention widely uses a post-acceptance model (PAM) of IS continuance [2]. Satisfaction is often a key issue in such research.
Figure 1. Conceptual model
The context of the study is Software-as-a-Service (SaaS) in a cloud computing environment with the IS application and SaaS users as the IS sample. Cloud computing is an emerging technology enhancing subscribers’ perceptions of SaaS as long term partners. It is a good example of the wider SaaS market, which is rapidly growing as developers and service providers continue to make investments in developing technologies.
This research proposes a theoretical integration into PAM, which argues that perceived usefulness and satisfaction are necessary for IS continuance intention. Satisfaction is contingent on customer perceptions of service fairness with an organizational service provider who provides a technological product together with services.
978-0-7695-4694-0/12 $26.00 © 2012 IEEE DOI 10.1109/ICIS.2012.63
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II. LITERATURE REVIEW
application [6]. Levels of customer satisfaction result from many factors, although these are all grounded in the customer’s experiences of the service and the interaction with the service provider.
A. Post-Acceptance Model of IS Continuance The post-acceptance model (PAM) of IS continuance proposed by Bhattacherjee (2001b) seeks to explain user intentions to continue or discontinue using an IS. The upper part of Fig. 1 illustrates the key constructs and relationships of the PAM model where perceived usefulness and satisfaction directly influence IS continuance intention. The model was developed based on ECT as used in research on consumer behavior [4]. Continuance behavior may be defined as explaining user intentions to continue (or discontinue) using an IS, where a continuance decision follows an initial acceptance decision.
A number of studies empirically validate the linkage of confirmation and satisfaction [2, 7, 8]. PAM explains this relationship by noting that confirmation implies a realization of the expected benefits of IS usage, while satisfaction assesses the user’s positive or negative experience of using the IS. Realization of benefits will result in satisfaction. As customers continue using the system with good results, confirmation reinforces satisfaction. Therefore, the following hypothesis is proposed: H2: The extent to which a user’s expectations are confirmed is positively associated with the level of satisfaction.
The model assumes that a user’s expectation toward using an IT system, after initial acceptance and use, should not be different from his/her expectations before using it, if pre-acceptance expectations are confirmed and the system meets prior expectations (perceived performance equals expectations). This confirmation will influence both satisfaction and perceived usefulness. Users form judgments about benefits from perceived usefulness, and so the intention to continue using an IS will be influenced by both perceived usefulness and satisfaction. The model thus parsimoniously explains decisions to continue to use an IS.
Perceived usefulness also influences satisfaction. To understand this assumption, it should be remembered that perceived usefulness assesses the degree to which an IS gives access to increased performance, while satisfaction assesses the user’s positive or negative experience of using the IS. According to PAM, perceived usefulness and satisfaction should be positively and significantly correlated, and previous research demonstrates that usefulness perceptions impact attitude during both pre-acceptance and post-acceptance stages of IS use. Bhattacherjee’s (2001b) study showed that perceived usefulness influences the satisfaction of individual users.
In line with Bhattacherjee (2001b), this study assumes that confirmation of expectation and perceived usefulness from prior use are the main antecedents of post-acceptance user satisfaction. Confirmation in this study is defined as an individual user’s perception of congruence between the expectation prior to use and actual performance [2]. Perceived usefulness is the degree to which an individual user believes that a particular system delivers benefits, notably that it will enhance his or her job performance by reducing the time to complete a task, and facilitate the completion of tasks with high quality [2].
In accordance with these observations, the more a user perceives the system to be useful, the more satisfied he or she will be with the system. Thus, the third hypothesis proposed is as follows: H3: The perceived usefulness of the IS is positively associated with a user’s level of satisfaction with using the IS. As noted above, TAM provides a limited explanation of continuance behaviors. By itself, or even with its extensions, TAM is somewhat weak in the ability to predict post-acceptance continued IT usage [9, 10] or to explain discontinuance after successful acceptance [2]. Bhattacherjee (2001b) notes that the “long-term viability of an IS and its eventual success depend on its continued use rather than [its] first-time use.” (pp. 351-352). This is the basis for the distinction of PAM from TAM, with PAM’s focus on IS continuance intention.
Customers’ initial expectations can be easily confirmed (or not) once they have actual experience. Customers may have varying experiences, and adjust their perceptions to be consistent with their perceived reality. If use of the system generates worse results than expected, the disconfirmation will negatively alter their prior perceived usefulness, but good results will enhance perceived usefulness. Thus, confirmation positively influences perceived usefulness. Bhattacherjee (2001a, 2001b) showed this relationship in the context of business-to-consumer e-commerce services [5]. These considerations lead to the hypothesis:
Perceived usefulness is one of the two main antecedents to the intention to use in TAM, and it also directly influences subsequent IS continuance intention in PAM. Based on TAM, perceived usefulness can significantly influence a user’s decision to adopt an IS. Bhattacherjee’s (2001b) study showed that perceived usefulness also influences a user’s decision to continue to use an IS. Perceived usefulness will positively influence the continuance intention and lead to continued use of the system., This, therefore, yields the fourth hypothesis.
H1: The extent to which a user’s expectations are confirmed is positively associated with the level of perceived usefulness. One of the definitions of satisfaction from Spreng, MacKenzie, & Olshavsky (1996) is “an affective state that is the emotional reaction to a product or a service experience” (p. 17). User satisfaction is therefore defined as the end-user’s perception when interacting with a specific
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H4: The perceived usefulness of the IS is positively associated with intention to continue using the system.
Several previous studies have revealed a relationship between social fairness and both managerial performance [27] and employee behaviors [28]. Social fairness has become one of the important components of outcome fairness. In a transformational leadership study, social fairness had more impact than structural fairness because the leader cares about the needs and well-being of the followers and wants to be open and responsive [25, 29, 30].
User satisfaction is a significant factor in the IS context [2, 11, 12]. Online, e-satisfaction is a key determinant of technology acceptance and continued usage [13, 14]. PAM views relationship satisfaction as a basis for the continued intention to use IS; user satisfaction with prior use has a strong positive impact on user intention to continue using the system [2]. Other IS researchers have also found that user satisfaction is a strong predictor of system use [15]. Thus, the relationship between satisfaction and IS continuance intention can be stated as:
This study is motivated to the view of Greenberg’s (1993) taxonomy that positions the focal determinants of fairness as broader areas which are based on the immediate focus of a just action relative to existing categories of fairness. The two specific determinants of service fairness which these give rise to are:
H5: Satisfaction with prior IS usage is positively associated with IS continuance intention. Some studies have extended PAM, adding complexity to examine various antecedents. Most of those issues are outside the scope of this research, which aims specifically to determine the impact of service fairness on satisfaction. This research shows that the basic PAM works in a cloud computing context; otherwise there would be little need to worry about satisfaction. However, the various extensions are not necessary for simply showing that PAM works, so the original simple PAM framework is used. Here, the basic model is extended from satisfaction, which is a key construct influencing continuance intention (as in H5). Thus, the discussion now turns to examining service fairness from the standpoint of organizational fairness and with respect to its influence on user satisfaction.
1) Structural Determinants of Fairness: Structural determinants of fairness refers to the structural elements of the organization and focuses on the environmental context within which interaction occurs [31]. In service delivery, structural fairness refers to structural elements of the service provider that allow involvement of their customers in decision-making, and provide a fair distribution of outcomes. When customers perceive high structural fairness, they will believe that an unfair outcome was merely an accident and will expect structural fairness to occur the next time. That is, they will be less likely to terminate their relationship with the service provider, and they remain satisfied with the service. Additionally, customer satisfaction will increase if the service provider provides advanced technological support to monitor and track their service, especially with on-line customers. Empirical results support the concept of perceived structural fairness that has a direct impact on outcomes [24-26].
B. The Structure of Organizational Fairness Organizational fairness is an important construct which has been widely discussed in the field of organizational behavior [16, 17]. (Prior studies have used the term ‘justice’ and ‘fairness’ interchangeably. Here, the term ‘fairness’ is used for the purpose of consistency.) Organizational fairness has also received attention in the context of employee perceptions of fairness in the workplace with regard to matters such as job satisfaction, complaint handling, human research management [18], customer satisfaction with services and service delivery [19, 20].
When customers feel they have been treated equally (or not) with respect to the final service outcomes, customers judge that this comes partly from how the system is structured. Feelings of structural fairness can be important between the customers and the service provider, as individual customers feel they should receive the same services from the service personnel as anyone else. Customers can have negative feelings if they find that they receive fewer resources than others. Customer feelings of having experienced a fair process can be used to increase customer outcomes (i.e. satisfaction). This consideration leads to the following hypothesis:
Organizational fairness may be defined as the perception of fairness by an individual in the working environment [21, 22]. Greenberg’s (1993) rudimentary taxonomy highlights the distinction between structural and social determinants of fairness. The taxonomy is formed with two independent dimensions: category of fairness (procedural and distributive), and focal determinants (structural and social).
H6: Perceptions of structural service fairness will be positively associated with satisfaction.
The concept of focal determinants has been one of the major research areas in organizational psychology [23]. Previous studies have discussed the focal determinants in the area of strategic decision making in leadership and ethics [24, 25], and human resource management (HRM) in compensation and performance management [26].
2) Social Determinants of Fairness: Social determinants of fairness is recognized as one of the important sources of fairness perceptions [31] and Greenberg (1993) proposed this distinguishable fairness in the taxonomy. Social fairness focuses on the treatment of individuals and informational exchange [31] by “showing concern for individuals regarding the distributive outcomes
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they receive” (p. 85), and “may be sought by providing knowledge about procedures that demonstrate a regard for people’s concerns” (p. 84).
A. Sample and data collection The pilot testing and the main study included individuals in small and medium sized enterprises who use business-tobusiness (B2B) CRM-SaaS in cloud computing. For both the pilot and the main study, an online professional marketing research firm which has access to over four million respondents worldwide, implemented an online survey. The panel members were recruited according to pre-qualifying characteristics, and they have a choice whether or not to answer any specific survey. The respondents were CRM-SaaS users.
In IT service delivery, social service fairness refers to the customers’ perceptions that the service provider cares about their wellbeing and keeps customers informed before and during changes to the service process. Additionally, social fairness can take the form of any information provided by service providers. Customers are given information about services they have received or with which they have been involved. When customers feel they have been treated fairly, with respect, sincerely and politely by the service provider, for personnel throughout the service delivery process, the level of customer satisfaction will increase. High levels of informational fairness may be achieved by being truthful in all communications and tailoring service providers’ explanations to customer needs.
A web-based survey is an appropriate choice for this study because of the characteristics of the research subject (i.e., CRM-SaaS subscribers’ access the software via Internet on a daily basis) [38].Because the sample has frequent and easy access to the Internet, and are comfortable using it, they are more likely to answer on the Internet. Therefore, web-based surveys do not have restricted geographical location, are likely to gain higher numbers of responses, and may extract longer and more substantive quality answers than a mail survey [5, 39].
When customers or users perceive a fair interaction and a fair information exchange before, during, and after the service delivery process from the perspective of social fairness, this can lead to positive or increased customer outcomes. From this, the following hypothesis is developed:
Recruitment e-mails were sent to 31,015 prospective panel members nationwide in the USA identified from company databases of full-time employees working in organizations. The first response rate was 11.58% (3,591). Four stringent screening questions reduced this to 490 questionnaires, at a response rate of 1.58%. The screening questions ensured that
H7: Perceptions of social service fairness will be positively associated with satisfaction. These two service fairness dimensions should have an impact on satisfaction, and H6 – H7 address the question of whether an individual’s perception of the focal determinants of fairness (structural/social) is strong enough to influence customer satisfaction, thus indirectly contributing to the IS continuance or continued usage. This study applies a conceptual model in which the perceptions of the focal determinants of service fairness are integrated with PAM based on Bhattacherjee’s (2001b) model (Fig. 1).
•
The respondents used CRM software over the Internet in their work place. A list of specific, common CRM-SaaS was used to make sure the applications were comparable. • The respondents’ organization had used the software for more than 2 years, so their answers are about continuance, rather than adoption and the trial use period. • Respondents used the software at least once a week for their work, which is considered as using the software as part of normal routine activity, and • The respondents had contacted the software service provider for support. If they have not had any interaction(s) with the software service provider and/or the software service provider personnel, they did not qualify to take part in the survey. Since the usable response rate was relatively low, tests for non-response bias were performed by comparing answers on the last quartile of the responses returned with those of the first quartile [40]. There were no differences in the mean of any item in the model constructs, and only two differences in the variances. This indicated that nonresponse bias was not a significant problem and the survey was able to collect adequate data in this research.
III. METHODOLOGY We employed several previously published measures with some modification and supplementation reflecting the specific IS context and the targeted users. Items for basic PAM were developed by Bhattacherjee (2001b) and several other researchers [e.g. 32, 33, 34]. Fairness items were adapted from a number of works, but generally follow [3537]. All items were reworded to relate specifically to customer relationship management (CRM) SaaS, called ‘the software’ throughout the survey questionnaire (Appendix 1A and 1B). All survey items had response options ranging from (1) to (7), representing ‘strongly disagree’ (1) to ‘strongly agree’ (7). Variables were measured using a previously established scale where possible. The initial questionnaire was reviewed by a panel of seven experts from IS academia and industry IS management, followed by a small pilot survey (n=60). This pilot showed good results on the basic PAM and service fairness concepts. The main survey was then carried out.
Males constitute 61.22% of the 490 respondents. The majority (64.70%) is in the age range from 30 years to 50 years old, and nearly ninety percent (88.98%) had over 5 years working experience. The most common positions
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variables is high, as indicated by the R2 values. The highest R2 appeared in IS continuance intention (78%) and the next highest R2 were shown in perceived usefulness (73%) and satisfaction (69%) (Table 2).
were operating staff (16.73%), supervisors (15.51%) and sales representatives (13.06%). Half of the respondents (50%) were from organizations employing between 51 and 500 employees. Respondents from the business services industry (51.84%) made up the highest percentage. In summary, the sample constituted an experienced workingage group, with responsibility at their present company requiring frequent use of CRM software, and who interact with the software service provider. IV. RESULTS Table 1 presents descriptive statistics for the composite variables used for each construct measure. The internal reliability of the measures ranged from .830 to .938 for the post-acceptance model and from .956 to .960 for the two fairness dimensions. All the measures included in the questionnaire showed adequate levels of initial internal consistency reliability (> .70). A correlation matrix of variables (not presented) showed that in general, the correlations were consistent with theoretical expectation. However, the relationship between structural fairness and social fairness (0.908) was above the border line of 0.85 [41], which might indicate some multicolinearity problems.
*significant at p = .05
Figure 2. A full model fit TABLE 2. STANDARDIZED PATH COEFFICIENTS Dependent (R2) 2
Determinant (hypothesis)
Coefficients (P-value) .852 (.000)
Usefulness (R =.726)
Confirmation (H1)
Continuance Intention (R2 = .775)
Usefulness (H4) Satisfaction (H5)
.239 (.000) .718 (.000)
Satisfaction (R2 = .686)
Confirmation (H2) Usefulness (H3) Structural fairness (H6) Social fairness (H7)
.442 (.000) .212 (.022) .532 (.000) .002 (.986)
TABLE 1. CONSTRUCT DESCRIPTIVE STATISTICS AND RELIABILITY Variable (number of items)
Mean
S.D.
Usefulness (4)
5.643
1.086
Cronbach’s Alpha .938
Confirmation (3)
5.399
1.011
.830
Continuance intention (3)
5.582
1.041
.893
Satisfaction (4)
5.512
1.088
.929
Structural fairness (12)
5.510
0.957
.956
Social fairness (10)
5.597
0.990
.960
V. CONCLUSION The first objective of this study is to examine whether continued usage in cloud computing can be determined by the variables in the post-acceptance model (H1 – H5). The results show that all five hypotheses are supported, demonstrating that basic PAM works well in this context.
The structural model was estimated using AMOS (SPSS version 18). It was accepted (chi-square = 1302.320; df = 201, p = .000, relative chi-square = 6.479). The path coefficients for the structural model are shown in Table 2. The standardized regression weights (Figure 2) between independent and dependent variables shows a strong path (with statistical significance) for all hypothesized relationships, except between social fairness and satisfaction, which was not significant.
The second objective (H6 – H7; bottom part of Figures 1 & 2), proposes that perceptions about the focal determinants of service fairness can explain and predict individual satisfaction. In other words, this study explores whether the focal determinants of service fairness issues have some indirect impact on the continued use of the system through satisfaction. The findings show a positive and significant path from structural service fairness to satisfaction. That is, satisfaction with the service delivery process is affected by the processes and value outcome.
Standardized estimates and standardized regression weights are presented in Fig. 2 and Table 2. The first set of hypotheses (H1 – H5) tested whether PAM can be applied in this research context. All five hypotheses were supported. The findings confirm that PAM [2] can be used here.
The path from social service fairness to satisfaction is not significant. There are several possible reasons for the negatively significant result between social fairness and satisfaction. Some of the problems may come from multicolinearity, with relatively high correlations (above the cut-off point) between the structural fairness and social fairness dimensions (0.908). At this point, we do not have a
Among the second set of hypotheses (H6 – H7), analysis of path coefficients indicates that H6 is supported. The influences of structural fairness (coefficient = 0.532) on satisfaction was positive and significant. However, interestingly, the impact of social fairness on satisfaction was not significant (Table 2). The impact of the endogenous
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[3]
good explanation for this result. This issue clearly needs additional research. Nevertheless, the basic PAM was shown to hold, and the basic concept that various dimensions of service fairness have an impact on satisfaction in the PAM model was also confirmed. This study does, of course, have several limitations. First, the scope is limited to the context of SaaS enterprises in a cloud computing environment. While this is an important and increasingly widespread context, it would be beneficial to replicate the study to broaden the contexts. For example, related sorts of environments could be public SaaS, Infrastructure-as-a-Service (IaaS) or Platform-as-aService (PaaS) applications. Second, this study employed a one-sided survey response from external customers using SaaS in a cloud computing environment. Further study using a dyadic approach could gain in-depth understanding on the responses from both customers and service providers. Finally, this research was cross-sectional, surveyed at one period in time. The findings can only reflect that specific time, but satisfaction is also a product of cumulative experience and may change over time.
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[5]
[6]
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[8]
The limitations have helped to determine other potential directions of future research, but other useful areas for future work have also been identified. First, an IS in a large organizational context, where they have their own system and the IS service is for internal customers, is a potential environment to be investigated. Internal organizational employees account for a large percentage of IS users. Studies of these extrinsically motivated users may contribute many theoretical insights to the IS postacceptance model. Second, testing the research model with different types of IS context will improve the generalizability of the empirical results of this study.
[9]
[10]
[11]
This research has offered an important contribution by integrating the focal determinants of service fairness with the IS continuance intention domain. The focal determinants of service fairness do have a significant impact on satisfaction, and thus, indirectly influence IS continuance. This suggests areas that managers of IS support services need to consider, and points out areas that research on IS management must account for. The focal determinants of service fairness are clearly an important issue for IS users.
[12]
[13]
[14]
Appendix 1A: Items for the basic PAM Appendix 1B: Items for service fairness For those interested, both appendices are available by email from the first author.
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