System Design Features and Repeated Use of Electronic Data Exchanges

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Research Implications

These results show how important it is to study inter-organizational phenomena over time. The study presents findings consistent with past research (e.g., [64]) in which initial (T1) outcome feedback did not have a significant effect on data exchange information quality beliefs. In T2 use of electronic data exchanges, however, outcome feedback was found to have a significant impact on PIQ. As a result, the measurement of constructs across time should enable the examination of a network of beliefs that relate to the success of data exchanges. Limiting the examination to initial use exchanges fails to capture this.

We used structural assurance [30] as a control variable, and found that it affected intention to use directly, although this impact decreased somewhat in T2. Table 6 shows that while much of structural assurance’s effect was suppressed by PIQ (as shown in the two halves of Table 6, where structural assurance had highly increased effects when PIQ was not present), it still had a significant effect. Structural assurance implies that institutional controls are in place to protect the exchange user from transactional problems, providing a safe and secure environment. This should be important to exchange customers. Future research should expand on this finding by modeling the effects of other variables (e.g., asset specificity, goal incongruence).

The results show PIQ becoming a more powerful predictor of intent to use over two time periods, which expands on the findings of Nicolaou and McKnight [64]. Future research should examine whether this trend continues indefinitely or slows/stops over time. We speculate that it continues for a while and then slows, as direct quality of the user’s exchange experience becomes more salient.

Table 9 shows several other ways to expand beyond this study. First, researchers should use other theoretical perspectives to examine the electronic data exchange across time. Table 9 shows that exchanges could be researched by comparing our theory to such other theories as the elaboration likelihood model [69] or the vulnerability-stress-adaptation model Karney and Bradbury [42]. Second, research studies could use the current research model to study people-to-people relationships in other IS domains. For example, the IS-vendor outsourcing relationship and the user-systems developer relationships could also be studied using similar relational constructs as this study used. Control transparency could be adapted to reflect a similar enhancer of communication between people. Quality of information exchanged between people would be the result. Third, this and similar models should be tested using other methods. Our two period study should be retested with more time periods in order to examine longer term effects. In conjunction with the elaboration likelihood model of cognitive processing and attitude change, for example, future models could examine repeated interactions and simulate the optimal number of repetitions needed to achieve enduring attitudes that are resistant to incremental cues and predictive of future behavior. We also recommend including measures that are not self-reported [42].

Study Limitations

This study is limited to one experimental context that may or may not generalize to other contexts. Future research in other contexts and with other experimental or field-study methods can test how well these results generalize. The experimental setting used here does not represent the complexity one would find in real world exchanges among competing and cooperating partners [61]. Our setting more closely resembles a “spot market” exchange than the complex, relational exchange one would find between a major manufacturer (e.g., Ford) and its suppliers. Future research should attempt to capture more of the richness of such real world exchange settings. Another limitation is that several of the PIQ items did not work, so we had to eliminate them. This suggests more measurement work is needed to further refine and validate the PIQ scale of measurement. The control transparency treatment is another limitation. While we tried to form the treatment to represent the transparency of the data coming back to the user about the order s/he placed, the treatment also may indicate to the user that proper controls exist. We caution that the results may be due to either the data transparency or the control the transparency communicates or both.

Implications for Practice

Exchange providers should be careful to provide the right kind of information transparency and outcome feedback on their exchange. Our study finds both design features influence perceived information quality (PIQ), but at different points in time. We also find that PIQ affects exchange success. That is, favorable perceptions of information quality produce greater customer intent to continue to use the exchange. Hence, efforts to improve control transparency, outcome feedback and PIQ will have a large impact on intent to use the exchange. But at T1, users provide the supplier some outcome feedback slack that is not continued long.

No matter how transparent the exchange communication is, the repeated reception of negative feedback about exchange fulfillment outcomes will harm customer PIQ perceptions. This research shows that while positive feedback becomes more valued with continued use, two rounds of negative shipment feedback will negate any positive initial impressions gained from a best-practice exchange design. Therefore, providers should make sure their fulfillment mechanisms work well, and should provide this positive feedback to their customers on a timely basis. This study not only guides organizations to design online data exchanges properly, but also demonstrates the consequences of repeated online transaction failure.


This study produced two key findings. First, it demonstrated clearly that outcome feedback affects perceived information quality (PIQ) and intent to use the exchange differently between the first and second transaction (T1 and T2), occurring within a two-week time period. Second, it showed that negative outcome feedback decreases the effects of control transparency on the intent to use the exchange from T1 to T2. In addition, it shows that PIQ becomes even more predictive of a user’s intent to continue using the exchange by the second transaction. Overall, it shows that the two system design interventions of control transparency and outcome feedback can influence intent to use electronic data exchanges. However, a system designer should not rely solely on providing transparent signals about the data exchanged. While this is found important in both of the first two transactions of use, the effects diminish due to outcome feedback change from the first to the second transaction. Both system designers who employ electronic data exchanges and researchers who examine their effective design, must consider the effects of both control transparency and outcome feedback interventions across time.

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Outcome Feedback

Ctrl Transparency:

General Feedback

Specific Positive

Specific Negative


Exchanges A and C *

Exchanges A and D

Exchanges A and E


Exchanges B and C

Exchanges B and D

Exchanges B and E

* Note: Letters indicate the types of data exchanges used in each experimental cell. The first letter indicates the exchange used to vary control transparency (A or B; hi/lo transparency, respectively), while the second letter indicates the exchange used to vary outcome feedback (C, D, or E; general, specific positive, specific negative feedback, respectively).

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