Online retailers are increasingly providing service technologies, such as technology-based and human-based services, to assist customers with their shopping. Despite the prevalence of these service technologies and the scholarly recognition of their importance, surprisingly little empirical research has examined the fundamental differences among them. Consequently, little is known about the factors that may favor the use of one type of service technology over another. In this paper, we propose the Model of Online Service Technologies (MOST) to theorize that the capacity of a service provider to accommodate the variability of customer inputs into the service process is the key difference among various types of service technologies. We posit two types of input variability: Service Provider-Elicited Variability (SPEV), where variability is determined in advance by the service provider; and User-Initiated Variability (UIV), where customers determine variability in the service process. We also theorize about the role of task complexity in changing the effectiveness of service technologies. We then empirically investigate the impact of service technologies that possess different capacities to accommodate input variability on efficiency and personalization, the two competing goals of service adoption. Our empirical approach attempts to capture both the perspective of the vendor who may deploy such technologies, as well as the perspective of customers who might choose among service technology alternatives. Our findings reveal that SPEV technologies (i.e., technologies that can accommodate SPEV) are more efficient, but less personalized, than SPEUIV technologies (i.e., technologies that can accommodate both SPEV and UIV). However, when task complexity is high (vs. low), the superior efficiency of SPEV technologies is less prominent, while both SPEV and SPEUIV technologies have higher personalization. We also find that when given a choice, a majority of customers tend to choose to use both types of technologies. The results of this study further our understanding of the differences in efficiency and personalization experienced by customers when using various types of online service technologies. The results also inform practitioners when and how to implement these technologies in the online shopping environment to improve efficiency and personalization for customers.
That recommendation agents (RAs) can substantially improve consumers’ decision making is well understood. Far less understood is the influence of specific design attributes of the RA interface on decision making and other outcome measures. We investigate a novel design for an RA interface that enables it to interactively demonstrate trade-offs among product attribute values (i.e., trade-off transparency feature) to improve consumers’ perceived product diagnosticity and perceived enjoyment. We also examine the extent to which the trade-offs among product attribute values should be revealed to the user. Further, based on the stimulus– organism–response model, we develop a theoretical model that extends the effort–accuracy framework by proposing perceived enjoyment and perceived product diagnosticity as two antecedents for perceived decision quality and perceived decision effort, respectively. In an experimental study, we find that (1) the trade-off transparency feature significantly affects perceived enjoyment and perceived product diagnosticity, (2) perceived enjoyment and perceived product diagnosticity follow an inverted U-shaped curve as the level of trade-off transparency increases, (3) although users spend more time understanding attribute trade-offs with the trade-off transparency feature, they are more efficient in selecting a product, (4) perceived enjoyment simultaneously leads to better perceived decision quality and lower perceived decision effort, and (5) perceived product diagnosticity leads to better perceived decision quality without compromising perceptions of decision effort. Theoretically, this study increases our understanding of how the design of an RA interface can improve consumers’ product diagnosticity and enjoyment, and proposes two antecedents to improve perceived decision quality and reduce perceived decision effort. For design practitioners, our results indicate the importance of providing the trade-off transparency design feature to potential consumers.
Wixom and Todd (2005) integrated the user satisfaction and the technology acceptance literatures to theorize about and account for the influence of the information technology artifact on usage. Based on Wixom and Todd's integrated model of technology usage, we propose the 3Q model by investigating the role of service quality (SQ), in addition to system quality (SysQ) and information quality (IQ), in website adoption. Attention to SQ is critical, as consumer websites have increasingly become the target of SQ assessment made by consumers, not just traditional SysQ and IQ evaluations. As part of our study, we further theorize and empirically test the relationships among these three types of quality constructs and hypothesize that perceived SysQ influences perceived IQ and perceived SQ, and perceived IQ influences perceived SQ. Our study extends the Wixom and Todd model in the e-service context and is the first of its kind to empirically examine the combined impact of perceived SQ, perceived SysQ, and perceived IQ on usage intention. Our study advances the theoretical understanding of SQ and the relationships among perceptions of SysQ, IQ, and SQ in the eservice context. The results also inform practitioners that high IQ and SysQ can directly or indirectly improve SQ in the e-service context.