<b>Research Note</b>—The Influences of Online Service Technologies and Task Complexity on Efficiency and Personalization
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".