How financial markets reflect the benefits of self‐service technologies
Bibliographic record
Abstract
Purpose Self‐serve technologies (SSTs) provide many benefits such as speed, time and place convenience for the customer and reduced labour costs for the firm. The aim of this study is to consider whether these benefits are denoted by changes in the firms' stock price when SSTs are introduced. Design/methodology/approach Using data from banking, retail (grocery and gas), and airline industries, this event study considers overall effects of SST implementation on stock price, and also considers effects in three sub‐categories: industry, time period, and scope of announcement (i.e. corporate vs. regional). Findings SST announcements had a positive effect on firm value during the late 1990s. However, for the most part, financial markets do not respond to SST announcements. This is in line with the strategic necessity hypothesis and the resource‐based view of the firm, but may also be partly due to the phased rollouts that are typical of these implementations (which dilute the impact over time). Research limitations/implications It proved quite difficult to locate original public announcements of SST investments in publicly traded companies; thus, the sample size is smaller than desired. However, a bootstrapping method was used to crosscheck the findings. Practical implications Firms should not promise investors immediate increase in firm value, but rather demonstrate the benefits from a longer term, competitive and customer‐oriented perspective. Originality/value This is the first study to consider the effect of implementing SSTs using event study methodology. Most prior SST studies have considered behavioural aspects of the implementation, while most prior event studies that have considered IT implementations have done so in a general sense, not focusing on a specific technology. Using a new dataset collected from two decades of SST implementations, this study focuses on the impact of SSTs from a different perspective.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".