Understanding the impacts of increasing returns in the context of social media use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Social networking sites (SNS) follow the same diffusion pattern and are subject to the same phenomena as other technologies (e.g. QWERTY keyboard, Microsoft Office and VHS) that were subject to increasing returns. Since they may lock-in users, increasing returns significantly alter the way a technology is used and should be managed. The purpose of this paper is thus to verify if SNS are subject to increasing returns and, if so, to better understand their impacts in this context. Design/methodology/approach A research model that combines path dependency theory (PDT) tenets with the push-pull-mooring (PPM) model of information technology (IT) switching was developed and tested with data collected from 416 SNS users via a field survey. Participants were voluntary students at a North American university enrolled in a compulsory undergraduate course in business administration. Partial least square analysis structural equation modeling (PLS-SEM) was used to validate our research model and test our hypotheses. Findings Results show that SNS are subject to three forms of increasing returns: those stemming from device complementarity, learning and adaptive expectations. In addition, the findings show that increasing returns stemming from SNS use have the potential to lock-in SNS users by increasing their switching costs. Practical implications SNS users should be careful when using an SNS since such use can create a path that is self-reinforced and that can lock them due to the increasing returns it yields. SNS vendors/providers need to learn how to manage increasing returns if they want to foster continued use of their SNS and/or poach users from their competitors. Lastly, SNS regulators should revise or put in place new governance mechanisms since increasing returns, when properly leveraged, may undermine fair competition by allowing companies to lock-in users and lock-out competitors. Originality/value This study contributes to IS research by: (1) empirically demonstrating that increasing returns are present in the context of SNS use, (2) identifying increasing returns as key antecedents of user switching costs, (3) validating a theoretical framework that allows for the appraisal of PDT tenets in a variance model and (4) instantiating PDT tenets at the individual level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it