The impact of digital technologies and social media on the urban attractiveness of smart cities
Bibliographic record
Abstract
Smart city initiatives use digital technologies to enhance user experiences and improve the attractiveness of urban environments. However, little is known about how these technologies influence a city's ability to attract different types of newcomers, and even less about the role of social media in this process. This work examines how a city's use of social media influences the relationship between the effect of digital technology implementation and the urban attractiveness for national and international newcomers. Focusing on three types of national and international newcomers (i.e., citizens, students, and tourists) to a city, we present and test a model of how social media curates, broadcasts, and accelerates information flows about the availability and value of smart city technology to newcomers. Using novel data from 30 Italian cities (2010−2021), we find support for this model, with digital technologies having a curvilinear impact on urban attractiveness, and that social media extends the threshold of this impact. Moreover, we find that these effects differ for national and international newcomers. These findings challenge smart city scholars and practitioners to reconsider the ‘more is better’ narrative that assumes increasing technology implementation is always beneficial, highlighting instead the value of contingency-based approaches over one-size-fits-all technological determinism. • Digital technology implementation shows a curvilinear relationship with urban attractiveness, indicating that benefits are not unlimited. • Social media use by cities extends the tipping point of digital technology's positive effects, but this advantage is mainly for national newcomers. • Findings challenge the “more-is-better” assumption in smart city research, highlighting the value of differentiated, user-centric digital strategies. • The asymmetric effects between national and international newcomers underscore the need for tailored policy design and targeted investment priorities.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".