UNDERSTANDING INDIVIDUAL ADOPTION AND USE OF SOCIAL COMPUTING: A USER-SYSTEM FIT MODEL AND EMPIRICAL STUDY
Bibliographic record
Abstract
During the last few years, the phenomenon of Web 2.0, or social media, has gained growing attention both in academic research and in practice. Evidence suggests that a complex and rich set of motives drive individuals to use social media. However, traditional models of IT acceptance generally do not account for these motives, and thus are not particularly suited to explain the adoption and use of social media. Indeed, a central construct in many of these models, the „usefulness‟ construct, exclusively focuses on productivity and/or performance-related motivations and seems too narrow in a social media context. The current study makes an effort to provide improved understanding of that important phenomenon by offering two contributions. First, the research expands the existing conceptualizations of usefulness through the construct of „needs-supplies fit‟. Based on theories of motivational needs and person-environment fit, needs-supplies fit is conceptualized as a second-order construct with dimensions that span a wide spectrum of needs, including both extrinsic and intrinsic needs. Building on the extant literature and extensive interviews, this research develops a user-system fit model. The model comprises the perceived user-system fit construct, a third-order multidimensional construct that is a combination of four dimensions of fit: user-expression, needs-supplies, demands-abilities, and user-group fit. Perceived user-system fit, is hypothesized to be positively associated with social media use. The model is tested using a Web-based survey of 643 undergraduate students in a large Canadian university. Results indicate that 4 of 5 hypotheses are supported and that the user-system fit model explains 32.2% of social media usage of respondents.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".