Information Professionals’ Attitudes Influence the Diffusion of Information and Communication Technologies
Bibliographic record
Abstract
A Review of: Rabina, D. L., & Walczyk, D. J. (2007). Information professionals’ attitude toward the adoption of innovations in everyday life. Information Research, 12(4), 1-15. Objective – This study examined the general characteristics and patterns of librarians in connection with their willingness to adopt information and communication technologies. Design – Online questionnaire. Setting – General distribution to information professionals through online inquiry. More than 70% of responders worked in public or academic libraries. Subjects – Librarians and library staff at mostly public and academic libraries. Methods – The study was conducted during a two week period in April 2006 through an online questionnaire that was sent to library and librarian-related electronic mail lists. The questionnaire was divided into two parts and contained a total of 39 questions. Part one contained eight questions that asked for demographic data and the respondent’s daily attitude toward the adoption of information and communication technologies. Questions regarding age, number of years worked in a library, career, type of library environment worked in, and primary responsibilities within that environment were asked. For one question the respondents were asked to identify which of the categories they fall under when adopting a new technology. The results from part one were used to consider the innovativeness of librarians. The results from part two were used for a study of opinions on innovations and their relative advantage. Main Results – A total of 1,417 responses were received. Of those, 1,128 were fully completed and considered valid and used for inquiry. The majority of respondents worked in public or academic libraries. Nine hundred and twenty-six respondents, or 88%, were from the U.S. and represented more than 300 distinct zip codes. Two hundred and two respondents, or 12%, were international respondents. This study notes that the sociologist, Everett Rogers, identified and defined five adopter categories in 1958. Those categories are: innovators, early adapters, early majority, late majority, and laggards. The findings of this study indicate that regardless of the demographic variables considered, more than 60% of respondents, the majority of librarians surveyed, fall into two contrasting adapter categories: early adopters and early majority. The study suggests that the efficient and effective diffusion of new technologies in library settings may be difficult. Three problematic areas among librarians for the dissemination of innovation were identified: conflicting opinions among multiple opinion leaders, deceleration in the rate of adoption, and improper re-invention. The findings of the study also suggest that “contrary to common beliefs, librarians in academic or special libraries are no more innovative than public or school librarians” (Conclusion, ¶3). Conclusion – The study concludes that librarians’ attitudes are unevenly distributed with most either accepting new innovations or being late adopters. The variables of age, role, tenure, and library type had little impact on the approach of the professional toward innovation. The identification of the three problem areas: opinion leadership, deceleration of adoption, and improper re-invention, represents where more time and effort may need to be spent to make the implementation of new technology a smoother process.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".