Bibliographic record
Abstract
一、英文文獻: 1. Aaker, D.A. (1991) Managing Brand Equity. New York: Free Press. 2. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Springer series in social psychology pp.11-39. Berlin: Springer. 3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, pp. 179-211. 4. Christodoulides, G. & de Chernatony, L. (2010). Consumer-based brand equity conceptualisation and measurement. International Journal of Market Research, vol.52 pp. 43-66. 5. Davis(1989),User acceptance of computer technology：a comparison of two theoretical model, Management Science, Vol. 35, No 8 , August 1989 ,pp. 982-1002 6. de Chernatony, L., Harris, F.J. & Christodoulides, G. (2004) Developing a brand performance measure for financial services brands. Services Industries Journal, 24, 2, pp. 15–33. 7. Erdem, T. & Swait, J. (1998) Brand equity as a signaling phenomenon. Journal of Consumer Psychology, 7, 2, pp. 131–157. 8. Fishbein, M. (1967). Attitude and the prediction of behavior. In M. Fishbein (Ed.), Readings in attitude theory and measurement (pp. 477-492). New York: Wiley. 9. Lassar, W., Mittal, B. & Sharma, A. (1995) Measuring customer-based brand equity. Journal of Consumer Marketing, 12, 4, pp. 11–19. 10. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co. 11. Jourdan, P. (2002) ,Measuring Brand Equity: Proposal For Conceptual and Methodological Improvements, in NA - Advances in Consumer Research Volume 29, eds. Susan M. Broniarczyk and Kent Nakamoto, Valdosta, GA : Association for Consumer Research, pp. 290-298. 12. Kesavan, Srinivasan (1979) Homogenization of elliptic eigenvalue problems: Part 1 Applied Mathematics and Optimization, 5 (1). pp. 153-167. ISSN 0095-4616 13. Keller, K.L. (1993) Conceptualizing, measuring and managing customer-based brand equity. Journal of Marketing, 57, 1, pp. 1–22. 14. Kotler, P. (2003),”Marketing Management”, 11th ed, Prentice-Hall Inc. 15. Lassar, W., Mittal, B. & Sharma, A. (1995) Measuring customer-based brand equity. Journal of Consumer Marketing, 12, 4, pp. 11–19. 16. Maslow, A.H. (1970). Motivation and personality. New York: Harper & Row. 17. Mittal, B. (1990) The Relative Roles of Brand Beliefs and Attitude toward the Ad as Mediators of Brand Attitude: A Second Look, Journal of Marketing Research Vol. 27, No. 2, pp. 209-219 18. Nunnally JC (1978). Psychometric Theory, 2nd ed. New York: McGraw-Hill. 19. Park, C. S. & Srinivasan, V. (1994) A survey-based method for measuring and understanding brand equity and its extendibility. Journal of marketing research: JMR, 31, 271 - 288. 20. Vazquez, R., Del Rio, A.B. & Iglesias, V. (2002). Consumer-based Brand Equity:Development and Validation of a Measurement Instrument. Journal of Marketing Management , 18, 1-2, pp. 27–48. 二、網站、電子資料 1. 手機王Apple iPhone問世五週年回顧特集 (手機王) 擷取自2013.6.18 http://goo.gl/YSIhU 2. Apple iPhone 5、iPod Nano 7G、iPod Touch 5G 發表會重點總整理( Engadget中文版) 2013.6.18 http://goo.gl/R6Uyy 3. 美智慧手機滿意度 hTC亞軍(蘋果日報)擷取自2013.6.18 http://goo.gl/vdSuh 4. 2013年BRANDZ全球最具價值品牌百強排行榜(WPP)擷取自2013.6.18 http://goo.gl/FzX2z 5. Top 10 Everything 2012(TIME) 擷取自2013.6.18 http://goo.gl/KTzxE 6. Apple’s iPhone 5 top world smartphone sales for Q4 2012(Strategy Analytics)擷取自2013.6.18 http://goo.gl/WKpxm 三、中文文獻 1. 黃凱麟(2012)。手機消費者之品牌權益認知與多樣化搜尋行為對其再購意願影響之研究。碩士論文，淡江大學。 2. 楊雅婷(2009)，以理性行為理論和科技接受模型來探討消費者對創新科技智慧型手機的購買意願行為之研究。碩士論文，南華大學。 3. 葉紋君(2010)，科技準備度與科技接受模式之整合與延伸-以智慧型手機為例。碩士論文，國立彰化師範大學。 4. 葉俊麟(2011)，以計劃行為理論與科技接受模型來探討智慧型手機的購買決策。碩士論文，國立中央大學。 5. 莊坤穎(2011)，以科技接受模式探討智慧型手機對使用者之行為影響。碩士論文，義守大學。 6. 吳賜聲(2011)，運用科技接受模式探討使用者在行動通訊上網之影響因素-以智慧型手機為例。碩士論文，萬能科技大學。 7. 鍾欣倫(2011)，以科技接受模式探討影響使用行動上網之行為意願。碩士論文，國立東華大學。 8. 莊雅雯(2012)，以TAM模式探討使用者對智慧型手機之購買意願。碩士論文，真理大學。 9. 侯宗佐(2012)，結合科技接受模式與從眾理論探討影響消費者購買智慧型手機之因素。碩士論文，國立高雄第一科技大學。 10. 韓怡萱(2012)，運用科技接受模式探討國小教師智慧型手機之使用行為-以雲林縣為例。碩士論文，國立虎尾科技大學。 11. 蕭文龍(2009)，變量分析最佳入門實用書(第二版)：SPSS+LISREL，碁峰。
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.046 |
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".