Younger Adults Derive Pleasure and Utilitarian Benefits from Browsing for Music Information Seeking in Physical and Digital Spaces. A Review of: Laplante, A., & Downie, J. S. (2011). The utilitarian and hedonic outcomes of music information-seeking in everyday life. Library & Information Science Research, 33, 202-210. doi:10.1016/j.lisr.2010.11.002
Bibliographic record
Abstract
Objective – This study’s objective was to identify the utilitarian and hedonic features of satisfying music information seeking experiences from the perspective of younger adults when using physical and digital music information retrieval (MIR) systems in their daily lives. Design – In-depth, semi-structured interviews. Setting – Large public library in Montreal, Canada. Subjects – 15 French-speaking younger adults,10 males and 5 females (aged 18 to 29 years, mean age of 24 years). Methods – A pre-test was completed to test the interview guide. The guide was dividedinto five sections asking the participants questions about their music tastes, how music fit into their daily lives, how they discovered music, what music information sources were used and how they were used, what made their experiences satisfying, and theirbiographical information. Participants were recruited between April 1, 2006 and August 8,2007 following maximum variation samplingfor the main study. Recruitment stopped whendata saturation was reached and no new themes arose during analysis. Interviews were recorded and the transcripts were analyzed via constant comparative method (CCM) to determine themes and patterns. Main Results – The researchers found that both utilitarian and hedonic factors contributed to satisfaction with music information seeking experiences for the young adults. Utilitarian factors were divided between two main categories: finding music and finding information about music. Finding information about music could be further divided into three sub-categories: increasing cultural knowledge and social acceptance through increased knowledge about music, enriching the listening experience by finding information about the artist and the music, and gathering information to help with future music purchases including information that would help the participants recommend music to others. Hedonic outcomes that contributed to satisfying information seeking experiences included deriving pleasure and feeling engaged while searching or browsing for music. Especially satisfying experiences were those where the participants felt highly engaged in the process and found new, independent, non-mainstream music. Not finding new music did not automatically lead to an unsatisfying experience for the participants; however, technology malfunctions in digital MIR systems and unpleasant environments such as those with unfriendly staff in physical music spaces (libraries and stores), led to unsatisfying experiences for the participants. Conclusions – As the results show that the hedonic aspects of music information seeking are very important, designers of MIR systems must take into account the hedonic as well as utilitarian outcomes when creating user interfaces. MIR systems should be designed with browsing as well as searching capabilities so searchers can make serendipitous discoveries of new music and information about music. In other words, MIR systems need to be engaging to ensure satisfying interactions for searchers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".