Bibliographic record
Abstract
Mobile technology has been one of the major growth areas in computing over recent years (Urbaczewski, Valacich, & Jessup, 2003). Mobile devices are becoming increasingly diverse and are continuing to shrink in size and weight. Although this increases the portability of such devices, their usability tends to suffer. Fuelled almost entirely by lack of usability, users report high levels of frustration regarding interaction with mobile technologies (Venkatesh, Ramesh, & Massey, 2003). This will only worsen if interaction design for mobile technologies does not continue to receive increasing research attention. For the commercial benefit of mobility and mobile commerce (m-commerce) to be fully realized, users’ interaction experiences with mobile technology cannot be negative. To ensure this, it is imperative that we design the right types of mobile interaction (m-interaction); an important prerequisite for this is ensuring that users’ experience meets both their sensory and functional needs (Venkatesh, Ramesh, & Massey, 2003). Given the resource disparity between mobile and desktop technologies, successful electronic commerce (e-commerce) interface design and evaluation does not necessarily equate to successful m-commerce design and evaluation. It is, therefore, imperative that the specific needs of m-commerce are addressed–both in terms of design and evaluation. This chapter begins by exploring the complexities of designing interaction for mobile technology, highlighting the effect of context on the use of such technology. It then goes on to discuss how interaction design for mobile devices might evolve, introducing alternative interaction modalities that are likely to affect that future evolution. It is impossible, within a single chapter, to consider each and every potential mechanism for interacting with mobile technologies; to provide a forward-looking flavor of what might be possible, this chapter focuses on some more novel methods of interaction and does not, therefore, look at the typical keyboard and visual display-based interaction which, in essence, stem from the desktop interaction design paradigm. Finally, this chapter touches on issues associated with effective evaluation of m-interaction and mobile application designs. By highlighting some of the issues and possibilities for novel m-interaction design and evaluation, we hope that future designers will be encouraged to “think out of the box” in terms of their designs and evaluation strategies.
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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 | 0.082 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".