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Mobile Portals as Innovations

2009· book-chapter· en· W4232505328 on OpenAlexaff
Alexander Serenko, Ofir Turel

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsLakehead University
Fundersnot available
KeywordsTypologyWorld Wide WebMobile business developmentKnowledge managementMobile deviceComputer scienceMobile technologyMobile Web

Abstract

fetched live from OpenAlex

The purpose of this chapter is to analyze mobile portals (m-portals) as an innovation. M-portals are wireless Web pages that help portable device users interact with mobile content and services (based on the definition by Clarke & Flaherty, 2003). Previous works in the area of mobile portals mostly concentrated on their technical aspects, implementation issues, classifications, and user acceptance (e.g., Gohring, 1999; GSA, 2002; Koivumäki, 2002). At the same time, these studies did not view mobile portals as innovations themselves, nor discussed the innovative potential of this novel technology. Analyzing technological artifacts as innovations is important for two reasons. First, such analysis can help m-portal developers and providers pinpoint the salient mportal characteristics that drive service diffusion. Second, it can assist potential m-portal developers and providers understand the risks associated with entering this segment of wireless services. This study attempts to contribute to the knowledge base by discussing various dimensions of the innovativeness of mobile portals and predicting the commercial success as well as potential risks of designing m-portals. Specifically, this investigation utilizes two innovation-based models as a lens of analysis. The first is the Moore and Benbasat’s (1991) list of perceived characteristics of innovating (PCI), which is adapted to assess the innovation features of mobile portals. The second is the Kleinschmidt and Cooper’s (1991) market and technological newness map. By applying these frameworks, the study attempts to develop a better understanding of individual innovation characteristics and the innovation typology of mobile portals that is important for both theory and practice. Mobile portals are a fruitful area of growth and interest. Even though the technology has been in use for only several years, both researchers and practitioners have devoted substantial efforts to design m-portals that would meet enduser requirements. To ensure the success of this technology, it is important to further understand its innovative potential. However, little work has been done in this area. A discussion grounded on the existing innovation schools of thought would help to bridge that gap.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0020.011
Scholarly communication0.0120.016
Open science0.0010.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0170.002

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.

Opus teacher head0.076
GPT teacher head0.370
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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