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Analytical Impact of Digital Marketing on Smart Wearables in India

2022· book-chapter· en· W4281742933 on OpenAlex
Devesh Bathla, Raina Ahuja, Shraddha Awasthi, Amrith Santhosh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in finance, accounting, and economics book series · 2022
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsWearable computerWearable technologyKey (lock)SmartwatchPerceptionInternet of ThingsComputer scienceEngineeringInternet privacyPsychologyComputer securityEmbedded system

Abstract

fetched live from OpenAlex

Advancement in innovation and technology has transformed the lifestyle of individuals. Wearable innovation is an old-style case of such insight. Despite the fact that this innovation has been common for quite a while, the furor of wearable development began when the model of Google Glass was concocted. It helped clients to start thinking past this present reality. Preceding the prototype, customers were uninformed about wearable development. In the 21st century, wearable innovation has purchased new advancements which have helped wearables to take off in the mechanical market. While it is intended to study the awareness of smart wearables, it is also synthesized to identify the key perceptions about smart wearables in the study. It is further being analysed to check the influence of digital marketing in purchase decision for smart wearables with specific focus on all digital platforms.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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