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Record W2121770068 · doi:10.5539/ass.v11n26p196

Smartphone Dependency and Its Impact on Purchase Behavior

2015· article· en· W2121770068 on OpenAlexvenueno aff
Amran Harun, Toh Soon Liew, Abdul Wahid Mohd Kassim, Rini Suryati Sulong

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsDependency (UML)GratificationSmartphone addictionOutcome (game theory)Confirmatory factor analysisPsychologyOrder (exchange)AdvertisingMarketingBusinessComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

People nowadays seem to become dependent towards smartphone due to its convenience, great camera features, easy applications’ installations, and more importantly, it can do most of the computer functions on the go. Significantly, smartphone usage in Malaysia is growing enormously and has become a significant and lucrative industry. This study aims to understand the antecedents and the outcome of the smartphone dependency among smartphone consumers. Two theories - the theory of uses and gratification and the media dependency theory- were used as the theoretical basis for this study, in order to determine the motivations to use a smartphone and to define dependency and its outcome. The antecedent variables were convenience, social need and social influence; and all of these dimensions are conceptualized as one-dimensional. The outcome of dependency on smartphones was expected to be the purchase behavior. Data analyses were based on 226 valid questionnaires that were collected among smartphone users. The result from the Confirmatory Factor Analysis of Partial Least Square (PLS) shows that only social needs and social influence significantly influenced the smartphone dependency among consumers, therefore indicating that these two factors are important to influence dependency on the smartphone. In addition, the analysis also verifies that the purchase behavior is the outcome of the dependency on the smartphone. Based on these results, marketers could focus on creating dependency among consumers on smartphone usage based on the consumers’ social need, which eventually will promote future purchase behavior in the long run. More importantly, understanding social influences as antecedents.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.035
GPT teacher head0.368
Teacher spread0.333 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2015
Admission routes1
Has abstractyes

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