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Record W4255450891 · doi:10.18034/ajtp.v5i1.428

Customers’ Expectation towards Online Marketing: A Study on Some Economic Zone in Bangladesh

2018· article· en· W4255450891 on OpenAlexaff
Md. Shariful Alam Khandakar, Md. Rakib Uddin Bhuiyan, Humaira Siddika

Bibliographic record

VenueAmerican Journal of Trade and Policy · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsLa Cité Collégiale
Fundersnot available
KeywordsMarketingBusinessDigital marketingOrder (exchange)Marketing effectivenessReturn on marketing investmentMarketing researchMarketing strategyMarketing managementOnline advertisingCustomer satisfactionQuantitative marketing researchThe InternetComputer science

Abstract

fetched live from OpenAlex

This paper attempts to analysis the customers’ expectation towards online marketing in Bangladesh. First, it evaluates the impact of independent variables (factors of online marketing) on dependent variable (customers’ satisfaction). In order to do so a survey was conducted through questionnaire and data collected from 200 respondents’. Samples (Individuals and corporate firms) were selected from three economic zones and multiple regression analysis was conducted for the study. It also evaluates customers’ expectation from online marketers. Moreover the study highlights the online marketing strategies. Next, the study focuses on the benefits from online marketing. It also identifies the problems and challenges to develop online marketing strategies. Online marketing opens huge opportunities for marketer and customer where the ratio of benefit is higher than the ratio of costs. Finally, the study provides specific ways to solve the problems emphasizing on online marketing principles and to avail the opportunity to cope with the today’s competitive market. The strategies require continuous improvement and it is expected to be more dynamic in future.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.068
GPT teacher head0.402
Teacher spread0.334 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2018
Admission routes1
Has abstractyes

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