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Record W4206934078 · doi:10.5267/j.ijdns.2021.12.010

Mobile-customer relationship management and its effect on post-purchase behavior: The moderating of perceived ease of use and perceived usefulness

2022· article· en· W4206934078 on OpenAlexvenueno aff
Jassim Ahmad Al-Gasawneh, Batool Al Khoja, Marzouq Ayed Al-Qeed, Nawras M. Nusaira, Qais Hammouri, Marhana Mohamed Anuar

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilitySample (material)Technology acceptance modelMarketingCustomer relationship managementPartial least squares regressionBusinessConsumer behaviourPsychologyComputer science

Abstract

fetched live from OpenAlex

Customers' online shopping intentions have not changed in response to technological advancements, making it difficult for businesses and marketers to invent new strategies to maintain long-term relationships with customers and encourage them to repurchase despite unprecedented technological advancements around the world. Following these issues, the current study investigated how M-CRM, Perceived Ease of Use, and Perceived Usefulness influenced Post-Purchase Behavior, as well as how Ease of Use and Perceived Usefulness mediated the relationship between M-CRM and Post-Purchase Behavior. The study introduces the Unified Theory of Acceptance and Use of Technology as a theoretical framework to accomplish this goal. The 239 responses were evaluated using Smart Partial Least Squares after the data was obtained from a random sample of Jordanian consumers. M-CRM, as well as Perceived Ease of Use and Perceived Usefulness, had a beneficial influence on post-purchase behavior, according to the data. Perceived Usefulness and Ease of Use The relationship between M-CRM and Post-Purchase Behavior was impacted by usefulness. Companies might use these facts to develop a marketing strategy for Jordanian customers.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.137
GPT teacher head0.393
Teacher spread0.256 · 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

Citations36
Published2022
Admission routes1
Has abstractyes

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