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Record W4386507641 · doi:10.33897/fujbe.v2i2.122

Engaging Customers by Fostering Learning Process and Strategic Flexibility Together in Cellular Sector of Pakistan

2017· article· en· W4386507641 on OpenAlexaff
Maryam Zeb, A. Sher, Muhammad Awais, Hussaun A. Syed

Bibliographic record

VenueFoundation University Journal of Business & Economics · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsFlexibility (engineering)BusinessProcess (computing)Order (exchange)Competitive advantageMarketingGlobalizationKnowledge managementBusiness environmentProcess managementIndustrial organizationComputer scienceManagementEconomicsBusiness administration

Abstract

fetched live from OpenAlex

Globalization has caused immense changes in the business environment and has made difficult for the organizations to respond quickly and effectively to the changing customer preferences. Therefore, organizations that are flexible and are involved in the learning process are able to compete in the modern markets. This research study seeks to achieve the understanding of the contribution of organizational learning along with strategic flexibility to enhance the customer performance in sustaining competitive advantage in a rapidly changing business environment. This allows the organizations to react quickly towards the changing market requirement. Questionnaire was used to collect data. Findings suggested that both the constructs of organizational learning and strategic flexibility help firms to adapt to the changing business conditions in order to satisfy the needs of their customers. The cellular companies in Pakistan call for new approaches to engage customers therefore, managers are required to focus on learning process along with strategic flexibility to respond to the changing market conditions in a timely manner.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.034
GPT teacher head0.242
Teacher spread0.208 · 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
Published2017
Admission routes1
Has abstractyes

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