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Exploring Role of Organizational Culture and Leadership in Customer Relationship Management in Banks

2022· article· en· W4312919483 on OpenAlexaff
P. Fata Zainabu, Claire Miranda Joanna

Bibliographic record

VenueSplint International Journal Of Professionals · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsOrganizational cultureBusinessCustomer relationship managementKnowledge managementManagementPublic relationsMarketingPolitical scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

This study explored the relationship between organizational culture, leadership, and implementation of CRM implementation in international banks operating in Tanzania, namely Barclays bank, Standard Chartered, Stanbic and Citibank. CRM is the backbone of an organisation, as it focuses on customer retention through understanding customers’ needs and by maximizing the value customers perceive to be benefiting from their product/service provider. A qualitative approach was adopted for conducting this exploratory study for which the data was collected, using online surveys. This study identified weaknesses such as in the type of leadership style, lack of top management commitment and support, coordination and corroboration as factors affecting the implementation of CRM at Barclays bank, Standard Chartered, Stanbic and Citibank banks. The organizations need to implement a suitable leadership style that is engaging and committed with top management involvement and coordination as for better CRM implementation. Furthermore, the study identified major mismatch of culture affects implementation of CRM. Based on the four case studies, this study illuminated the need for organizations to promote cooperative organizational culture. Promoting a culture of corroboration and engagement organizations can avoid conflicts in implementing a good CRM system.

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 score0.627
Threshold uncertainty score0.675

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.145
GPT teacher head0.287
Teacher spread0.142 · 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
Published2022
Admission routes1
Has abstractyes

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