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Record W2053519627 · doi:10.17722/ijrbt.v3i2.130

Sport Sponsorship as a Tool of Marketing Communication – A Case Study of Two Real Estate Companies

2013· article· en· W2053519627 on OpenAlexvenueno aff
Gurpartap Singh, Ambika Bhatia

Bibliographic record

VenueInternational Journal of Research in Business and Technology · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessReal estateMarketingMarketing communicationFinance

Abstract

fetched live from OpenAlex

Sponsorship, in general, is an important component of promotional mix devised by many companies. Companies are known to use sponsorship as a means to associate with a particular event as part of their marketing efforts. Sponsoring of sporting events has been in use since long in various parts of the world and has also found ready acceptance in India. Many companies, whether their products /services have some association with a sport or not, are using sport sponsorship. The purpose of this paper is, therefore, to make an attempt to better understand the use of sport sponsorship as a tool of marketing communication. To achieve this purpose, a simple instrument was designed with questions related to the sponsorship objectives, process of selecting the event, and evaluation of effectiveness of sport sponsorship. Review of literature was carried out to develop a conceptual framework presenting a foundation for data collection.  Real Estate companies are prominent sponsors of sports in the country. A qualitative, case study methodology has been used, based on documents and interviews of two Real Estate companies. The study shows that sport sponsorship has varied objectives. However, some of the most commonly used objectives of sport sponsorship are the corporate related objectives like corporate image, entertainment for clients, and employee relations. The study further shows that companies select a specific sport sponsorship property on the basis of different criteria such as the interest of the top management and the concerned sport’s potential to convey the marketing message. Further, the results of the study show that companies do not carry out formal evaluation of the effectiveness of sport sponsorship.

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)0.003
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 score0.229
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.385
Teacher spread0.313 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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