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Record W4206476323 · doi:10.21272/mmi.2021.4-14

Analysis of territories marketing activities among small and medium business: a bibliometric analysis

2021· article· en· W4206476323 on OpenAlexaboutno aff
Лілія Хоменко, Анна Росохата, Adam Jasnikowski

Bibliographic record

VenueMarketing and Management of Innovations · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsTourismMarketingMarketing researchPoliticsPlace brandingMarketing scienceMarketing managementSociologyPublic relationsPolitical scienceBusinessRelationship marketing

Abstract

fetched live from OpenAlex

The article summarizes the arguments and counterarguments in the scientific discussion on place marketing. The study's main purpose is to understand the nature and features of existing research on marketing areas to determine the direction of future research for both scientists and practitioners. Systematization of literature sources and approaches to solving the problem of marketing areas suggests that many studies in this area require a synthesis of existing research. The urgency of solving this scientific problem is that although the use of marketing in public administration has intensified in recent years, there are many problems in this area. The research of place marketing was carried out in the following logical sequence: determination of the impact of the authors, journals, and articles on territorial marketing, keyword analysis, identification of marketing clusters, visualization of scientific literature on place marketing. The study covers 1970-2021. The research object is 1611 relevant publications published in various scientific sources. The most influential journals were found to be the Journal of Business Research, Tourism Management and Marketing Theory. The most cited authors were Kavaratzis M. and Warnaby G. Most of the articles have been published by researchers from the USA, England, Italy, Canada, the Netherlands, Germany, Australia, and France. Eight key clusters were identified in the marketing of territories: politics, tourism, model, identity, place branding, residents, framework, city brand. In the last five years, most research has been devoted to placing brands, destination marketing, geographies, politics, culture, place branding, identity, tourism, involvement, governance, impact, smart city, loyalty, community. Areas of future research could include destination brand, technology, regeneration, legitimacy, experiences, word-of-mouth, attitude, reflections, memory, inequity of cities, inclusive place branding, brand equity, place attachment, place identity, and others. It is also recommended to focus on city-twinning, sister city, municipal cooperation. The study results could be helpful for companies involved in developing the brand of territories, local authorities for the development of place marketing, and scientists researching place marketing.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1430.182
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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

Citations7
Published2021
Admission routes1
Has abstractyes

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