Blending foodscapes and urban touristscapes: international tourism and city marketing in Indian cities
Bibliographic record
Abstract
Purpose Gateway cities such as Delhi, Mumbai and Kolkata are central in the tourist experience to India, yet the official government authorities and destination marketing organizations tend to underestimate the potential of these destinations to prospective and returning international tourists. In particular, there is little empirical research on urban tourism, food tourism and city marketing in the aforementioned cities. This paper aims to explore the scope for the promotion of Delhi, Mumbai and Kolkata as food urban destinations. Design/methodology/approach For the purposes of this study, a case study methodology using content analysis was developed to ascertain the nexus between food and tourism in the three observed cities. Materials were gathered for the year 2019, with a focus on brochures, tourist guides, websites and social media accounts for Delhi, Mumbai and Kolkata. A two-coding approach through NVivo was designed to analyse and report the findings. Findings The findings of the study suggest that the cities of Delhi, Mumbai and Kolkata fall short in positioning themselves as food urban destinations. Moreover, the study reports a dissonance between the imagery of Delhi, Mumbai and Kolkata portrayed to international tourists through induced images and the food-related experiences available in the cities. This divide reflects a pattern in destination marketing in India observed in previous research. Research limitations/implications The exploratory nature of this study calls for more research in the trends and future directions of food tourism and urban marketing in Indian cities. Moreover, this study calls for further research on the perceptions of urban food experience in Indian cities among international and domestic tourists. Practical implications A series of practical implications can be drawn. First, urban and national destination marketing organizations need to join efforts in developing urban marketing campaigns that place food as a key element of the urban experience. Second, cities worldwide are rebranding themselves as food destinations and Indian cities should reconsider local and regional culinary traditions as mean to reposition themselves to food travellers’ similar niche segments. Social implications The quest for authenticity is central in the expectations of incoming tourists. Moreover, the richness and variety of local and regional food in the cities analysed in this study can enhance urban visitor experience, with obvious economic and socio-cultural benefits for the local businesses and residents. Originality/value This study is the first of its kind to provide preliminary evidence on the nexus between food and tourism in Indian cities. Building from the literature, it developed a conceptual framework for the analysis of food tourism and urban branding and shed light on a currently overlooked aspect of incoming tourism to India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".