Research on Assessing Comprehensive Competitiveness of Tourist Destinations Within Cities, Based on Field Theory and Competitiveness Theory

The question of how to assess the comprehensive competitiveness of tourist destinations within cities is an important aspect for determining the potential of a city’s tourism development and its ranking among peers in the field. There are four main parts to the content of this article, which consist of the analysis of competition formation motives based on “Field Theory”, the selection of influencing factors by drawing on Porter’s theory of competitiveness, the construction of an assessment model based on the multi-factors weighted comprehensive evaluation method, and an empirical analysis using Nanjing as the research area. The conclusions are as follows: Firstly, the tourist destination field within a city is composed of three interrelated elements, which are actors, rules, and competition. Under the influence of mainstream social and cultural trends, each tourist destination occupies a certain “position” by relying on the attractiveness formed by various types of capital, and then participates in peer competition within the field. Secondly, the three major influencing aspects of the competitiveness of tourist destinations are element conditions, demand characteristics, and supporting conditions. The key points involved in the three aspects can be summarized into four categories of factors, namely, quality evaluation, popularity level, spatial attractiveness, and emotional cognition, which together constitute the indicator system. Thirdly, there are thirteen tourist destinations in Nanjing that are rated above the average, accounting for about 43% of all the popular destinations. The variation coefficient of competitiveness results is about 35%, indicating a moderate to relatively weak degree of dispersion. Finally, the competitiveness of the thirty hot tourist destinations generally presents a spatial order that gradually weakens in an outward direction from the center zone of the city, forming an overall pattern of cluster groups of well-known tourist destinations in the core of the city, relatively random small clusters in the new main city area, and scattered point distribution in the suburbs.