Leveraging neural networks to model tourist behaviour for sustainable heritage conservation

Artificial intelligence, as an emerging technological science, provides advanced methods for simulating and extending human intelligence. The technological convergence from various domains has been the focus of researcher community for the last few years. This study applies artificial neural network algorithms to examine sustainability indicators at UNESCO-declared heritage destinations in Pakistan. Data has been collected from 417 national tourists visiting key heritage sites, including the Heritage Remains at Moenjodaro, the Buddhist Remains of Takht-i-Bhai and the neighboring city at Sahr-i-Bahlol, the Fort and Shalamar Gardens in Lahore, the Historical Monuments at Makli (Thatta), Rohtas Fort, and Taxila. However to investigate the influence of sustainability indicators, this study intends to measure the satisfaction level of the national tourists in the targeted six UNESCO-declared heritage destinations in Pakistan. Tourists' satisfaction levels were measured using a five-point Likert scale. Using ANN algorithms the findings reveal that economic, perceptual, ecological, and socio-cultural factors have significant positive relationships with the sustainability of UNESCO heritage destinations in Pakistan. This study insights as the ecological perspective plays the most instrumental role in attaining sustainability in UNESCO-declared destinations in Pakistan. Further the tourists' perceptional perspective shows a positive impact in maintaining sustainability in heritage destinations. Furthermore the influential indicator is the socio-cultural perspective. The least influential factor for heritage destinations' sustainability is the economic perspective. For the policy-makers the data driven analysis provides ground for economic impact modeling, preservation, marketing and policy implementation.