A Social Networking Strategy for Improving Knowledge Management and Communication in the Travel Industry

In today’s technology-driven marketplace, the adoption and management of corporate and customer-facing Social Networking Sites (SNs) is often viewed as a key success factor for Travel Industry (TI) organisations. Knowledge management and the sharing of expertise and experiences through communication between internal and external stakeholders via social networks is an activity which TI organisations are aiming to exploit in order to improve the open sharing, retrieval, organisation and leveraging of knowledge. Through a study of currently-available literature relating to social networking adoption within the TI and a case study analysis of corporate social networking practices at three multi-national TI organisations (British Airways, Thomas Cook and Marriott Hotels), it may be observed that correlations exist between the development of social networking and the processes TI organisations now use to manage knowledge. We explore how these companies are currently utilizing SNs to improve knowledge management practices inside and outside of their organisational boundaries. From our analysis, lessons may emerge as to how TI companies are gaining competitive advantage through the use of social networking; a proposed strategy is identified to determine how TI organisations may make best use of social networks.

New Developments in Tourism and Hotel Demand Modeling and Forecasting

Abstract Purpose The purpose of the study is to review recent studies published from 2007-2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field. Design/Methodology/approach Articles on tourism and hotel demand modeling and forecasting published in both science citation index (SCI) and social science citation index (SSCI) journals were identified and analyzed. Findings This review found that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area. Research limitations/implications The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implications This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/value The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.