Destination Marketing: Optimizing Resource Allocation Using Modern Portfolio Theory

Abstract

Despite the significant contribution that tourism generates for national economic health, the tourism industry is challenged by seasonal and periodic volatility in arrivals. This volatility causes inefficiencies in the allocation of a destination’s resources. Policy makers and operators prefer a steady and constant inflow of tourists. Though prior studies have applied a portfolio optimization approach to inbound tourism flows, this study is the first to consider the destination’s marketing budget and each tourism markets’ heterogeneous marketing expenditure–demand elasticity. Our model provides destination marketing organizations with direct guidance as to how to allocate their marketing budget to facilitate portfolio profiles that are efficient from a risk-reward perspective, and that are attainable given budget constraints and known marketing expenditure–demand elasticity patterns. Using Tourism Research Australia International Visitor Survey data, we find that variability in tourism fluctuations can be reduced and inbound tourism numbers/spending significantly increased by implementing smarter (data-driven) marketing budget allocations.

Identifying a destination’s optimal tourist market mix: Does a superior portfolio model exist?

“…to rebalance the tourist nationality mix to the desired proportions. If not, an additional financial resource constraint can be imposed on the portfolio model (see Board & Sutcliffe, 1991;Johar et al, 2021). We also assume, consistent with the previous tourism portfolio literature, that policy makers cannot, or choose not to, engage in initiatives to alter the input parameters of the portfolio model, e.g., reduce the variability of tourist bed-nights or increase expenditure per bed night for various nationalities.…”

“…The Levels Model 2 minimises the risk of total tourist arrivals, bed-nights or expenditure, with no limit on the total level of tourist arrivals or bed-nights. This model has been applied to Croatia by Ivanovic et al (2018), Canada by Jang (2004), France by Botti et al (2012), Morocco by Ratsimbanierana et al (2013), Taiwan by Jang and Chen (2008), Canada and the US by Jang et al (2004), Australia by Johar et al (2021), and without an empirical application by Arbel and Bargur (1980).…”

“…Jang et al (2004) dropped the upper and lower constraints on the policy variables in their version of Levels Model 2, while Jang and Chen (2008) minimised arrivals risk with constraints on the policy variables, and Jang (2004) minimised bed-nights risk with no constraints. Johar et al (2021) introduced a budget constraint, rather than a capacity constraint, but did not include restrictions (upper and lower constraints) on the size of changes from the current situation. Arbel & Bargur (1989) mention resource constraints, but did not include them, or upper and lower constraints on the policy variables.…”

Evolving Landscape of Partnerships of U.S. Destination Marketing Websites: External Hyperlinks From 1999 to 2018

“…Wang, 2008). These partnerships evolve over time whereby continuity is likely determined by both internal and external factors such as budget (Johar, Tan, Maung, & Douglas, 2022), political pressures (Ooi, 2004) and technological change (Buhalis, 2000), to name just a few. In the last two decades or so, such environmental changes included the 2008 financial crisis, the adoption of Internet technology Zach, F. J., .…”