Managing Chaos in Nonlinear Economic Systems: Globalization and Destination Tourism

Introduction

Without question, tourist destinations face the full potency of globalization. The industry is claimed to be the world’s largest and fastest growing: it has expanded rapidly for half a century, and is on track to double its share of world spending by 2020 (UNTWO, 2006). Increasingly the industry is spearheaded by international chains offering multiple brands in destinations worldwide. Destination tourism typically involves tourists travelling from many parts of the world to favored rather small locations, selected for their climate, heritage, or similar desired characteristics Although a fickle and volatile industry, tourism is a relatively profitable and thus attractive to international business and investors (Cornelissen, 2005; Strauss, 2007). From a business management perspective tourism provides some useful insights. Not only is the industry highly competitive and innovative across the private sector, but so many places, even nations, today look to tourism as the main hope for economic salvation. Thus, hotel, travel, retail, city, public-private partnerships, and tourism authorities, at all geographic scales cooperate in determining a destination’s tourism policy (Knowles, et al, 2001; Wanhill, 2005).

The growth of visitors and tourism-related activities at any given destination typically follows a roughly S-shaped Tourist Area Life Cycle (TALC). This path was originally described by Butler and Brougham (1972) and Butler (1980). The TALC describes the stages of a destination’s progression from exploration, involvement, development, consolidation, stagnation, to rejuvenation or decline. This S-shaped life cycle is closely related to the product life cycle proposed by Vernon (1966). A variety of institutional, technological and other explanations have been attached to the model.¹ Over the years there has been considerable discussion of the significance of the life cycle and of the variability in the growth trajectories of different destinations.

A typical trajectory exhibits fluctuations in key variables, such as the annual number of visitors, superimposed on one or more S-shaped curves.² In some cases the reasons for these variations are fairly easy to discern, in others more complicated to the point that several authors have called for a “complexity” theory of tourism (Faulkner and Russell, 1997; McKercher, 1999; Baggio, 2008). This raises the question of what type of complexity or chaos theory might be relevant: whether tourism is merely very complicated with many entities and feedbacks subject to a plethora of concatenated fluctuations and shocks, or unstable, even chaotic, due to the embodied structures and behaviors within the tourism system? The answer appears to be that tourism is all of this and possibly more.

The model presented in this chapter makes several predictions about tourism “chaos.” These predictions have been explored using available time-series and comparative evidence from island destinations worldwide (Cole, 2007, 2009a, 2012). From this, it appears that, in terms of their logistic growth potential, some tourist destinations are well above the regime of smooth growth and even above levels required for chaos. Whether the fluctuations definitively equate to “chaos” is a moot point since their disruptive impact on an economy is the same and empirically it is difficult to distinguish true chaos from randomness and other extreme disruptions. Based on the formal definition of a destination’s trade-offs for risk, growth, and sustainability, this chapter offers a rule for moderating fluctuations. Although tourism may be exceptional in some respects, other rapidly growing industries and places, share the many of its principal characteristics. Thus, our model may have wider applicability. International destination tourism therefore provides an exemplar of how chaos can arise and managed at the local level in a globalized world economy