Business Travel Risk Management: Destination, Origin and Places In-Between
Ridley Tony
Experienced Leader in Risk, Security, Resilience, Safety, and Management Sciences | PhD Candidate, Researcher and Scholar
The following article follows on from Business Travel Risk Management: Hierarchy and Regimes
Chapter 3 - Fixity (Destination, Origin & Places In-Between)
Introduction
This chapter progresses on from mobility issues to that of static considerations such as place, cities, connectedness and crime. Due to the highly visual nature of some of the elements, images, and graphics feature prominently to convey context and compare complex spatial topics.
Researchers note that stereotypical risk, action and adventure-seeking males first dominated business travel studies, whereas women tend to represent tourism industry studies (Collins and Tisdell, 2002). Gendered stereotypes and influences continue to feature in transnational mobility literature on security, safety and perception of place ranging from generic former government intelligence agent commentary to quasi-official standards (British Standards Institute, 2016; Goslin, 2017).
Both examples typify the lack of empirical evidence, research or data to support universal assertions.
As with chapter two, risk cognition and perception, therefore, continue to influence the fixity literature.
Destination or visiting location entails choice and trade-offs. As a result, risk homeostasis influences decisions, especially when travellers assume a passive role (Wilde, 2014). In other words, travellers may make broad personal risk and benefit calculations about place pre-travel but accept little control once a journey has commenced due to their subordinate role or influence. Participation is of particular relevance within the context of transnational travel where individuals may surrender to local contexts or remain segmented from local communities (Adams and Van de Vijver, 2015), which becomes a matter of personal choice, competence and preference.
Risk-benefit correlation is considered stable across varied cohorts, including business and hedonistic leisure travellers (Skagerlund et al., 2020). Figure 34 summarises critical factors in determining risk for benefit, despite the absence of this foundational model in tourism literature.
This chapter explores static locations, connectedness and elements of crime and harm concentration.
Figure 34-1 The Affect Heuristic and Risk Perception, adapted from Skagerlund et al., 2020
Place
Early sociology academics posit that ‘space’ contains a certain social logic related to local politics and ideology (Hillier and Hanson, 1989).
However, visitors such as travellers and mobile business envoys are likely unaware of highly contextual influences of place, tending to rely on broader, general perceptions of place and risk (Schroeder et al., 2013).
Whereas figure 34 highlights affect heuristics influencing risk perception, figure 35 confounds perception further by showing local and global variants relative to a local place. It is this perception of place, primarily destination, that is crucial to transnational mobility as it may be more interpretive, transient and conceptual for individuals than empirical.
Figure 35-1. The social potential of space. Adapted from (Hillier and Hanson, 1989)
Destination safety reputation is cited as a crucial consideration for tourists (Mawby, 2000). However, tourism marketers and many academics seek to manipulate and project positive destination perceptions in order to increase visitation, sales (Wang et al., 2019). Conversely, negative destination perceptions are typically associated with ‘security’ issues which attract fewer travellers (Seabra et al., 2013). Travel agents, seen as the last link in tourism production chains, also influence the perception of place for economic gain (Romero and Tajeda, 2011; Chung et al., 2020).
It is therefore perhaps not surprising that destination or place is subject to bias and incomplete or perishable risk classifiers such as threat, health, crime and natural disasters (Debrudder et al., 2011; Baker, 2014; UNWTO, 2020).
As a result, a place becomes political, contextual with micro-places compete for visitors.
Micro-place consideration is relatively new in criminological studies (Weisburd et al., 2009). Local network and street-level crime analysis, visitor or resident crime and place attachment are even more recent considerations (Davis and Johnson, 2015; De Dominicis et al.; 2015 Bolvin and Felson, 2018). Overall, researchers conclude that local security and crime issues tend to be highly concentrated (Newman, 2000; Lautieri et al., 2005 Fialkowski and Bitner, 2008). The criminality of place and concentration explore this concept in the following sections. Unlike persistent findings in chapter two on mobility, these findings rank highly according to figures five and eight for knowledge and empirical evidence. The concentration of crime and security occurrence is consistent with power distribution curves, also known as Zipf’s law, which is explored and visually explained in more detail the following subsections.
Cities
In practical terms, cities typically represent a place for travellers.
The traveller’s perception and knowledge of a city are highly subjective, filtered by travel actors and differs from that of experts.
The previous two sections summarise the influences. This section considers cities in more detail.
Tourism literature tends to exclude culturally varied behaviour and summarises select city safety in broad economic or daily commute terms (Weisburd et al., 2009; Kaminski, 2013; McKinsey & Co, 2015; Fortunati and Taipale, 2017; Economist, 2019). In addition to ignoring local street networks influencing factors such as crime (Johnson, 2010).
In other words, cities tend to be expressed as broad summations, not cellular and connected entities with an emphasis on central place theory further ignoring temporal and spatial fluctuations (Barthelemy, 2016).
Cities are therefore not static, constant and unchanging environments. In contrast, urban mobility is dynamic and continuously evolving, which is mostly absent from destination tourism literature.
Opportunity and benefit-seeking behaviour may further influence urban movement. For example, 35 million urban mobility data points from 34 cities demonstrate Zipf’s power law of concentration with commuters typically limiting movement to local opportunity environments (Noulas et al., 2012). Figure 36 shows consistent movement concentration within local areas across various international cities with each distribution curve consistent with a power concentration curve. In other words, local community mobility is clustered within proximity of local opportunities. Business travellers are likely exceptions to the rule due to the pursuit of specific opportunities inconsistent with daily local life.
Figure 36-1. Global Urban Movements Across 34 Cities (Foursquare) Source (Noulas et al., 2012)
Opportunity pursuit can be further nuanced by the interaction between social groups, according to income (Bavaud and Mager, 2009). Figure 37 suggests that social class interaction only occurs in minimal spatial environments with the remaining, taking place within relatively comparable income groups (situation B and C). Figure 38, on the other hand, looks down on the spatial construct suggesting social groups cluster together according to economic class. Business mobility may again be the exception to the rule transitioning all three theories exposing individuals to varying levels of crime, safety or risk.
Figure 37-1. Distribution of Social Levels – Three Theories. Adapted from (Bavaud and Mager, 2009)
Figure 38-1. Spatial Location of Individuals – Three Theories. Source (Bavaud and Mager, 2009)
Previous examples imply central tendency and exclusive power concentration. Such mathematical models may be overly rigid of individual choice and variance of local and visitor mobility (Courtat et al., 2011; Barthelemy, 2013). For example, figure 39 shows a simple mathematical view of three streets on the left and the real-world navigation choices, leading to several variations on the right.
In other words, personal choice and local variance may lead travellers to new or irrational routes exposing them to varying safety and security concerns.
Outcomes are, therefore, not axiomatic or static. Figure 40 shows a further practical network choice. In short, local structure influences behaviour and routes, including business districts (Derudder et al., 2003; Courtat et al., 2011).
Figure 39-1. Street Maps - Mathematical and Realist Conversions. Source (Courtat et al., 2011)
Figure 40-1.City Street Networks with Varied Density. Source (Courtat et al., 2011)
Research into 175 professional firms offering 91,875 service values expands this premise to 525 cities to form a world city network of business activity (Beaverstock et al., 2002; Taylor et al., 2009).
In short, global and local business travel commutes exhibit vastly different structures and routes that change with choice and transport mobilities (Louf and Barthelemy, 2014).
Figure 41 displays a typical street layout across 131 global cities, with figure 42 displaying the typical street structure at the ground level.
In summary, business travellers cluster in distinct locations and move globally and through cities seeking unique opportunities (Beaverstock, 2002) in ways as yet inadequately studied in the existing literature.
The next sections explore how visitors may connect or move to and from places, along with exposure to crime.
Figure 41-1. High Structure and Order City Street Networks - Global Map of 131 Cities. Source (Louf and Barthelemy, 2014)
Figure 42-1. Representative Street Layout Density - Global Cities. Source (Louf and Barthelemy, 2014)
Transit and In-Betweenness
Business travel has been described as a “proxy for intensities of flow and connectedness between places” (Falcounbridge and Beaverstock, 2008), including intermediate workplaces such as hotels and airports (Jones, 2010).
However, minimal mention or analysis of ‘betweenness’ is present in tourism or mobility literature.
Quantitative criminology has only recently started to explore this risk network facet, too (Davies and Johnson, 2015). Figure 43 displays the concept of nodes (red) and links (green) with the proposition that nodes (e2) connect in this example to 49 path combinations whereas a link is limited to seven paths (e2). This first and second-order transition path is consistent with figure 10 displaying business travel phase transitions in chapter one (Vinkovic and Kirman, 2006; Gauvin et al., 2009). It is also consistent with the world city network in chapter 2. Figure 44 displays this first-order heuristic within urban environments. Darker red indicates high betweenness network value with decreasing colour, showing a decline in value to simplified link status.
Put another way, paths and connections influence risk and exposure to crime inconsistently.
Figure 43-1. Betweenness - Nodes and Links. Source (Davies and Johnson, 2015).
Figure 45-1. Betweenness - Operationalised Street Networks. Source (Davies and Johnson, 2015).
Modern technology and large data sets have repeatedly validated the theory of urban mobility and in-betweenness using location sharing services of millions of data points across hundreds of thousands of local journeys (Cheng et al., 2011; Wang et al., 2015). Again, social status and opportunity pursuit influence choice and paths supporting observations that journeys are concentrated in time and space. This concept holds across other global transport networks (Roth et al., 2012) with figure 44 displays the subway networks of large international cities, whereas figure 45 displaying a schematic representation of typical structures. Scholars, therefore, assert that “it is crucial to consider the full, multimodal, multilayer network aspects of transportation systems in order to understand the behaviour of cities” (Strano et al., 2015).
In short, business mobility should be considered in conjunction with local network structures, features of micro places and the means and variations upon which connections and commuting are made in the course of a journey.
Existing tourism and criminology literature is conspicuously devoid of these inclusions or considerations. The next section explores the criminality of place and concentration in more detail.
Figure 44-1. Large Subway Networks in Large Urban Areas. Source (Roth et al., 2012)
Figure 45-1. Schematic of Subway Networks. Source (Roth et al., 2012)
Next in the series:
Criminality of place: Crime concentration and the impact on tourists, travellers and visitors
Tony Ridley, MSc
Security, Risk & Management Sciences
Reference:
Ridley, T. (2020) What are the main private security risk management factors in transnational mobility? Master of Science, Dissertation. Security & Risk Management. University of Leicester