دانلود مقاله ISI انگلیسی شماره 1072
عنوان فارسی مقاله

تشخیص سیگنال به عنوان خط اول دفاع در مدیریت بحران گردشگری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
1072 2013 14 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Signal detection as the first line of defence in tourism crisis management
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Tourism Management, Volume 34, February 2013, Pages 158–171

کلمات کلیدی
مدیریت بحران - سیگنال های بحران - تشخیص سیگنال - تکنیک حادثه بحرانی - اسکن محیط زیست -
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پیش نمایش مقاله تشخیص سیگنال به عنوان خط اول دفاع در مدیریت بحران گردشگری

چکیده انگلیسی

The vulnerability of the tourism industry to a range of crises has attracted many scholars to investigate the crisis strategies and practices employed by destinations and tourism organizations mainly with regards to crisis preparedness, containment and damage limitation, crisis recovery and subsequent learning. One over-looked area has been that of crisis signal detection. This paper proposes a three-stage conceptual framework for crisis signal detection consisting of signal scanning, capture and transmission to the crisis response centre. With this framework as a basis, 16 corporate level executives of international tourism organizations were interviewed in order to explore the significance of signal detection in their crisis management practice and the challenges faced in each of these three stages. The findings offer insights into the design of crisis management mechanisms and open areas for further research.

مقدمه انگلیسی

The tourism industry is prone to crises as it is highly fragmented and complex with many interdependencies among its sectors. These interdependencies mean that a crisis in a tourism sector will have repercussions in the others. Henderson (2007, p. 8) argued that a transport accident, a hotel fire or a street riot in which tourists will be caught up will impact tourist arrivals in a destination with impacts on accommodation, attraction and transport providers as well as a number of other tourism stakeholders such as tour operators, travel agents and the destination's tourism authorities. Similarly, events that are not directly connected to tourism may have a huge impact on tourism sectors as witnessed in the 1997 Asian financial crisis (De Sausmarez, 2004), the 2001 World Trade Centre terrorist attack (Ito & Lee, 2005), the SARS epidemic (Pine & McKercher, 2004), the Indian Ocean tsunami (Rittichainuwat, 2006). It is noteworthy, however, that most crises do not occur suddenly. Mitroff (1988, p. 18) observed that “long before its actual occurrence, a crisis sends off a repeated and persistent trail of early warning signals” which could be picked up at a time where there is still opportunity to prevent it from occurring or to take measures that will minimise its impact. These early warning or crisis signals are pieces of information indicating deviation from normalcy (e.g., financial indicators exceeding a threshold, abnormal patterns of social behaviour, etc.) that may escalate and lead to a crisis. For example, a receding ocean following an earthquake felt in the coast may be an indication of an approaching tsunami, an unusually increased number of patients with respiratory problems admitted in a hospital may be an indication of an emerging epidemic and an increased number of clashes between religious sects in a destination may indicate possible political unrest. Early detection of these signals and timely response to them might have saved a good part of the 230,000 lives claimed by the 2004 Indian Ocean tsunami, of the 41% of tourism GDP that Hong Kong has lost due to SARS in 2003 or of the more than $600 million Bahrain has lost due to the cancellation of the Formula 1 Grand Prix in 2011. Several scholars in the field of crisis management (Boin, 2003; Boin & Lagadec, 2000; Takeda & Helms, 2006) have suggested that as crises are dynamic in nature with events morphing at varying rates of acceleration and deceleration. Small changes in the parameters of a crisis may ultimately cause enormous changes in its outcome as minute initial differences are magnified and transformed by the dynamical processes at work (“butterfly effect”, Lorenz, 1993) rendering the crisis extremely sensitive to the initial conditions of its evolution (Paraskevas, 2006). This sensitivity underlines the importance of early interventions in crisis development and, therefore, of processes able to capture the crisis dynamism through the detection, transmission and interpretation of the signals it emanates. With this thinking, Mitroff (1988) proposed a five phase (“five mechanisms” – in his terms) crisis management model: signal detection; preparation/prevention; containment (damage limitation); recovery; and learning. This model pre-supposes a signal detection mechanism for better crisis preparedness and even prevention of a crisis. The subject of tourism crises has attracted the attention of several scholars in the field resulting in a significant body of literature. These studies have contributed a lot in evaluating the impact of crises on tourism (e.g., Blake & Sinclair, 2003; Eugenio-Martin, Sinclair, & Yeoman, 2005; Pizam & Fleischer, 2002), addressing particular aspects of crisis management, mainly destination recovery (e.g., Beirman, 2003; Israeli & Reichel, 2003; Prideaux, 2004) or focussing on lessons learned from crises (e.g., De Sausmarez, 2004; Henderson, 2003a and Henderson, 2003b; Miller & Ritchie, 2003). However none of them has looked at crisis signals and what Mitroff (1988) calls “crisis detection mechanism”. Even the few studies that propose more strategic approaches to tourism crisis/disaster management (Faulkner, 2001; Ritchie, 2004) just touch upon crisis signal detection. Key research questions such as how should a signal detection mechanism be designed, what types of detectors it should use and for what signals it should look and where, largely remain with no answer. This paper aims to narrow this research gap, by exploring the ‘mechanism’ of crisis signal detection in the context of the tourism organizations. We first look at the crisis literature within tourism and we develop a conceptual framework for the detection process of crisis signals based on a number of theories including the information communication theory and the signal detection theory. We then conduct a fieldwork with 16 corporate level executives of international tourism organizations in order to explore the significance of signal detection in their crisis management practice, the way it is designed and the challenges they are facing. The paper concludes with suggestions for further research on the topic.

نتیجه گیری انگلیسی

The aim of this paper was to explore the concept of crisis signal detection in the context of the tourism industry. We first looked at the crisis literature within tourism which showed that tourism organizations have made significant progress towards addressing issues associated with crisis preparedness, containment and damage limitation, crisis recovery and learning from crisis. However, the extant research has not yet explored in depth the significant component of crisis management called crisis signal detection. Using basic concepts and ideas from Signal Detection Theory and the generic crisis management literature, we developed a conceptual framework for a three-stage process of crisis signals detection consisting of signal scanning, signal capture and signal transmission to the crisis response centre. With this framework as a basis, we then conducted a study on 16 corporate level executives of international tourism organizations in order to explore the significance of signal detection in their crisis management practice and the challenges they are facing. The study showed that there is a wide consensus that many crises emit warning signals before they manifest themselves and that although this is not a universal rule, signal detection can become (in the words of a participant) an organization's “first line of defence”. This line of defence would help in reducing the organization's exposure to the adverse effects of a crisis and, perhaps in certain cases, prevent the crisis itself. Of course, the sophistication of this defence will vary depending on the organization's crisis culture, size and financial capacity but most importantly on the ability of the detectors and the response decision makers to make sense of these signals. There are many challenges in doing so due to a number of causes which range from what is called ‘bounded awareness’ to internal politics and hidden agendas. The executives in these organizations underscore the importance of an organization-wide crisis culture where everybody is responsible for identifying, capturing and reporting any signals that may indicate an emerging crisis. The effectiveness of crisis signal detection depends primarily on the organization's ability to scan its environments and identify the signals that are relevant to it. In designing a crisis signal detection mechanism organizations should purposefully use a combination of core, ad hoc and expert detector networks as presented in Table 2 which enable the scanning for crisis signals of not only ‘what is out there’ (organization's physical and informational domains) but also of ‘what will possibly be’ (organization's cognitive domain). The study showed that the latter is not being actively pursued indicating that many opportunities of proactive action are missed. The importance of learning from crises and managing the knowledge acquired from the response to them was highlighted by many participants as a pre-condition for successful signal detection and capture through case definition and pattern recognition. However, the idea of centralisation and exploitation of this knowledge within an organisation appears to present several practical challenges (financial and technology limitations in the analysis, storage and retrieval of past experience) including the danger that a successful response in one crisis situation may not be appropriate for a similar crisis situation in the future. It became apparent that a timely response depends in most cases on ‘local knowledge’ which is quite difficult to be captured and centralised in its entirety, especially when signal detection relies on a complex network of detectors. Another interesting finding, related with the premise of ‘local knowledge’, was that the transmission of signals from the detectors to the decision makers should be relatively straightforward without many ‘hubs’ in between. Hubs may perhaps offer the benefit of filtering signals from noise and collating them in a way that may facilitate the decision makers to make sense of them, however, this is done at the expense of a timely response and it may cause the loss of important signals in the process. Therefore, the emphasis should be put more in the availability of communication platforms for the detectors to transmit the captured signals to the decision makers rather than on complex, sophisticated (and expensive) ‘fusion hubs’ and filtering mechanisms. Ultimately, the successful signal detection depends more on the ‘collective mind’ and the shared sense of purpose within the organisation rather than on predetermined repertoires and databases. Consistent with the social exchange theory principles discussed earlier in the paper, the first and most basic stage of crisis signal detection may simply be the sharing of time sensitive information and existing knowledge in an opportune fashion (for example the DHS's “See Something - Say Something” campaign for terrorism). However, social exchange theory alone cannot capture the complexity of signal detection since, at more advanced levels of maturity, signal detection and the ‘collective mind’ move from the opportune transmission of ‘what is’ to identifying ‘what could possibly be’ and where the signals of known and unknown crises could be captured. These findings have particular significance to tourism organisations since, due to their high interconnectivity with all aspects of society (political, economic, social, technological and environmental), they are more vulnerable to crises and are affected by every possible disruption from normalcy whether this is political or civil unrest, a natural catastrophe, economic recession, etc. Therefore, these findings may help practitioners who embark in the design of crisis management mechanisms in creating a basic framework of actors and conditions for the effective detection of crisis signal in their organization. The findings will help them identify detectors they can employ, scanning approaches, scanning domains and issues to be taken into consideration in the three stages of the detection process. For academic researchers, apart from the deeper exploration of three stages of the crisis signal detection, this study opens a wide range of areas for further investigation. For example, of great interest would be to investigate how the ‘crisis culture’ to which the participants in this study so often referred to can be developed and embedded throughout an organization. Another interesting area for research would be to investigate how a detection network can be populated and maintained, what strategies could be used for detector recruitment and engagement in both core and ‘ad hoc’ networks and how can these detectors could be maintained active and productive. Moreover, the scanning methodologies and approaches in the three organizational domains (physical, informational and cognitive) will differ significantly and this is an area that offers a wide scope for further research. One obvious direction for research would be to examine the efficacy of these methodologies in case studies of actual crises. The issue of crisis knowledge management appears to be another interesting avenue of research with significant implications in the design and exploitation of a signal detection system. Also, this study is based only on insights and views of corporate executives. Practitioners at other levels of the organization, i.e., regional VPs, area managers, hotel general managers and chief security officers may not completely share these views and may offer different insights on crisis signal detection in general or in particular aspects of it such as detection network, reliability of ‘ad hoc’ detectors, attributes of ‘core’ detectors, etc. Finally, it is worth pointing out that early interventions may prevent a crisis but may also merely change its course and, as a dynamic phenomenon, evolve in a different form of crisis. Further interventions may cause further evolution of the crisis and this process of co-evolution offers scope for the exploration of all crisis management components through the lens of chaos and complexity theories.

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