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1 |
Why tourists engage in online collective actions in times of crisis: Exploring the role of group relative deprivation
چرا گردشگران در مواقع بحرانی به اقدامات جمعی آنلاین می پردازند: بررسی نقش محرومیت نسبی گروه-2020 Social media platforms empower tourists to engage in secondary crisis communication and even take collective
action against destinations. Such online actions result in challenges for tourism destinations related to crisis
management and image restoration, especially for human-induced tourism crises caused internally by managerial
or institutional faults. Based on the theory of relative deprivation and examining the reputation crisis of
Snow Town as a case, this study aims to understand the cognitive, emotional, and behavioral mechanisms of
tourists’ secondary crisis communications. The results show that group relative deprivation perceived by tourists
can elicit their group-based anger and distrust toward the destination and can also lead to online collective
action and offline behavioral intentions (here, negative travel intention). Additionally, the results show a reverse
influence of aim-oriented and behavior-oriented online collective actions on travel intentions and that aimoriented
actions positively mediate anger and travel intentions. This study provides new insights into how a
personal incident evolves to become a tourism crisis during social-media communications and discusses managerial
implications for crisis management and post-crisis marketing. Keywords: Group relative deprivation | Group-based anger | Destination trust | Collective online actions | Secondary crisis communication | Crisis management |
مقاله انگلیسی |
2 |
Big data prediction of durations for online collective actions based on peak’s timing
پیش بینی داده های بزرگ زمانی برای اقدامات جمعی آنلاین بر اساس زمان قله-2018 Peak Model states that each collective action has a life circle, which contains four periods of
‘‘prepare’’, ‘‘outbreak’’, ‘‘peak’’, and ‘‘vanish’’; and the peak determines the max energy and
the whole process. The peak model’s re-simulation indicates that there seems to be a stable
ratio between the peak’s timing (TP) and the total span (T) or duration of collective actions,
which needs further validations through empirical data of collective actions. Therefore, the
daily big data of online collective actions is applied to validate the model; and the key is
to check the ratio between peak’s timing and the total span. The big data is obtained from
online data recording & mining of websites. It is verified by the empirical big data that
there is a stable ratio between TP and T; furthermore, it seems to be normally distributed.
This rule holds for both the general cases and the sub-types of collective actions. Given
the distribution of the ratio, estimated probability density function can be obtained, and
therefore the span can be predicted via the peak’s timing. Under the scenario of big data,
the instant span (how long the collective action lasts or when it ends) will be monitored and
predicted in real-time. With denser data (Big Data), the estimation of the ratio’s distribution
gets more robust, and the prediction of collective actions’ spans or durations will be more
accurate.
Keywords: Big data ، Prediction ، Peak’s timing ، Ratio ، Span ، Collective actions |
مقاله انگلیسی |