Suchergebnisse

Natural and man‐made disasters potentially cause significant damage and disruption to communities. As population density increases and natural disasters become more extreme, it becomes increasingly important to both manage communications and extract information from communications to be able to mitigate the negative effects of such disasters. The emergence of social media platforms has led to new avenues for the collection and dissemination of information that is either local or global. Although social studies have revealed that social media feeds can improve effective disaster preparedness and recovery, it has been observed that the use of social media can have severe negative consequences through the rapid spread of false information leading to the inappropriate allocation of resources and in extreme cases panic and lawlessness. Rumours and false information are likely to affect appropriate corporate responses as well as, where appropriate, responses of public organizations tasked with appropriately responding to natural and man‐made disasters. The ability to identify instances of false information through the course of natural and man‐made disasters is a critical capability for corporate and public bodies in order to improve disaster management and response.To reduce the impact of these rumours, a technique is proposed that makes use of supervised learning to differentiate between information about an actual event from information about a false one and communicating it effectively to appropriate organizations. For this purpose, 934 social media feeds were analysed using a Naïve Bayes classifier. Clearly, applied early on this technique potentially can improve the quality of disaster response and recovery and mitigate the negative consequences.Broadly speaking, we consider this research to relate to the management of knowledge and information flow in disaster situations. Clearly, the techniques introduced in this paper, in providing individuals and organizations with access to knowledge rather than false information and rumours, will help organizations manage resources and activities during disasters more efficiently and effectively. The study concludes with the implications, limitations, and future directions.

PurposeThe new ways to complete financial transactions have been developed by setting up mobile payment (m-payment) platforms and such platforms to access banking in the financial mainstream can transact as never before. But, does m-payment have veiled consequences? To seek an answer, the research was undertaken to explore the dark sides of m-payment for consumers by extending the theory of innovation resistance (IR) and by measuring non-adoption intention (NAI).Design/methodology/approachThree hundred individuals using popular online m-payment apps such as Paytm, PhonePe, Amazon Pay and Google Pay were surveyed for the primary data. IBM AMOS based structural equation modelling (SEM) was used to analyse the data.FindingsEach m-payment transaction leaves a digital record, making some vulnerable consumers concerned about privacy threats. Lack of global standards prevents consumers from participating in the m-payment system properly until common interfaces are established based on up-to-date standards. Self-compassion (SC) characteristics such as anxiety, efficacy, fatigue, wait-and-see tendencies and the excessive choice of technology effect contribute to the non-adoption of m-payment.Originality/valueThis study proposes a threat model and empirically explores the dark sides of m-payment. In addition, it also unveils the moderator's role of SC in building the structural relationship between IR and NAI.

PurposeThe current study tries to better understand the resistance toward food delivery applications (FDAs). This study has adapted the existing criteria to measure different consumer barriers toward FDAs. It also examined the relationships between various consumer barriers, intention to use FDAs and word-of-mouth (WOM).Design/methodology/approachThis study utilized the innovation resistance theory (IRT) and a mixed-method approach comprised of qualitative essays submitted by 125 respondents and primary surveys (N = 366) of FDA users.FindingsTradition barrier (trust) shared a negative association with use intention, while image barrier (poor customer service) shared a negative association with WOM. The intention to use was positively associated with WOM. Additionally, the study results reveal that image barrier (poor customer experience) and value barrier (poor quality control) were, in fact, positively related to WOM. This study also discusses the managerial and theoretical implications of these findings and the scope for further research on FDAs.Originality/valueFDAs have revolutionized the food delivery industry and made it more comfortable and convenient for the consumers. However, FDA service providers are facing challenges from both customers and restaurants. Although scholars investigated customer behavior toward FDAs, no prior study has focused on consumer barriers toward FDA usage.