The Effects of Trust Transference, Mobile Attributes 89 BAR

Abstract Trust is essential in building relationships. In mobile commerce, as in electronic commerce, trust is even more valuable given the absence of human contact and direct observation of the service provider. Despite the importance of trust for mobile commerce, there has been little academic effort to study the relationships between mobile devices unique components of interactivity and customer trust, or the relationship between offline, online and mobile trust. This study proposes a trust-mediated model for customer attitude and transaction intentions in mobile commerce contexts that incorporates trust transference and unique factors present in mobile commerce. Data were collected in an online survey and analyzed via structural equations modeling. Results suggest that trust transferred from online contexts and ease of use have significant effects on mobile trust formation, while also indicating that mobile trust influences consumers' attitudes and intentions to purchase using mobile devices

Mobile Advertising and its Acceptance by American Consumers

Mobile advertising creates opportunities for marketers to capture the attention of consumers on a one-to-one basis. However, there is little data proving the success of mobile advertising models. This study examined the drivers that influence consumer’s acceptance of SMS-based mobile advertisements and the differences in responses between two cultures, Finland and the United States. My questionnaire and conceptual model was taken from a study done in Finland in 2007. My results indicated that utility, context and trust are positively related to the acceptance of mobile advertising while sacrifice is negatively related. Control did not have a clear relationship to acceptance because three of the four questions about control averaged more than 6.0 on a seven-point scale meaning there was no variation in the responses. However, when a separate regression was done on the remaining control question, a strong positive relationship between that question and acceptance was found

An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks

Customer empowerment in tourism through Consumer Centric Marketing (CCM)

We explain Consumer Centric Marketing (CCM) and adopt this new technique to travel context. Benefits and disadvantages of the CCM are outlined together with warnings of typical caveats Value: CCM will be expected as the norm in the travel industry by customers of the future, yet it is only the innovators who gain real tangible benefits from this development. We outline current and future opportunities to truly place your customer at the centre and provide the organisation with some real savings/gains through the use of ICT Practical Implications: We offer tangible examples for travel industry on how to utilise this new technology. The technology is already available and the ICT companies are keen to establish ways how consumers can utilise it, i.e. by providing ‘content’ for these ICT products the travel industry can fully gain from these developments and also enhance consumers’ gains from it. This can result in more satisfied customers for the travel (as well as ICT) companies thus truly adopting the basic philosophy of marketin