54 research outputs found

    The Importance of Personalization in Affecting Consumer Attitudes toward Mobile Advertising in China

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    Empowered by the Web’s interactive and quick-response capabilities, mobile marketing is a very promising direct marketing channel. The present research investigates consumer attitudes toward mobile advertising in China. The results of a survey indicate that (1) consumers in China generally have slightly negative attitudes toward receiving mobile advertising (2) there is a direct relationship between consumer attitudes and consumer intention in receiving mobile advertising. (3) Personalization plays an important role in affecting consumers’ attitude toward receiving mobile advertising. Thus the designers and marketers should better strategize their advertising designs by considering the personalization factor

    Design and implementation of location-based service for targeted advertising

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    Nowadays, mobile phones have been increasingly advertised. These performance advertisement tools altered to be one of the beneficial factors in order to promote products and services in national or local companies. One of the outstanding features of mobile phones is that everybody has accessibility in different circumstances and times. Companies struggle to draw customers attention by providing information, stimulating text or image to advertise their products by which high cost have been consumed. In this study, a system is designed and implemented for efficient and effective interaction between companies and customers. It is worth mentioning here this system has some great feature like being aware of text, owning mobile user Interface and presenting location-based service. These features enable companies to design an advertisement in a purposeful way. Such these advertisements can effectively be sent to the population company which are on target. Finally, the system was evaluated. Reduction in cost and effectiveness of advertising are grounded in the result of the study

    Network problems detection and classification by analyzing syslog data

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    Network troubleshooting is an important process which has a wide research field. The first step in troubleshooting procedures is to collect information in order to diagnose the problems. Syslog messages which are sent by almost all network devices contain a massive amount of data related to the network problems. It is found that in many studies conducted previously, analyzing syslog data which can be a guideline for network problems and their causes was used. Detecting network problems could be more efficient if the detected problems have been classified in terms of network layers. Classifying syslog data needs to identify the syslog messages that describe the network problems for each layer, taking into account the different formats of various syslog for vendors’ devices. This study provides a method to classify syslog messages that indicates the network problem in terms of network layers. The method used data mining tool to classify the syslog messages while the description part of the syslog message was used for classification process. Related syslog messages were identified; features were then selected to train the classifiers. Six classification algorithms were learned; LibSVM, SMO, KNN, Naïve Bayes, J48, and Random Forest. A real data set which was obtained from the Universiti Utara Malaysia’s (UUM) network devices is used for the prediction stage. Results indicate that SVM shows the best performance during the training and prediction stages. This study contributes to the field of network troubleshooting, and the field of text data classification

    Understanding the Attitude of Generation Z Consumers Towards Advertising Avoidance on the Internet

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    One of the biggest challenges faced by marketers today is to comprehend the reasons behind people’s avoidance towards advertisements worldwide, and how that can be managed. Although many researchers have explored the subject in both traditional and contemporary marketing communication mediums, there is no evidence of studies conducted in the context of Bangladeshi market, with specific concentration on Generation Z consumers. This generation constitutes a significant portion of the entire population in the country, indicating that a major share of current and potential customers belong to this age group. Intriguingly, even though they are characterized as highly tech-savvy customers, they are also more likely to avoid online advertisements, making the marketing efforts of organizations ineffective. Therefore, this study investigates the determinants that cause the Generation Z consumers in Bangladesh to avoid advertisements on the Internet. The collected data from 280 respondents were analyzed through descriptive statistics using SPSS.24, followed by confirmatory factor analysis (CFA) and structure equation modeling (SEM), which were performed with the help of AMOS.17 to eventually test the hypotheses developed for this study. The findings indicate goal impediment, privacy concern, ad clutter, and negative experiences are positively related to advertising avoidance online. Keywords: advertising avoidance, goal impediment, privacy concern, ad clutter, negative experience, and generation-z consumer

    Estimating Trust Strength For Supporting Effective Recommendation Services

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    In the age of information explosion, Internet facilitates product searching and collecting much more convenient for users. However, it is time-consuming and exhausting for users to deal with large amounts of product information. In response, various recommendation approaches have been developed to recommend products that match users’ preferences and requirements. In addition to the well-known collaborative filtering recommendation approach, the trust-based recommendation approach is the emerging one. The reason is that most of online communities allow users to express their trust on other users. Based on the analysis of trust relationships, the trust-based recommendation approach finds out and consults the opinions of more reliable users and therefore makes better recommendations. Existing trust-based recommendation techniques consider all trust relationships in a given trust network equally important and give them the same trust strength. However, in a real-world setting, trust relationships may be of various strengths. In response, in this study, we propose a mechanism for trust strength estimation on the basis of the machine learning approach and estimate the trust strength for each existing trust relationship in a given trust network. To overcome the sparsity of the trust network, we also develop a modified trust propagation method to expand the original trust network. Finally, we perform a series of experiments to demonstrate the performance of our trust-based recommendation approach based on the trust strength estimation mechanism. Our empirical evaluation results show that our proposed approach outperforms our benchmark techniques, i.e., the traditional collaborative filtering approach and the original trust-based one

    An Exploratory Study for Perceived Advertising Value in the Relationship Between Irritation and Advertising Avoidance on the Mobile Social Platforms

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    This study delves deeply into advertising avoidance research and redefines the uses and gratifications theory (U&G) as divided into (a) convenience U&G, (b) content U&G, and (c) social U&G to conduct an approach to alleviate the degree of advertising avoidance on the mobile social platforms. To carefully study the forming framework of advertising avoidance, we extract the factor irritation considered to directly impact on avoidant intention induced by perceived intrusiveness and privacy concerns. As an important previous factor in advertising research, we also test the moderating effect of perceived advertising value between irritation and advertising avoidance. Findings show that ubiquity takes a negative role on mobile social platforms and tailoring also takes different roles on perceived intrusiveness and privacy concerns; unfortunately, content U&G consist of advertising informativeness and entertainment didn’t find any significant effect; in contrast with previous study, social U&G as social interaction and social integration also show some different roles but is ambiguous. However, the positive relationship of perceived intrusiveness, privacy concerns, irritation, and advertising avoidance has been confirmed again although with a pity of insignificant moderating effect of advertising value. Management issues, theoretical contributions, limitations and future study are discussed as follow

    A Novel Contextual Information Recommendation Model and Its Application in e-Commerce Customer Satisfaction Management

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    In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods

    An Empirical Comparison of Dissimilarity Measures for Recommender Systems

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    Many content-based recommendation approaches are based on a dissimilarity measure based on the product attributes. In this paper, we evaluate four dissimilarity measures for product recommendation using an online survey. In this survey, we asked users to specify which products they considered to be relevant recommendations given a reference product. We used microwave ovens as product category. Based on these responses, we create a relative relevance matrix we use to evaluate the dissimilarity measures with. Also, we use this matrix to estimate weights to be used in the dissimilarity measures. In this way, we evaluate four dissimilarity measures: the Euclidean Distance, the Hamming Distance, the Heterogeneous Euclidean-Overlap Metric, and the Adapted Gower Coefficient. The evaluation shows that these weights improve recommendation performance. Furthermore, the experiments indicate that when recommending a single product, the Heterogeneous Euclidean-Overlap Metric should be used and when recommending more than one product the Adapted Gower Coefficient is the best alternative. Finally, we compare these dissimilarity measures with a collaborative method and show that this method performs worse than the dissimilarity based approaches
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