19 resultados para advertising, avoidance, online social networking, perceptions, privacy, teenagers, trust
em Aston University Research Archive
Resumo:
Purpose – Traditionally, most studies focus on institutionalized management-driven actors to understand technology management innovation. The purpose of this paper is to argue that there is a need for research to study the nature and role of dissident non-institutionalized actors’ (i.e. outsourced web designers and rapid application software developers). The authors propose that through online social knowledge sharing, non-institutionalized actors’ solution-finding tensions enable technology management innovation. Design/methodology/approach – A synthesis of the literature and an analysis of the data (21 interviews) provided insights in three areas of solution-finding tensions enabling management innovation. The authors frame the analysis on the peripherally deviant work and the nature of the ways that dissident non-institutionalized actors deviate from their clients (understood as the firm) original contracted objectives. Findings – The findings provide insights into the productive role of solution-finding tensions in enabling opportunities for management service innovation. Furthermore, deviant practices that leverage non-institutionalized actors’ online social knowledge to fulfill customers’ requirements are not interpreted negatively, but as a positive willingness to proactively explore alternative paths. Research limitations/implications – The findings demonstrate the importance of dissident non-institutionalized actors in technology management innovation. However, this work is based on a single country (USA) and additional research is needed to validate and generalize the findings in other cultural and institutional settings. Originality/value – This paper provides new insights into the perceptions of dissident non-institutionalized actors in the practice of IT managerial decision making. The work departs from, but also extends, the previous literature, demonstrating that peripherally deviant work in solution-finding practice creates tensions, enabling management innovation between IT providers and users.
Resumo:
Word of mouth (WOM) communication is a major part of online consumer interactions, particularly within the environment of online communities. Nevertheless, existing (offline) theory may be inappropriate to describe online WOM and its influence on evaluation and purchase.The authors report the results of a two-stage study aimed at investigating online WOM: a set of in-depth qualitative interviews followed by a social network analysis of a single online community. Combined, the results provide strong evidence that individuals behave as if Web sites themselves are primary "actors" in online social networks and that online communities can act as a social proxy for individual identification. The authors offer a conceptualization of online social networks which takes the Web site into account as an actor, an initial exploration of the concept of a consumer-Web site relationship, and a conceptual model of the online interaction and information evaluation process. © 2007 Wiley Periodicals, Inc. and Direct Marketing Educational Foundation, Inc.
Resumo:
Socially constructed marketing imageries (e.g. e-atmospherics) help consumers while making choices and decisions. Still, human and retailing technology interactions are rarely evaluated from a social practice perspective. This article explores the potential impact of socially constructed e-atmospherics on impulse buying. A framework with three interrelated factors, namely social acoustic, co-construction and mundane language enactment is analysed. The way these allow for e-social norms to organically emerge is elaborated through a set of propositions. Retailing implications are subsequently discussed.
Resumo:
In recent years, the rapid spread of smartphones has led to the increasing popularity of Location-Based Social Networks (LBSNs). Although a number of research studies and articles in the press have shown the dangers of exposing personal location data, the inherent nature of LBSNs encourages users to publish information about their current location (i.e., their check-ins). The same is true for the majority of the most popular social networking websites, which offer the possibility of associating the current location of users to their posts and photos. Moreover, some LBSNs, such as Foursquare, let users tag their friends in their check-ins, thus potentially releasing location information of individuals that have no control over the published data. This raises additional privacy concerns for the management of location information in LBSNs. In this paper we propose and evaluate a series of techniques for the identification of users from their check-in data. More specifically, we first present two strategies according to which users are characterized by the spatio-temporal trajectory emerging from their check-ins over time and the frequency of visit to specific locations, respectively. In addition to these approaches, we also propose a hybrid strategy that is able to exploit both types of information. It is worth noting that these techniques can be applied to a more general class of problems where locations and social links of individuals are available in a given dataset. We evaluate our techniques by means of three real-world LBSNs datasets, demonstrating that a very limited amount of data points is sufficient to identify a user with a high degree of accuracy. For instance, we show that in some datasets we are able to classify more than 80% of the users correctly.
Resumo:
The popularity of online social media platforms provides an unprecedented opportunity to study real-world complex networks of interactions. However, releasing this data to researchers and the public comes at the cost of potentially exposing private and sensitive user information. It has been shown that a naive anonymization of a network by removing the identity of the nodes is not sufficient to preserve users’ privacy. In order to deal with malicious attacks, k -anonymity solutions have been proposed to partially obfuscate topological information that can be used to infer nodes’ identity. In this paper, we study the problem of ensuring k anonymity in time-varying graphs, i.e., graphs with a structure that changes over time, and multi-layer graphs, i.e., graphs with multiple types of links. More specifically, we examine the case in which the attacker has access to the degree of the nodes. The goal is to generate a new graph where, given the degree of a node in each (temporal) layer of the graph, such a node remains indistinguishable from other k-1 nodes in the graph. In order to achieve this, we find the optimal partitioning of the graph nodes such that the cost of anonymizing the degree information within each group is minimum. We show that this reduces to a special case of a Generalized Assignment Problem, and we propose a simple yet effective algorithm to solve it. Finally, we introduce an iterated linear programming approach to enforce the realizability of the anonymized degree sequences. The efficacy of the method is assessed through an extensive set of experiments on synthetic and real-world graphs.
Resumo:
In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.
Resumo:
Using survey data from 358 online customers, the study finds that the e-service quality construct conforms to the structure of a third-order factor model that links online service quality perceptions to distinct and actionable dimensions, including (1) website design, (2) fulfilment, (3) customer service, and (4) security/privacy. Each dimension is found to consist of several attributes that define the basis of e-service quality perceptions. A comprehensive specification of the construct, which includes attributes not covered in existing scales, is developed. The study contrasts a formative model consisting of 4 dimensions and 16 attributes against a reflective conceptualization. The results of this comparison indicate that studies using an incorrectly specified model overestimate the importance of certain e-service quality attributes. Global fit criteria are also found to support the detection of measurement misspecification. Meta-analytic data from 31,264 online customers are used to show that the developed measurement predicts customer behavior better than widely used scales, such as WebQual and E-S-Qual. The results show that the new measurement enables managers to assess e-service quality more accurately and predict customer behavior more reliably.
Resumo:
Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.
Resumo:
Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.
Resumo:
Handheld and mobile technologies have witnessed significant advances in functionality, leading to their widespread use as both business and social networking tools. Human-Computer Interaction and Innovation in Handheld, Mobile and Wearable Technologies reviews concepts relating to the design, development, evaluation, and application of mobile technologies. Studies on mobile user interfaces, mobile learning, and mobile commerce contribute to the growing body of knowledge on this expanding discipline.
Resumo:
The authors propose a new approach to discourse analysis which is based on meta data from social networking behavior of learners who are submerged in a socially constructivist e-learning environment. It is shown that traditional data modeling techniques can be combined with social network analysis - an approach that promises to yield new insights into the largely uncharted domain of network-based discourse analysis. The chapter is treated as a non-technical introduction and is illustrated with real examples, visual representations, and empirical findings. Within the setting of a constructivist statistics course, the chapter provides an illustration of what network-based discourse analysis is about (mainly from a methodological point of view), how it is implemented in practice, and why it is relevant for researchers and educators.
Resumo:
In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.
Resumo:
Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification. © 2014 ACM.
Resumo:
Polycystic ovary syndrome affects 6 percent of women. Symptoms include hirsutism, acne, and infertility. This research explores the impact of polycystic ovary syndrome on women's lives using photovoice. Nine participants photographed objects related to their quality of life and made diary entries explaining each photograph. Three themes emerged from thematic analysis of the diaries: control (of symptoms and polycystic ovary syndrome controlling their lives), perception (of self, others, and their situation), and support (from relationships, health care systems, and education). These findings illuminate positive aspects of living with polycystic ovary syndrome and the role pets and social networking sites play in providing support for women with polycystic ovary syndrome.
Resumo:
Full text: There are phrases in daily use today which were not so common a decade or so back, such as ‘ageing population’ or ‘climate changes’ or ‘emerging markets’ or even ‘social networking’. How do these things affect our lives is certainly interesting but for us as eye care practitioners how these changes affect our clinical work may be also relevant and sometimes more interesting. A recent advertisement for recruitment to the Royal Marine Corps of the British Army ended with a comment ‘find us on Facebook!’ The BCLA, IACLE and other organisations as well as many manufacturers have their own Facebook groups. In 2011 Chandni Thakkar was awarded the BCLA summer studentship and her project was based around increasing the contact lens business of a small independent optometric practice where contact lens sales were minimal. The practice typically recruited one new wearer per month. Chandni was able to increase the number of new patient fits with various strategies (her work was presented as poster at the 2012 BCLA conference in Birmingham). One of her strategies was to start a Facebook group and 655 joined the special group she started in just over a month. Interestingly she found that the largest single factor in convincing patients to trial contact lenses was recommendation by the eye care practitioner at the end of the examination, but nonetheless it is interesting that so many people used the social networking site to find out more information regarding contact lenses in her study. Moreover, we already see the use, by some practitioners, of smart phone ‘apps’ or electronic diaries or text messages when coordinating patient check-ups. Climate change has affected the way we think and act; we now leave out special recycle bins and we hope that the items that are recyclable are actually recycled and do not just join our other refuse somewhere down the track! How environmentally friendly are contact lenses? This was discussed by various speakers at this year's BCLA conference in Birmingham. Daily disposable lenses surely produce more contact lens waste but do not involve solutions in plastic bottles like monthly lenses. It is certainly something that manufacturers are taking seriously and of course there are environmental benefits but the cynic in each of us sees the marketing potential too. The way the ageing population is certainly something that will impact all healthcare providers. In the case of eye care with people living longer they will need refractive corrections for longer. Furthermore, since presbyopes are not resigning themselves to only gentle hobbies like knitting and gardening, but instead want to continue playing tennis or skiing or whatever, their visual demands are becoming more complex. This is certainly an area that contact lens manufacturers are focussing on (pun not intended!). Again the BCLA conference in Birmingham saw the launch of various new products by different companies to help us deal with our presbyopic contact lens wearers. It is great to have such choice and now with fitting methods becoming easier too we have no excuse not to try them out with our clients. Finally to emerging markets – well there was not a specific session at the BCLA conference in May discussing this but this most certainly would have been discussed by professional services managers and marketing directors of most of the contact lens companies. ‘How will we conquer China?’ ‘How can we increase our market share in Russia?’ Or ‘How should we spend our marketing budget in India?’ These topics as well as others would certainly have cropped up in backroom discussions. Certainly groups like IACLE (International Association of CL Educators) have increasing numbers of members and activities in developing markets to ensure that educators educate, to that practitioners can practice successfully and in turn patients can become successful contact lenses wearers. Companies also wish to increase their market share in these developing markets and from the point of view of CLAE we are certainly seeing more papers being submitted from these parts of the world. The traditional centres of knowledge are being challenged, I suppose as they have been throughout history, and this can only be a good thing for the pursuit of science. The BCLA conference in Birmingham welcomed more international visitors than ever, and from more countries, and long may that continue. Similarly, CLAE looks forward to a wider audience in years to come and a wider network of authors too.