The impact of online reviews on hotel booking intentions and trust

A growing reliance on the Internet as an information source when making choices about tourism products raises the need for more research into electronic word of mouth. Within a hotel context, this study explores the role of four key factors that influence perceptions of trust and consumer choice. An experimental design is used to investigate four independent variables: the target of the review (core or interpersonal); overall valence of a set of reviews (positive or negative); framing of reviews (what comes first: negative or positive information); and whether or not a consumer generated numerical rating is provided together with the written text. Consumers seem to be more influenced by early negative information, especially when the overall set of reviews is negative. However, positively framed information together with numerical rating details increases both booking intentions and consumer trust. The results suggest that consumers tend to rely on easy-to-process information, when evaluating a hotel based upon reviews. Higher levels of trust are also evident when a positively framed set of reviews focused on interpersonal service.

A survey on the relationship between trust and interest similarity in online social networks

A remarkable growth in quantity and popularity of online social networks has been observed in recent years. There is a good number of online social networks exists which have over 100 million registered users. Many of these popular social networks offer automated recommendations to their users. This automated recommendations are normally generated using collaborative filtering systems based on the past ratings or opinions of the similar users. Alternatively, trust among the users in the network also can be used to find the neighbors while making recommendations. To obtain the optimum result, there must be a positive correlation exists between trust and interest similarity. Though the positive relations between trust and interest similarity are assumed and adopted by many researchers; no survey work on real life people’s opinion to support this hypothesis is found. In this paper, we have reviewed the state-of-the-art research work on trust in online social networks and have presented the result of the survey on the relationship between trust and interest similarity. Our result supports the assumed hypothesis of positive relationship between the trust and interest similarity of the users.

Developing trust networks based on user tagging information for recommendation making

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user’s interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user’s personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.

SimTrust : a new method of trust network generation

Trust can be used for neighbor formation to generate automated recommendations. User assigned explicit rating data can be used for this purpose. However, the explicit rating data is not always available. In this paper we present a new method of generating trust network based on user’s interest similarity. To identify the interest similarity, we use user’s personalized tag information. This trust network can be used to find the neighbors to make automated recommendation. Our experiment result shows that the precision of the proposed method outperforms the traditional collaborative filtering approach.

Managing Trust in Online Social Networks

In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Recommendations may be received through a chain of friends network, so the problem for the user is to be able to evaluate various types of trust opinions and recommendations. This opinion or recommendation has a great influence to choose to use or enjoy the item by the other user of the community. Collaborative filtering system is the most popular method in recommender system. The task in collaborative filtering is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users. Because of the different taste of different people, they rate differently according to their subjective taste. If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster. But the collaborative filtering system performs poor when there is insufficient previous common rating available between users; commonly known as cost start problem. To overcome the cold start problem and with the dramatic growth of online social networks, trust based approach to recommendation has emerged. This approach assumes a trust network among users and makes recommendations based on the ratings of the users that are directly or indirectly trusted by the target user.

Trust and reputation management in web-based social network

know personally. They also communicate with other members of the network who are the friends of their friends and may be friends of their friend’s network. They share their experiences and opinions within the social network about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Opinions, reputations and ecommendations will influence users' choice and usage of online resources. Recommendations may be received through a chain of friends of friends, so the problem for the user is to be able to evaluate various types of trust recommendations and reputations. This opinion or ecommendation has a great influence to choose to use or enjoy the item by the other user of the community. Users share information on the level of trust they explicitly assign to other users. This trust can be used to determine while taking decision based on any recommendation. In case of the absence of direct connection of the recommender user, propagated trust could be useful.

Haunted by the past : effects of poor management on employee attitudes and turnover

Change management research has largely ignored the effects of organizational change management history in shaping employee attitudes and behavior. This article develops and tests a model of the effects of poor change management history (PCMH) on employee attitudes (trust, job satisfaction, turnover intentions, change cynicism, and openness to change) and actual turnover. We found that PCMH, through PCMH beliefs, led to lower trust, job satisfaction and openness to change, and higher cynicism and turnover intentions. Also, PCMH beliefs predicted employee turnover over 2 years.

Building organizational capability through a quality assurance program within a business school and encouraging organizational culture change

This study explores organizational capability and culture change through a project developing an assurance of learning program in a business school. In order to compete internationally for high quality faculty, students, strategic partnerships and research collaborations it is essential for Universities to develop and maintain an international focus and a quality produce that predicts excellence in the student experience and graduate outcomes that meet industry needs. Developing, marketing and delivering that quality product requires an organizational strategy to which all members of the organization contribute and adhere. Now, the ability to acquire, share and utilize knowledge has become a critical organizational capability in academia as well as other industries. Traditionally the functional approach to business school structures and disparate nature of the social networks and work contact limit the sharing of knowledge between academics working in different disciplines. In this project a community of practice program was established to include academics in the development of an embedded assurance of learning program affecting more than 5000 undergraduate students and 250 academics from nine different disciplines across four schools. The primary outcome from the fully developed and implemented assurance of learning program was the five year accreditation of the business schools programs by two international accrediting bodies, EQUIS and AACSB. However this study explores a different outcome, namely the change in organizational culture and individual capabilities as academics worked together in teaching and learning teams. This study uses a survey and interviews with academics involved, through a retrospective panel design which contained an experimental group and a control group. Results offer insights into communities of practice as a means of addressing organizational capability and changes in organizational culture. Knowledge management and shared learning can achieve strategic and operational benefits equally within academia as within other industrial enterprises but it comes at a cost. Traditional structures, academics that act like individual contractors and deep divides across research, teaching and service interest served a different master and required fewer resources. Collaborative structures; fewer master categories of discrete knowledge areas; specific strategic goals; greater links between academics and industry; and the means to share learned insights will require a different approach to resourcing both the individual and the team.