If Big Brother comes to a venue near you! Employee surveillance issues and the communication professional

Todoy's monogers-drowing on the expertise of their IT professiono/s-perceived levels of trust within the organisation, cantribute to employees' increased stress and decreased job dissatisfaction, and affect productivity. Highlighted are current trends in workplace privacy, key communication and control issues, the current legal climate, and ethical issues that communication professionals need to address to forestall future problems. lA questionnaire is included as a starting point for communication professionals to assess their own attitudes and values to workplace surveillance.

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.

An examination of the relationship between emotional intelligence, leadership style and perceived leadership outcomes in Australian educational institutions

In the field of leadership studies transformational leadership theory (e.g., Bass, 1985; Avolio, Bass, & Jung, 1995) has received much attention from researchers in recent years (Hughes, Ginnet, & Curphy, 2009; Hunt, 1999). Many previous studies have found that transformational leadership is related to positive outcomes such as the satisfaction, motivation and performance of followers in organisations (Judge & Piccolo, 2004; Lowe, Kroeck, & Sivasubramaniam, 1996), including in educational institutions (Chin, 2007; Leithwoood & Jantzi, 2005). Hence, it is important to explore constructs that may predict leadership style in order to identify potential transformational leaders in leadership assessment and selection procedures. Several researchers have proposed that emotional intelligence (EI) is one construct that may account for hitherto unexplained variance in transformational leadership (Mayer, 2001; Watkin, 2000). Different models of EI exist (e.g., Goleman, 1995, 2001; Bar-On, 1997; Mayer & Salovey, 1997) but momentum is growing for the Mayer and Salovey (1997) model to be considered the most useful (Ashkanasy & Daus, 2005; Daus & Ashkanasy, 2005). Studies in non-educational settings claim to have found that EI is a useful predictor of leadership style and leader effectiveness (Harms & Crede, 2010; Mills, 2009) but there is a paucity of studies which have examined the Mayer and Salovey (1997) model of EI in educational settings. Furthermore, other predictor variables have rarely been controlled in previous studies and only self-ratings of leadership behaviours, rather than multiple ratings, have usually been obtained. Therefore, more research is required in educational settings to answer the question: to what extent is the Mayer and Salovey (1997) model of EI a useful predictor of leadership style and leadership outcomes? This project, set in Australian educational institutions, was designed to move research in the field forward by: using valid and reliable instruments, controlling for other predictors, obtaining an adequately sized sample of real leaders as participants and obtaining multiple ratings of leadership behaviours. Other variables commonly used to predict leadership behaviours (personality factors and general mental ability) were assessed and controlled in the project. Additionally, integrity was included as another potential predictor of leadership behaviours as it has previously been found to be related to transformational leadership (Parry & Proctor-Thomson, 2002). Multiple ratings of leadership behaviours were obtained from each leader and their supervisors, peers and followers. The following valid and reliable psychological tests were used to operationalise the variables of interest: leadership styles and perceived leadership outcomes (Multifactor Leadership Questionnaire, Avolio et al., 1995), EI (Mayer–Salovey–Caruso Emotional Intelligence Test, Mayer, Salovey, & Caruso, 2002), personality factors (The Big Five Inventory, John, Donahue, & Kentle, 1991), general mental ability (Wonderlic Personnel Test-Quicktest, Wonderlic, 2003) and integrity (Integrity Express, Vangent, 2002). A Pilot Study (N = 25 leaders and 75 raters) made a preliminary examination of the relationship between the variables included in the project. Total EI, the experiential area, and the managing emotions and perceiving emotions branches of EI, were found to be related to transformational leadership which indicated that further research was warranted. In the Main Study, 144 leaders and 432 raters were recruited as participants to assess the discriminant validity of the instruments and examine the usefulness of EI as a predictor of leadership style and perceived leadership outcomes. Scores for each leadership scale across the four rating levels (leaders, supervisors, peers and followers) were aggregated with the exception of the management-by-exception active scale of transactional leadership which had an inadequate level of interrater agreement. In the descriptive and measurement component of the Main Study, the instruments were found to demonstrate adequate discriminant validity. The impact of role and gender on leadership style and EI were also examined, and females were found to be more transformational as leaders than males. Females also engaged in more contingent reward (transactional leadership) behaviours than males, whilst males engaged in more passive/avoidant leadership behaviours than females. In the inferential component of the Main Study, multiple regression procedures were used to examine the usefulness of EI as a predictor of leadership style and perceived leadership outcomes. None of the EI branches were found to be related to transformational leadership or the perceived leadership outcomes variables included in the study. Openness, emotional stability (the inverse of neuroticism) and general mental ability (inversely) each predicted a small amount of variance in transformational leadership. Passive/avoidant leadership was inversely predicted by the understanding emotions branch of EI. Overall, EI was not found to be a useful predictor of leadership style and leadership outcomes in the Main Study of this project. Implications for researchers and human resource practitioners are discussed.