Employee perceptions of reputation : an ethnographic study

The ability of organizational members to identify and analyse stakeholder opinion is critical to the management of corporate reputation. In spite of the significance of these abilities to corporate reputation management, there has been little effort to document and describe internal organizational influences on such capacities. This ethnographic study conducted in Red Cross Queensland explores how cultural knowledge structures derived from shared values and assumptions among organizational members influence their conceptualisations of organizational reputation. Specifically, this study explores how a central attribute of organizational culture – the property of cultural selection – influences perceptions of organizational reputation held by organizational members. We argue that these perceptions are the result of collective processes that synthesise (with varying degrees of consensus) member conceptualisations, interpretations, and representations of environmental realities in which their organization operates. Findings and implications for organizational action suggest that while external indicators of organizational reputation are acknowledged by members as significant, the internal influence of organizational culture is a far stronger influence on organizational action.

Reputation modelling in citizen science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, as the gathered information is from the crowd, the data quality is always hard to manage. There are many ways to manage data quality, and reputation management is one of the common approaches. In recent year, many research teams have deployed many audio or image sensors in natural environment in order to monitor the status of animals or plants. The collected data will be analysed by ecologists. However, as the amount of collected data is exceedingly huge and the number of ecologists is very limited, it is impossible for scientists to manually analyse all these data. The functions of existing automated tools to process the data are still very limited and the results are still not very accurate. Therefore, researchers have turned to recruiting general citizens who are interested in helping scientific research to do the pre-processing tasks such as species tagging. Although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Therefore, this research aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we aim to investigate how to use reputation management to enhance data reliability. Reputation systems have been used to solve the uncertainty and improve data quality in many marketing and E-Commerce domains. The commercial organizations which have chosen to embrace the reputation management and implement the technology have gained many benefits. Data quality issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. However, research on reputation management in this area is relatively new. We therefore start our investigation by examining existing reputation systems in different domains. Then we design novel reputation management approaches for Citizen Science projects to categorise participants and data. We have investigated some critical elements which may influence data reliability in Citizen Science projects. These elements include personal information such as location and education and performance information such as the ability to recognise certain bird calls. The designed reputation framework is evaluated by a series of experiments involving many participants for collecting and interpreting data, in particular, environmental acoustic data. Our research in exploring the advantages of reputation management in Citizen Science (or crowdsourcing in general) will help increase awareness among organizations that are unacquainted with its potential benefits.

Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

Product reputation model : an opinion mining based approach

Product rating systems are very popular on the web, and users are increasingly depending on the overall product ratings provided by websites to make purchase decisions or to compare various products. Currently most of these systems directly depend on users’ ratings and aggregate the ratings using simple aggregating methods such as mean or median [1]. In fact, many websites also allow users to express their opinions in the form of textual product reviews. In this paper, we propose a new product reputation model that uses opinion mining techniques in order to extract sentiments about product’s features, and then provide a method to generate a more realistic reputation value for every feature of the product and the product itself. We considered the strength of the opinion rather than its orientation only. We do not treat all product features equally when we calculate the overall product reputation, as some features are more important to customers than others, and consequently have more impact on customers buying decisions. Our method provides helpful details about the product features for customers rather than only representing reputation as a number only.

Reputation and Legitimacy : accreditation and rankings to assess organizations

A key perspective on reputation is that of assessment. Much of the communication literature focuses on the influence organizations have on impression formation. This chapter however suggests that in order to understand reputation assessment, it is also important to understand the related concept of legitimacy. It addresses two approaches to understanding reputation namely accreditation and ranking. Accreditation alludes to concepts of legitimacy in which firms may acquire credibility by meeting formalized standards of certification. Ranking deals with categorizing and rating organizational reputations so that they may be assessed relative to one another. The chapter explores the various ways in which the mechanisms of accreditation and ranking operate and the role of social actors in developing and applying them. Ranking systems that provide the mechanism for comparing organizations and assessing their relative value are also explored.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

Corporate reputation and financial performance : underlying dimensions of corporate reputation and their relation to sustained financial performance

This thesis examined the relationship between firms' corporate reputation and their future financial performance. Corporate reputation was represented by measuring the level of senior executives' attention to a number of intangible firm' resources (e.g. financial reputation, service culture) within firms' annual reports over a 17 year period. Initial findings suggested there was only a small relationship between reputation and future performance which lead to a reformulation of the problem. Reputation was posited to be a source of corporate resilience that helped firms with stronger reputations to sustain superior financial performance in times of difficulty, as well as allowing them to rebound more quickly from performance decline. Results suggest this interpretation of corporate reputation as well as indicating that industry sectors operate in different reputational 'domains' in which the relative importance of financial versus stakeholder aspects of corporate reputation varies.

Accused cleric built reputation at small school in Australia

The little Anglican boarding school of St Barnabas, in the mountain town of Ravenshoe, north Queensland, was where Robert Waddington built his reputation as an educationalist. He was headmaster there from 1961-70, but a growing number of former pupils are now coming forward to say they were subjected to sexual and physical abuse by him. Bim Atkinson, 58, said that his ordeal began after he was sent to the school and joined the choir. “I was absolutely petrified of the man,” Mr Atkinson recalls. “After I joined the choir, the abuse started. He would tell me to go to the sickbay, which had a connecting door to his bedroom and then I would end up in his room.”

Combining recommender and reputation systems to produce better online advice

Although recommender systems and reputation systems have quite different theoretical and technical bases, both types of systems have the purpose of providing advice for decision making in e-commerce and online service environments. The similarity in purpose makes it natural to integrate both types of systems in order to produce better online advice, but their difference in theory and implementation makes the integration challenging. In this paper, we propose to use mappings to subjective opinions from values produced by recommender systems as well as from scores produced by reputation systems, and to combine the resulting opinions within the framework of subjective logic.

Assessment of damages for defamation of business and personal reputation

An award of damages for defamation is to provide reparation for harm to a plaintiff’s reputation for the publication of defamatory material, compensate for any personal distress caused and vindicate the plaintiff’s reputation.1 Assessing such damages is recognised as a difficult task and perhaps the Queensland courts face further difficulties as there are few awards of damages for defamation in the state. This was pointed out in the recent decision of the Queensland Court of Appeal, Cerutti & Anor v Crestside Pty Ltd & Anor.2 This decision examined in detail the principles of assessing damages for defamation.

The economics of credence goods : an experiment on the role of liability, verifiability, reputation and competition

Credence goods markets are characterized by asymmetric information between sellers and consumers that may give rise to inefficiencies, such as under- and overtreatment or market breakdown. We study in a large experiment with 936 participants the determinants for efficiency in credence goods markets. While theory predicts that liability or verifiability yield efficiency, we find that liability has a crucial, but verifiability at best a minor, effect. Allowing sellers to build up reputation has little influence, as predicted. Seller competition drives down prices and yields maximal trade, but does not lead to higher efficiency as long as liability is violated. (JEL D12, D82)

A normal-distribution based reputation model

Rating systems are used by many websites, which allow customers to rate available items according to their own experience. Subsequently, reputation models are used to aggregate available ratings in order to generate reputation scores for items. A problem with current reputation models is that they provide solutions to enhance accuracy of sparse datasets not thinking of their models performance over dense datasets. In this paper, we propose a novel reputation model to generate more accurate reputation scores for items using any dataset; whether it is dense or sparse. Our proposed model is described as a weighted average method, where the weights are generated using the normal distribution. Experiments show promising results for the proposed model over state-of-the-art ones on sparse and dense datasets.

A reputation-enhanced recommender system

Reputation systems are employed to provide users with advice on the quality of items on the Web, based on the aggregated value of user-based ratings. Recommender systems are used online to suggest items to users according to the users, expressed preferences. Yet, recommender systems will endorse an item regardless of its reputation value. In this paper, we report the incorporation of reputation models into recommender systems to enhance the accuracy of recommendations. The proposed method separates the implementation of recommender and reputation systems for generality. Our experiment showed that the proposed method could enhance the accuracy of existing recommender systems.