Reputation Assessment Research Articles

Advancing reputation measurement: evolving toward improved quantitative assessments

Purpose The purpose of this paper is to provide evidence on the information-gathering deficits in contemporary reputation measurement that are rooted in sampling and to obtain supporting information from respondents from various stakeholder groups. Design/methodology/approach In regard to social emergence theory, the authors examine the common practice of aggregating reputational judgments from randomly sampled respondents without considering their knowledge domains. A stereotyping experiment conducted in three countries provides evidence that lower-level reputations might vary, whereas higher-level reputations resulting from the social emergence process do not vary. Findings The findings demonstrate that researchers should consider respondents’ heterogeneity in regard to reputation measurement. Stakeholder judgments divergent from their domains of expertise often add noise, instead of informative answers, to the reputational categories. Research limitations/implications The social emergence process, in addition to the roles of the stakeholders, their interaction structures and the timing of their communication, needs to be incorporated into an improved reputation measurement method. Practical implications Not all information from the same respondent should be considered when computing a final reputation score. Respondents’ heterogeneity is revealed to be fundamental for reputational assessments. Originality/value This study is original in its examination of the validity of reputation assessment being restricted to lower-level descriptions of the supervenience relation. Building upon the results of the experiment conducted in three national framings, this paper suggests ways to improve reputation measurement.

Crowdsourced Traffic Event Detection and Source Reputation Assessment Using Smart Contracts.

Real-time data about various traffic events and conditions—offences, accidents, dangerous driving, or dangerous road conditions—is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.

The Management of Reputational Risks in Banks: Findings From Germany and Switzerland

This article identifies reputation-risk-relevant factors for banks, and the focus will be placed on the development of an indicator-based model for the assessment of reputation. Requirements and insights are based on a survey of credit institutions in Germany and Switzerland, which have been predominantly affected during the financial crisis by aptly nascent risks and which are thereby also partially affected even today. Reputation level can be considered as a temporally dynamical phenomenon which predominantly develops depending on the changes in the reputation drivers and expectations of the groups of stakeholders. This control parameter can be determined with the aid of Reputation Index Points (RIP). Efficient reputation risk management can, in the future, help prevent negative spillover effects from banks which face difficulties from the society or the taxpayers.

Blockchain-based reputation management for custom manufacturing service in the peer-to-peer networking environment

Blockchain enables users to be autonomous without the need of trust by using a digital ledger of decentralized consensus. Blockchain-based IoT allows service providers to transfer the security and maintenance responsibility to self-maintaining customers. Adopting peer-to-peer (P2P) networking and computing for more than billions of transactions can reduce the costs arising from the installation and maintenance of centralized systems. Small manufacturers providing industrial IoT (IIoT) services can participate more actively in blockchain-based IIoT applications with three-dimensional printing and digital manufacturing technologies, but the measure to maintain privacy on a blockchain is not robust. Here, we propose a P2P networking-based custom manufacturing service, which is an order-driven trading service between a manufacturer and a customer in the blockchain-based IIoT architecture. The proposed system consists of reputation management and service architecture. We propose a new reputation assessment method customized to increase the reliability and accuracy of industrial manufacturing systems. We also propose a manufacturer rating classification to guide the customers’ decision making in a reliable manner and a malicious evaluator identification to exclude feedbacks from malicious evaluators. The proposed service architecture is composed of trustless P2P protocols designed for preserving privacy and providing non-repudiation. We used a cryptographic algorithm for ensuring transaction privacy and the digital signing of blockchain for non-repudiation. We also analyzed the proposed service architecture and the possible attack scenarios to verify the security requirements. We verified that the reputation management system was influenced by each feedback dynamically and guided the customer’s present decision making with reliable and classified manners, by simulating reputation classification and malicious evaluator identification. Further, we have summarized the originality and the characteristics of the proposed approach by comparing closely related studies and concluded with a future research guide.

Clustering Babyshop at Marketplace X with Cluster Ensemble based on Squeezer Algorithm

Marketplace is one of the most popular digital business in Indonesia. One of the category that grow in Marketplace X is shops that sell baby equipment or better known as babyshop. In order to provide the best service and keep the credibility of babyshop specialty shops, important to do qualitry monitoring on of them through clustering. Clustering based on store reputation assessment indicators consisting of variables that are categorical and numerical in scale. This study aims to classify babyshop on Marketplace X based on the characteristics of the store using cluster analysis with cluster ensemble based mixed data clustering (CEBMDC) based on the weigthed squeezer algorithm. This study use the stream data from 218 babyshop at Marketplace X which consists of service factors, reputation level, type and location of the babyshop. The optimal cluster results was into three clusters in which cluster one consists of 21% babyshop, cluster two 48% babyshop, and 31% babyshop at cluster three. The first cluster is a cluster with the tendency of the babyshop to be classified as good, the cluster two tend to have a normal (neutral) reputation, while members of cluster three has a for poor reputation.

온라인 뉴스 데이터를 활용한 AR-KNU 감성사전 기반의 대학 평판도 평가

지금까지의 대학 평판도 평가는 설문 조사를 통한 평판도 분석의 한계, 평판도 분석에 활용된 공공 데이터와 텍스트 데이터의 기간적 불일치 그리고 심층적인 분석 결과의 부재라는 문제점을 지닌다. 이 논문에서는 감성 분석 기반의 새로운 대학 평판도 평가 알고리즘을 제안하고 기존 연구와의 비교 분석을 수행한다. 이러한 목표 달성을 위해 감성 사전을 구축하고 감성 문단 및 군집을 추출한다. 정제된 군집 생성을 위해 추출된 군집에 대한 군집화, 군집 정제 연산을 수행하고, 최종적인 대학 평판도 평가 결과를 생성한다. 이 연구의 결과는 구체적인 대학의 이슈 발견에 활용될 수 있으며, 정확도가 향상된 대학 평판도 평가의 새로운 알고리즘 개발에 기여할 수 있다. 또한 다양한 평판도 평가 분야에서 유의미한 기초 자료로 활용될 수 있다.

Problems of business reputation Accounting

The article deals with the problems arising when accounting for the business reputation of an organization as an intangible asset. Aspects of assessment of positive reputation in tax and accounting are revealed. The author’s position on accounting of business reputation of the organization in the international and Russian practice is given.

Improving the Safety and Quality of Building Materials Based on the Assessment of Experience and Business Reputation of the Enterprise

Improving the Safety and Quality of Building Materials Based on the Assessment of Experience and Business Reputation of the Enterprise

Source reputation assessment in an IoT-based vehicular traffic monitoring system

Vehicular traffic safety relies, to a great extent, on traffic control and traffic offence detection. While this is currently in the domain of governmental and road maintenance services, many alternatives, such as crowdsensing-based approaches have emerged. We present a traffic event reporting scheme, focusing on event detection and data source reputation mechanisms. Proposed scheme is evaluated on a set of representative scenarios. Proposal is critically discussed and is followed by guidelines for future work.

Reputational Capital as a Basis for the Economic Growth of Enterprises in the Hotel and Restaurant Business

Actuality. In scientific and practical terms, the need for a comprehensive study of theoretical and methodological problems of reputational capital has come to naught; an assessment of its impact on the strategy of growth and the formation of competitive advantages in the organizations of all spheres of activity, in particular, actualization of the problem for the hotel and restaurant industry sphere. Purpose and methods. The purpose of the article is to justify the further research of the business category of business reputation and provide evidence of the need for management in this asset as a factor in increasing the material value of enterprises in general and hotel and restaurant business in particular Methodological basis of the research were the following methods of scientific analysis: dialectical method, methods of interdisciplinary research, systemic method, morphological analysis method, methods of analysis and synthesis, logical generalization method. Results. The article deals with the economic content of the category “business reputation”, generalized the ratio of the category “business reputation” with the related ones – “image”, “brand”, “authority” of the organization, and distinguishes between them, the methodical approaches to the assessment of business reputation are analyzed, RM. Conclusions and discussion. The results of the conducted research allow asserting that today, for business success, the most valuable strategic resource and tool in competition is the business reputation, as a stakeholder assessment of compliance with expectations and the real situation. The scientific novelty of the obtained results is determined by the generalization of the best business experience of the formation and maintenance of positive business reputation; improvement of factor analysis of the impact on business performance taking into account changing environmental conditions. The results of scientific research can be directly used or implemented in one or another sphere of psychological, social, economic or financial practice in individual industries. With the fullness of the hotel and restaurant business with qualitative services of the same type, the most important were non-obvious factors (intangible assets of hotels and restaurants): business reputation, including managers and managers; brands, a system of effective communications and a quality strategy for cooperation with target audiences.

Identification of the theoretical aspects of enterprise reputation

Purpose – to identify the theoretical aspects of enterprise reputation.
 Design/Method/Research approach. Authors applied a structural-functional method in the course of systemic investigation and substantiation of the methodological toolset of enterprise reputation assessment and the method of logical generalization when analyzing the evolution of scientific views on the nature of the notion "reputation". The information base of this research is the monographic works and scientific publications on relevant subjects.
 Findings. Authors have substantiated the theoretical aspects of enterprise reputation, according to which the reputation of an enterprise is formed under the influence of both intangible and tangible factors. Approaches to defining reputation of an enterprise were systemized, with their new classification proposed, which distinguishes the immanent-functional, value, emotional (image), monitoring, market, and integrated approaches. Current methodological toolset of enterprise reputation assessment has been analyzed, and the scope of its application has been determined, as well as the main advantages and disadvantages. An algorithm for evaluating an enterprise reputation has been developed, in accordance with the proposed theoretical approach, a market share, and the totality of consumers values.
 Practical implications. Results of this study could form the basis for forming a policy of an enterprise concerning the activation of reputation management processes with the purpose of strategic development of the enterprise and in order to faster meet the expectations of its stakeholders, which would provide a synergistic effect.
 Originality/Value.Authors proposed to define the essence of the notion of an enterprise "reputation", which, in contrast to existing interpretations, focuses on the cognitive-contemplative characteristic of an enterprise, which is formed based on the results of comparing the totality of tangible, intangible, personal, and social values, inherent to its external and internal stakeholders; changing them in time and space indirectly affects positioning of the enterprise in the market as a result of change in the way its stakeholders perceive it.
 Research limitations/Future research. Results of this study should be laid at the basis of the implementation of the proposed algorithm for assessing reputation in the process of enterprise management.
 Paper type ‒ theoretical.

Explaining reputation assessments

Reputation is crucial to enabling human or software agents to select among alternative providers. Although several effective reputation assessment methods exist, they typically distil reputation into a numerical representation, with no accompanying explanation of the rationale behind the assessment. Such explanations would allow users or clients to make a richer assessment of providers, and tailor selection according to their preferences and current context. In this paper, we propose an approach to explain the rationale behind assessments from quantitative reputation models, by generating arguments that are combined to form explanations. Our approach adapts, extends and combines existing approaches for explaining decisions made using multi-attribute decision models in the context of reputation. We present example argument templates, and describe how to select their parameters using explanation algorithms. Our proposal was evaluated by means of a user study, which followed an existing protocol. Our results give evidence that although explanations present a subset of the information of trust scores, they are sufficient to equally evaluate providers recommended based on their trust score. Moreover, when explanation arguments reveal implicit model information, they are less persuasive than scores.

Reputation assessment mechanism for carpooling applications based on clustering user travel preferences

Abstract One way to ensure sustainable and environmental friendly mobility is the use of less vehicles for carrying more passengers, and carpooling is a means to achieve this goal. One major concern in carpooling services is related to trust, as carpooling users need to either share their vehicles, if they act as drivers, or travel with strangers if they act as passengers. One way to tackle trust concerns is the utilization of user reputation assessment mechanisms, whose objective is to provide ranking of users with respect to their behavior, based on feedback provided by other users. This paper presents a newly introduced reputation assessment mechanism for carpooling applications, which, in addition to feedback provided by other users, takes into account user travel preferences. Preliminary experimental results have shown that the proposed mechanism is robust against attacks by malicious users, on their attempt to jeopardize the trustworthiness of the system, as it preserves the real reputation scores of the users, when the penetration rate of malicious users increases.

A Reputation Assessment Approach Based on Fuzzy Mathematics Mobile Application Crowdsourced Testers

The anonymity and unsupervision of mobile application crowdsourced testing, makes the tester easy to slacken or cheat on test tasks, resulting in a drop in the quality of crowdsourced testing. To solve the problem, this paper proposes a method for assessing the reputation of a mobile application crowdsourced testers based on fuzzy mathematics. This method closely integrates crowdsourced testing features. By introducing a tester evaluation mechanism, building a reputation assessment model and studying the calculation and update of reputation in the crowdsourced testing, it achieves a reasonable assessment of the reputation of a crowdsourced tester and can effectively screen out high reputation testers, thus ensuring the quality of mobile application crowdsourced testing.

Edge Computing and Social Internet of Things for Large-Scale Smart Environments Development

Large-scale smart environments (LSEs) are open and dynamic systems typically extending over a wide area and including a huge number of interacting devices with a heterogeneous nature. Thus, during their deployment scalability and interoperability are key requirements to be definitely taken into account. To these, discovery and reputation assessment of services and objects have to be added, given that new devices and functionalities continuously join LSEs. In spite of the increasing interest in this topic, effective approaches to develop LSEs are still missing. This paper proposes an agent-based approach that leverages edge computing and Social Internet of Things paradigms in order to address the above mentioned issues. The effectiveness of such an approach is assessed through a sample case study involving a commercial road environment.

Reputation-Aware, Trajectory-Based Recruitment of Smart Vehicles for Public Sensing

With the abundant on-board resources in smart vehicles, they have become major candidates for providing ubiquitous services, including public sensing. One of the challenges facing such ubiquitous utilization is the recruitment of the participating vehicles. In this paper, we present the reputation-aware, trajectory-based recruitment (RTR) framework that handles recruitment of vehicles for public sensing. The framework considers the spatiotemporal availability of participants along with their reputation to select vehicles that achieve desired coverage of an area of interest within a budget cap. The framework consists of a reputation assessment scheme, a pricing model, and a selection scheme collaborating for a main recruitment objective; maximizing coverage with minimum cost. We propose greedy heuristic solutions targeting the selection problem in real-time. The RTR framework generalizes the basic selection problem to handle some practical scenarios, including departing vehicles and varying redundancy requirements. We also propose a reputation assessment scheme and a pricing model as parts of the framework. Extensive performance evaluation of the proposed framework is conducted and the evaluation shows that the proposed greedy heuristics are able to achieve results close to previously obtained optimal benchmarks under different scenarios, and that the framework succeeds in achieving high levels of coverage even when vehicles do not stick to their announced trajectories.

A comprehensive reputation assessment framework for volunteered geographic information in crowdsensing applications

Volunteered geographic information (VGI) is the result of activities where individuals, supported by enabling technologies, behave like physical sensors by harvesting and organizing georeferenced content, usually in their surroundings. Both researchers and organizations have recognized the value of VGI content, however this content is typically heterogeneous in quality and spatial coverage. As a consequence, in order for applications to benefit from it, its quality and reliability need to be assessed in advance. This may not be easy since, typically, it is unknown how the process of collecting and organizing the VGI content has been conducted and by whom. In the literature, various proposals focus on an indirect process of quality assessment based on reputation scores. Following this perspective, the present paper provides as main contributions: (i) a multi-layer architecture for VGI which supports a process of reputation evaluation; (ii) a new comprehensive model for computing reputation scores for both VGI data and contributors, based on direct and indirect evaluations expressed by users, and including the concept of data aging; (iii) a variety of experiments evaluating the accuracy of the model. Finally, the relevance of adopting this framework is discussed via an applicative scenario for recommending tourist itineraries.

Corporate reputation

PurposeThe purpose of this paper is to discuss the different approaches to the corporate reputation construct, in order to identify a comprehensive definition that can be used for measurement purposes, gaps identified by previous literature identified.Design/methodology/approachThis is a theoretical essay. The authors analyzed studies that involve the relationship between corporate reputation and organizational performance, and the attributes of national and international corporate reputation ratings.FindingsThe authors identified a more comprehensive definition for the reputation construct, and indicated courses for the construct’s measurement, by considering: the judgment by the stakeholders (internal, suppliers, clients and the financial market); periodical evaluations under different organizational perspectives; attention to theoretical assumptions, among other aspects.Research limitations/implicationsThe study is a theoretical paper that presents that the research field has many definitions that cannot be used interchangeably. It indicated how the reputation construct should be operationalized for measurement purposes. This study presented a reflection on the relationship between corporate reputation and performance, showing that it is not a settled topic in the academy.Practical implicationsThe study advances the understanding of the reputation construct measurement, considering the adopted definition and the discussion of the attributes of the main ratings on corporate reputation. The adoption of a measurement method that takes into account the definition used in this study and the features of the methodologies discussed will improve the corporate reputation assessment.Social implicationsLiterature indicates that a good corporate reputation can affect organizational performance and the inverse relationship is also true. As a social implication, it is extremely relevant to improve the understanding the definition and measurement methods of this construct.Originality/valueThis study discusses one of the most important intangible resources for organizations, contributing to the understanding of the difference between the market value and the book value of public companies. Besides it should be considered that there is one lack of a definition directly related to the measurement of the reputation construct in the literature, a gap in which this study contributes.

Reputation assessment: a review and unifying abstraction

AbstractTrust and reputation allow agents to make informed decisions about potential interactions. Trust in an agent is derived from direct experience with that agent, while reputation is determined by the experiences reported by other witness agents with potentially differing viewpoints. These experiences are typically aggregated in a trust and reputation model, of which there are several types that focus on different aspects. Such aspects include handling subjective perspectives of witnesses, dishonesty, or assessing the reputation of new agents. In this paper, we distil reputation systems into their fundamental aspects, discussing first how trust and reputation information is represented and second how it is disseminated among agents. Based on these discussions, a unifying abstraction is presented for trust and reputation systems, which is demonstrated by instantiating it with a broad range of reputation systems found in the literature. The abstraction is then instantiated to combine the range of capabilities of existing reputation systems in the Machine Learning Reputation System, which is evaluated using a marketplace simulation.

A dependable and lightweight trust proliferation approach for the collaborative IoT systems

Trust and reputation evaluation in the internet of things (IoT) constitutes nowadays a major challenge that is still attracting the research community to work on and investigate. The IoT with its widely heterogeneous objects requires collaborative computation to perform heavy cryptographic operations in the most constrained resource devices. Indeed, the effectiveness of the trust and reputation assessment plays an important role for selecting the best collaborators. In this paper, we propose a dependable and lightweight trust proliferation approach for the collaborative IoT systems. By recording the malicious tasks which could be performed during the service execution, our approach introduces a new derivation of direct trust. Furthermore, it combines dynamically the direct and indirect trust evaluation to ensure more trustworthiness and reliability in the selection of the best collaborator objects. Through the simulations, we show that our trust derivation approach achieves high accuracy in the selection of efficient and confident collaborators.