Building Corporate Reputation on the Internet: Is the Claimed Sales-Ranking Trustworthy?

Wang, Youquan; Fang, Changjian; Shen, Dongqin; Wu, Zhang; Cao, Jie

doi:10.1109/cbd.2018.00038

Cited by 2 publications

(3 citation statements)

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“…The proliferation of organizational reputation ranking assessments in recent years has driven organizations' interests to participate and win such evaluations (Barnett and Leih, 2018;Khan and Digout, 2018). However, concerns have been raised on the reliability of these mechanisms, especially pertaining to evaluation criteria and choice of respondents (Barnett and Leih, 2018;Wang et al, 2018). An examination of organizational reputation from within the organization would ideally address these concerns.…”

Section: Introductionmentioning

confidence: 99%

Antecedents of internal reputation and its consequences on organizational outcomes: a systematic review

Lee,

Abdullah

2024

IJOTB

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PurposeOrganizational reputation has come to the forefront amidst today’s increasingly competitive business environment. While the perspectives of external stakeholders continue to generate research attention among reputation scholars, perceptions of internal stakeholders like employees deserve similar or more focus due to their ability to sway external organizational reputation. Thoroughly understanding organizational variables that precede internal reputation perceptions among employees and the ensuing consequences enables effective reputation management measures. This study aims to develop a comprehensive framework that outlines the antecedents and consequences of the internal reputation concept.Design/methodology/approachThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 review protocol was applied. Around 24 journal articles were drawn from the Web of Science and Scopus databases and analyzed using qualitative content analysis.FindingsThe review revealed 31 constructs related to the internal reputation concept, which were categorized into seven main themes, and a comprehensive framework was developed. Future research recommendations include the need to expand the study’s parameters, adopt a different research method and target varied contexts to achieve a more exhaustive systematic literature review on internal reputation.Research limitations/implicationsThis study attempts to contribute to the understanding of the internal reputation concept, theories and reputation management practices through its findings.Originality/valueThis paper provides the first known systematic literature review of the internal reputation concept, which could provide practical guidance to professionals in internal reputation management and academic guidance for future research in internal reputation.

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Section: Introductionmentioning

confidence: 99%

Antecedents of internal reputation and its consequences on organizational outcomes: a systematic review

Lee,

Abdullah

2024

IJOTB

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“…In recent work, to verify the claimed sales‐ranking perplexing the government, we have proposed a Maximum Marginal Relevance (MMR)‐based index method to rank the trustworthy of every company . Take the claimed sales‐ranking of each enterprise in one or more market segments as an example, we can construct the positive, neutral, and negative evidences to rank the claimed sales‐ranking for a hundred companies in 2016.…”

Section: Introductionmentioning

confidence: 99%

“…In recent work, to verify the claimed sales-ranking perplexing the government, we have proposed a Maximum Marginal Relevance (MMR)-based index method to rank the trustworthy of every company. 11 Take the claimed sales-ranking of each enterprise in one or more market segments as an example, we can construct the positive, neutral, and negative evidences to rank the claimed sales-ranking for a hundred companies in 2016. By analyzing of public opinion and financial data from a hundred companies in China, we can demonstrate most of companies' claimed sales-ranking are trustworthy, while a small amount of companies with many negative news may not blend their reliable claim.…”

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confidence: 99%

Verifying the claimed sale‐ranking trustworthy: A maximum marginal relevance‐based ranking method

Wang

Fang

Shen

et al. 2019

Concurrency and Computation

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Summary Various online contents on Internet platforms or search engines are related to the corporate reputation. Facing the huge amount of online contents, we need a mining method that can automatically extract and analyze a large number of network‐related information and obtain the real reliability of aspect for the content claimed by companies. In this paper, we propose to generate a ranking model to verify whether the sales‐rankings claimed by companies are trustworthy. The key idea is that the company that has higher confidence score should be supported by the online media. We use a unique data set of public opinion data related with a specific company, which we supplement with data from various online news platform and retrieval webpages using a distributed and generic Web crawler. Meanwhile, basic information and open financial data of companies are also collected for auxiliary analysis. We present a Maximal Marginal Relevance‐based ranking model to compute the confidence score of each company, taking into consideration the two technologies of word embedding and KL‐Divergence to filter the irrelevant documents. Extensive experiments show that the proposed method outperforms the state‐of‐the‐art MMR‐based method, and we showcase three representative cases about the corporate reputation built by us that gives positive, neutral, and negative support respectively to the sales‐ranking claim of companies.

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Building Corporate Reputation on the Internet: Is the Claimed Sales-Ranking Trustworthy?

Cited by 2 publications

References 22 publications

Antecedents of internal reputation and its consequences on organizational outcomes: a systematic review

Antecedents of internal reputation and its consequences on organizational outcomes: a systematic review

Verifying the claimed sale‐ranking trustworthy: A maximum marginal relevance‐based ranking method

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