Effects of Medical Disputes on Internet Communications of Negative Emotions           and Negative Online Word-of-Mouth

Lee, Yi-Chih; Wu, Wei-Li

doi:10.2466/21.pr0.117c13z1

Cited by 12 publications

(5 citation statements)

References 63 publications

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“…This research also shows that not only is there a relationship between the different intangible variables of the proposed model, but also that their polarity is also important. Specifically, and despite the fact that some authors state that unfavourable emotions and experiences usually generate recommendations that are also unfavourable towards a brand ( Jiang et al, 2016;Lee and Wu, 2015;Verhagen et al, 2013), this study does not confirm it. In fact, the findings show that negative experiences and emotions can generate neutral and even favourable recommendations from analysts.…”

contrasting

confidence: 88%

“…Reputational intelligence recommending the purchase or sale of the bank's shares. In this sense, la mainstream opinion states that society's unfavourable emotions and experience generate unfavourable social recommendations towards a brand ( Jiang et al, 2016;Lee and Wu, 2015;Verhagen et al, 2013). However, some authors (Bollen et al, 2011;Gabbioneta et al, 2011) have considered that these unfavourable social emotions and experiences do not always generate unfavourable recommendations among certain influencers in the business, such as the financial analysts.…”

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confidence: 99%

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Reputational intelligence: innovating brand management through social media data

Casado-Molina

Ramos

Rojas-de-Gracia

et al. 2019

IMDS

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Purpose Companies are currently facing the challenge of understanding how their business is affected by the large volume of opinions continually generated by their stakeholders in social media regarding their intangible assets (experiences, emotions and attitudes). With this in mind, the purpose of this paper is to present an innovative management model, named E2AB, to measure and analyse reputational intangibles from digital ecosystems and their impacts on tangible assets. Design/methodology/approach The methodology applied was big data and business intelligence techniques. These methods were used in the computing process to obtain daily data from every asset guarantees that the model is validated with robust data. This model has been corroborated using data from the banking sector, specifically 402,383 net data inputs from the digital ecosystems. Findings This study illustrates the existence of a holistic influence of intangible assets over tangible assets. The findings demonstrate complex relationships between tangible and intangible assets, determined not only by the type of variable but also by its valence and intensity. Practical implications These findings may help chief communication officers and general managers a better understanding of how intangible assets extracted from online users’ opinions are related to their organisation’s tangible assets plus a chance to find out about their impact and how to manage them for a practical and agile decision making in real time. Originality/value It is a pioneering work in establishing a model, which demonstrates transversal and holistic relationships between relational intangible and tangible assets of firms from digital ecosystems, using business intelligence techniques.

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confidence: 88%

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confidence: 99%

Reputational intelligence: innovating brand management through social media data

Casado-Molina

Ramos

Rojas-de-Gracia

et al. 2019

IMDS

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“…Gheorghe and Liao (2012) examined the content of messages in an online gynecological community forum and emphasize that negative eWOM postings have a stronger impact linguistically than positive postings. Reasons for posting negative messages are often negative emotions like frustration, anger, uncertainty, disappointment, and sadness (Gheorghe & Liao, 2012;Lee & Wu, 2015). Venting, searching for advice, helping the receiver, and seeking revenge are identified by Lee and Wu (2015) as negative eWOM measures.…”

Section: Impact Of Wommentioning

confidence: 99%

“…Reasons for posting negative messages are often negative emotions like frustration, anger, uncertainty, disappointment, and sadness (Gheorghe & Liao, 2012;Lee & Wu, 2015). Venting, searching for advice, helping the receiver, and seeking revenge are identified by Lee and Wu (2015) as negative eWOM measures. Argan (2012) point out that three WOM dimensions tie strength and relationship, health knowledge and experience, and similarity and preference awareness directly and indirectly influence the satisfaction, encouragement, and discouragement of patients when selecting a physician.…”

Section: Impact Of Wommentioning

confidence: 99%

The current state of research of word-of-mouth in the health care sector

Pauli

Martin

Greiling

2022

Int Rev Public Nonprofit Mark

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Health information plays a significant role in the health behavior of individuals. Word-of-mouth (WOM) is essential in this context. In recent years, new forms of online communication have greatly expanded the possibilities for seeking information and, in consequence, significantly changed communication behavior. Similarly, the doctor-patient relationship has gradually evolved and the traditional asymmetry of medical knowledge is increasingly being corrected as today’s health care consumers are becoming more well-informed. A key source of information is either in-person or online WOM. A research gap exists in terms of analyzing the current state of research of WOM in health care. Although various studies highlight the influence of WOM on health behavior, to the best of our knowledge there exists no systematic literature review that summarizes the current state of research on WOM in health care. Therefore, this paper presents a comprehensive systematic literature review on WOM in health care. The literature review investigates existing WOM studies in the health care sector based on a systematic search for articles in a twenty-year timeframe from January 2000 to December 2019. The resulting total of 34 articles constitutes the basis of this paper. These studies are analyzed using a model of WOM in health care and – based on the theory of cognitive dissonance, the theory of the strength of weak ties, and the theory of perceived risk – clustered into the creation, spread, and impact of WOM. The investigated studies emphasize the importance of the staff in the service process. Furthermore, negative reviews have a stronger impact than positive ones, with service quality representing the main reason for negative WOM. In addition, the importance of electronic word-of-mouth (eWOM) is underlined, as online reviews are gaining popularity for patient decision-making processes. Although some studies have addressed WOM in health care, research gaps remain. For example, there are few studies on eWOM and some medical disciplines in private practice are neglected in WOM research. By systematically presenting and analyzing the literature on WOM in health care, this paper represents an important starting point for future research and also provides insights into the role of WOM in health care practice.

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“…After the test survey, the questions were modified, and the evaluation scale was reduced to a 5-point level. Though the authors studied the public services provided by the state institutions, the research is independent of the observed phenomenon (Lee, Wu, 2015).…”

Section: Entrepreneurship and Sustainability Issuesmentioning

confidence: 99%

Quality assessment of public services in Latvia

Katelo¹,

Kokina²,

Raščevskis³

2022

JESI

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At present, the service sector receives growing attention taking into account its role in the socio-economic development of the society. Public services produced to meet the demands of the population occupy a special place in this sphere. Improving the quality of public services is one of the essential goals in improving the functioning of public administration in Latvia and globally. The research aimed to assess the customer service quality of administrative services provided by Latvia's public administration institutions, performing the factor and cluster analysis of the collected data. Research base: Public administration institutions (6) and their branches (17) in Latvia. The research participants are two hundred ninety-two occasionally selected customers of public administration institutions who filled in SERVQUAL questionnaires before and after their visit to an institution. The sample of customers is occasional and administratively territorial, observing the proportional representation of planning regions. The service quality assessment modelthe SERVQUAL instrument (Parasuraman et al., 1988), was used for the data collection. The analysis of the research data factors and clusters was carried out. Service provision quality in all quality dimensions has been given a negative evaluation. The economic efficiency of the largest state institutions understudy is low, and so is the quality of provided services as evaluated by their customers.

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Effects of Medical Disputes on Internet Communications of Negative Emotions and Negative Online Word-of-Mouth

Cited by 12 publications

References 63 publications

Reputational intelligence: innovating brand management through social media data

Reputational intelligence: innovating brand management through social media data

The current state of research of word-of-mouth in the health care sector

Quality assessment of public services in Latvia

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