User-generated content behaviour of the dissatisfied service customer

Presi, Caterina; Saridakis, Charalampos; Hartmans, Susanna

doi:10.1108/ejm-07-2012-0400

Cited by 74 publications

(56 citation statements)

References 76 publications

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“…So far, research studies in this field has mostly focused on UGC (Christodoulides et al, 2012;Presi et al, 2014;Smith et al, 2012), while some studies compare UGC and MGC as a focal point (Goh et al, 2013;Lim et al, 2012) (see Table 1). However, notwithstanding these few studies, MGC has not yet been separately examined in detail.…”

Section: Ewom and Mgcmentioning

confidence: 99%

Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Alboqami

Al‐Karaghouli

Baeshen

et al. 2015

IJIMA

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Marketers desire to utilise electronic word of mouth (eWOM) marketing on social media sites. However, not all online content generated by marketers has the same effect on consumers; some of them are effective while others are not. This paper aims to examine different characteristics of marketer-generated content (MGC) that of which one lead users to eWOM. Twitter was chosen as one of the leading social media sites and a content analysis approach was employed to identify the common characteristics of retweeted and favourited tweets. 2,780 tweets from six companies (Booking, Hostelworld, Hotels, Lastminute, Laterooms and Priceline) operating in the tourism sector are analysed. Results indicate that the posts which contain pictures, hyperlinks, product or service information, direct answers to customers and brand centrality are more likely to be retweeted and favourited by users. The findings present the main eWOM drivers for MGC in social media.Keywords: electronic word of mouth; eWOM; social media; content analysis; marketer-generated content; MGC; Twitter; characteristics of tweets.Reference to this paper should be made as follows: Alboqami, H., Al-Karaghouli, W., Baeshen, Y., Erkan, I., Evans, C. and Ghoneim, A. (2015) 'Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on Twitter ', Int. J. Internet Marketing and Advertising, Vol. 9, No. 4, Biographical notes: Hassan Alboqami is a PhD student in the Business School at Brunel University in London since 2013. He obtains his Master degree at Hertfordshire University in the UK 2011. In his PhD, the research interests are electronic word of mouth (eWOM), social media and customer relationship management. Chris Evans is a Senior Lecturer in the Brunel Business School at Brunel University. His research interests include interactivity, Web 2.0 and social media, multimedia learning technology, and the adoption of innovative learning technologies. He is chair of the annual eLearning 2.0 international conference on social media for learning. Electronic word of mouth in social mediaAhmad Ghoneim is a Lecturer of eBusiness and Information Systems at Brunel Business School. He is a member of the (ISEing) group -a research centre of excellence that supports a number of (EPSRC) funded networks and projects. Ahmad is the Programme Chair for the annual EMCIS conference and is on the editorial board of the TGPPP journal. He also co-edited special issues for IJCEC and EJIS journals.All authors contributed equally and have been listed alphabetically.

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Section: Ewom and Mgcmentioning

confidence: 99%

Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Alboqami

Al‐Karaghouli

Baeshen

et al. 2015

IJIMA

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“…By analyzing UGC, companies can gain valuable information about why customers like or dislike a company's products and services (Li and Li, 2013). Opinions published on social media can represent authentic information voiced directly by consumers (Presi et al, 2014), as they tend to be produced without external pressure (Canhoto and Padmanabhan, 2015). Therefore, by studying UGC, different results can be obtained more readily than with traditional survey research.…”

Section: Introductionmentioning

confidence: 99%

“…For example, customers express their dissatisfaction on the internet to other users (Presi et al, 2014), providing an opportunity for companies to stay alert to customer experiences and to manage their reputations (Salminen and Degbey, 2015). Naturally, there are also shortcomings in relying exclusively on social media comments, as there are a variety of issues that raise questions about their reliability and their variety for use within the domain of online corporate reputation.…”

Section: Introductionmentioning

confidence: 99%

Classifying online corporate reputation with machine learning: a study in the banking domain

Rantanen

Salminen

Ginter

et al. 2019

INTR

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Purpose User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations. Design/methodology/approach The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data. Findings After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation. Practical implications For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN. Originality/value This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.

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“…Customers even spread negative word-of-mouth to others, especially the era of information technology which enables the spreading of negative word-of-mouth over the internet. Customers who are not satisfied remain to have a negative attitude towards any service recovery strategies by companies (35). It can also decrease the desire of customers to use the services of these companies (36, 37) in (35).…”

Section: Customer Satisfactionmentioning

confidence: 99%

“…Customers who are not satisfied remain to have a negative attitude towards any service recovery strategies by companies (35). It can also decrease the desire of customers to use the services of these companies (36, 37) in (35).…”

Section: Customer Satisfactionmentioning

confidence: 99%

Patients Satisfaction Based on Corporate Entrepreneurship

Ratnasaria¹,

Prasetyo²,

Kurniawati³

et al. 2018

IJET

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This research aim to determine what factors are included in the service excellence based on corporate entrepreneurship, whether these factors affect the patient satisfaction, and which factors that influencing dominantly to patient satisfaction. The research design of this study is exploratory quantitative approach, which is conducted in two stages. The first step, the research conducted by open-ended question to explore the perception of the 40 patients about the service excellence of the hospital, so it can be known the factors which include in service excellence based on corporate enterpreneurship can be developed in the improvement of health services at the Dr. Soetomo Hospital after the implementation of BPJS, then proceed with a structure close-ended question to 400 patient of Health-BPJS. The sampling technique was by purposive non-random sampling. The result of this study are, first, factors affecting patient satisfaction of Health-BPJS participant after get treatment at the government hospital (Dr. Soetomo Hospital and Hajj Public Hospital) formed by six factors. Factors that formed was named by the reliability factor, empathy factor, the factor means of support, administrative factors, service factors, and factors of hospital hygiene. Second, indicators with the highest loading values obtained from the rotation calculation factors are indicators of X13 on the reliability factor is the health services infrastructure supported by sophisticated technology.

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User-generated content behaviour of the dissatisfied service customer

Cited by 74 publications

References 76 publications

Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Classifying online corporate reputation with machine learning: a study in the banking domain

Patients Satisfaction Based on Corporate Entrepreneurship

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