scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

Lies, brands and social media

2019 ◽  
Vol 22 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Tracy Tuten ◽  
Victor Perotti
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Coverage ◽  
Future Research ◽  
False Information ◽  
Content Type ◽  
News Organizations ◽  
Media Sentiment ◽  
The Impact ◽  
False Claim

Purpose The purpose of this study is to illustrate the influence of media coverage and sentiment about brands on user-generated content amplification and opinions expressed in social media. Design/methodology/approach This study used a mixed-method approach, using a brand situation as a case example, including sentiment analysis of social media conversations and sentiment analysis of media coverage. This study tracks the diffusion of a false claim about the brand via online media coverage, subsequent spreading of the false claim via social media and the resulting impact on sentiment toward the brand. Findings The findings illustrate the influence of digital mass communication sources on the subsequent spread of information about a brand via social media channels and the impact of the social spread of false claims on brand sentiment. This study illustrates the value of social media listening and sentiment analysis for brands as an ongoing business practice. Research limitations/implications While it has long been known that media coverage is in part subsequently diffused through individual sharing, this study reveals the potential for media sentiment to influence sentiment toward a brand. It also illustrates the potential harm brands face when false information is spread via media coverage and subsequently through social media posts and conversations. How brands can most effectively correct false brand beliefs and recover from negative sentiment related to false claims is an area for future research. Practical implications This study suggests that brands are wise to use sentiment analysis as part of their evaluation of earned media coverage from news organizations and to use social listening as an alert system and sentiment analysis to assess impact on attitudes toward the brand. These steps should become part of a brand’s social media management process. Social implications Media are presumed to be impartial reporters of news and information. However, this study illustrated that the sentiment expressed in media coverage about a brand can be measured and diffused beyond the publications’ initial reach via social media. Advertising positioned as news must be labeled as “advertorial” to ensure that those exposed to the message understand that the message is not impartial. News organizations may inadvertently publish false claims and relay information with sentiment that is then carried via social media along with the information itself. Negative information about a brand may be more sensational and, thus, prone to social sharing, no matter how well the findings are researched or sourced. Originality/value The value of the study is its illustration of how false information and media sentiment spread via social media can ultimately affect consumer sentiment and attitude toward the brand. This study also explains the research process for social scraping and sentiment analysis.

scholarly journals A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

2020 ◽  
Vol 4 (4) ◽  
pp. 33
Author(s):  
Toni Pano ◽  
Rasha Kashef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Prediction Models ◽  
Financial Sector ◽  
Research Gap ◽  
Text Preprocessing ◽  
The Impact

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

Exploring user sentiment towards sponsorship and ambush marketing

2019 ◽  
Vol 20 (4) ◽  
pp. 583-602 ◽  
Author(s):  
Nick Burton
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Value Added ◽  
Statistical Modelling ◽  
Consumer Attitudes ◽  
Content Type ◽  
Event Marketing ◽  
Ambush Marketing ◽  
Ethics Research ◽  
Twitter Users

Purpose The purpose of this paper is to explore consumer attitudes towards ambush marketing and official event sponsorship through the lens of sentiment analysis, and to examine social media users' ethical responses to digital event marketing campaigns during the 2018 FIFA World Cup. Design/methodology/approach The study employed a sentiment analysis, examining Twitter users’ utilization of sponsor and non-sponsor promotional hashtags. Statistical modelling programme R was used to access Twitter’s API, enabling the analysis and coding of user tweets pertaining to six marketing campaigns. The valence of each tweet – as well as the apparent user motivation underlying each post – was assessed, providing insight into Twitter users’ ethical impressions of sponsor and ambush marketer activities on social media and online engagement with social media marketing. Findings The study’s findings indicate that consumer attitudes towards ambush marketing may be significantly more positive than previously thought. Users’ attitudes towards ambush marketing appear significantly more positive than previously assumed, as users of social media emerged as highly responsive to creative and value-added non-sponsor campaigns. Originality/value The findings affirm that sentiment analysis may afford scholars and practitioners a viable means of assessing consumer attitudes towards social marketing activations, dependent upon campaign objectives and strategy. The study provides a new and invaluable context to consumer affect and ambush ethics research, advancing sponsorship and ambush marketing delivery and social sponsorship analytical practice.

The impact of involvement on engagement with brand posts

2019 ◽  
Vol 13 (3) ◽  
pp. 277-301
Author(s):  
Ana Margarida Barreto ◽  
Diogo Ramalho
Keyword(s):  
Social Media ◽  
Consumer Involvement ◽  
Future Research ◽  
High Involvement ◽  
Content Type ◽  
Level Of Involvement ◽  
Goods And Services ◽  
Business To Consumer ◽  
Post Hoc ◽  
The Impact

Purpose This paper aims to look at the effects of different levels of involvement (high and low) on social media (Facebook) users' engagement (likes, shares and comments) with different types and formats of brand content. Design/methodology/approach The authors analyzed user reactions to 1,156 Facebook posts from eight business-to-consumer brands (goods and services). Based on a post hoc test, four product/services were identified as belonging to the group of high-involvement and the other four as low involvement. Findings The data suggest that, when involvement is low, users in general engage more with brand posts regardless their format (text, image and post) or type (hedonic and informative), or even the interaction of both. Moreover, low involvement leads users prefer to comment on brand content, whereas higher involvement is associated with to sharing it. Exceptions were observed for images (both hedonic and informative) and for hedonic image and video in both low and high involvement users. Research limitations/implications The goal was not to measure users’ attention to each type of post. Moreover, the authors did not have access to information regarding which devices were used to access the online content and whether that aspect might have an impact on users’ reactions. Neither do they claim that engagement necessarily reflects positive reactions, as any content analysis of users’ reactions was beyond the scope of this project. Practical implications These findings are expected to help brand managers and social media strategists to better select content based on their marketing goals, as well as to provide a potential explanation for the success of campaigns. Originality/value As far as we are aware, no previous study has attempted to observe the mediated effect of consumer involvement on brand posts considering their type and format. We also believe that this is the first observation of how behavior differentiates according to the target audience’s level of involvement. This paper also proposes a convenient framework for categorizing social network sites content. Suggestions for future research are made at the end.

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

2019 ◽  
Vol 30 (1) ◽  
pp. 45-66 ◽  
Author(s):  
Anette Rantanen ◽  
Joni Salminen ◽  
Filip Ginter ◽  
Bernard J. Jansen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Media ◽  
Corporate Reputation ◽  
Real Data ◽  
High Volume ◽  
Training Data ◽  
Real World Data ◽  
Content Type ◽  
Classification Framework

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.

Wearable IoT intelligent recommender framework for a smarter healthcare approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Iot Devices ◽  
The Right ◽  
Media Sentiment

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Nexus between risk disclosure and corporate reputation: a longitudinal approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nischay Arora ◽  
Ridhima Saggar ◽  
Balwinder Singh
Keyword(s):  
Corporate Reputation ◽  
Market Value ◽  
Risk Information ◽  
Future Research ◽  
Risk Disclosure ◽  
Total Risk ◽  
Content Type ◽  
Book Value ◽  
Market Capitalization ◽  
The Impact

PurposeThe study aims to explore the unexplored domain by examining the impact of risk disclosure on corporate reputation in an emerging economy, like India, characterized by huge information asymmetry and uncertainty.Design/methodology/approachIn total two measures of corporate reputation, i.e. market capitalization and excess of market value over book value have been deployed to measure reputation. Automated content analysis has been executed to measure the extent of total risk disclosure. The empirical analysis is premised on a sample of S&P BSE-100 index spanning over the period of ten years from 2009–2010 to 2018–2019; which eventually gets reduced to 58 nonfinancial firms. In order to unearth the risk–reputation relationship, a panel regression technique has been employed.FindingsThe main findings unmask that corporate risk disclosure has a positive bearing on corporate reputation. Substantiating legitimacy theory, its alternative measures like market capitalization and excess of market value over book value divulged to positively influence corporate reputation.Research limitations/implicationsThe study has certain limitations: since there is no standard method of measuring reputation, the results may vary subject to the changes in proxies of corporate reputation. The study also analyzed S&P BSE 100 index in India, and future research needs to approach a larger sample and in other emerging economies to fill up enough empirical evidence in this domain.Practical implicationsThe findings provide insight into the managers on making higher divulgence of material risk information for augmenting corporate reputation. In other words, it indirectly propels the firm to exhibit higher risk information for building reputational capital. From the investor's standpoint, they should admire such firms which dispel more risk information and should have positive outlook toward them, which in turn prompts them to disclose more risks.Originality/valueThis study is unique as it is the first longitudinal study examining the impact of risk disclosure on corporate reputation in Indian settings. It, thus, assists in furthering the risk disclosure literature where there is hardly any study that comprehensively looks into risk–reputation liaison among Indian nonfinancial companies.

Fashion bloggers' discourse on brands under corporate crisis: a netnographic research in Portugal

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Beatriz Casais ◽  
Lucilene Ribeiro Gomes
Keyword(s):  
Social Media ◽  
Crisis Management ◽  
Crisis Communication ◽  
Word Of Mouth ◽  
Future Research ◽  
Content Type ◽  
Crisis Events ◽  
Brand Crisis ◽  
Corporate Crisis ◽  
Crisis Situations

PurposeThis paper focuses on the analysis of fashion blog activity regarding brands under corporate crisis situations and discusses how these opinion leaders may be agents of corporate crisis management.Design/methodology/approachThe authors analyzed four influential Portuguese fashion blogs regarding eight fashion brands that had experienced a corporate crisis situation. In total, five of the selected brands were mentioned in 2.846 posts of blog content, whose discourse was deeply analyzed.FindingsThe absence of express reference to brand crisis suggests that fashion bloggers tend to ignore these crisis events or divert the readers' attention to the brands' more positive aspects. This result opens the discussion whether fashion bloggers downplay corporate crisis in brand equity or whether it expresses strategies of brand crisis communication through digital influencers.Originality/valueThough social media may be a source of negative word-of-mouth, social media influencers have been considered important partners of corporate crisis communication in particularly challenging times. Many studies have focused on the role of social media influencers in crisis management, but there was a dearth of research on the specific case of blogs. This study contributes to the understanding of fashion bloggers as agents of brand communication, particularly regarding crisis management and their role on brand activation and positive electronic word-of-mouth, even under crisis situations. This contribution paves the way for future research on whether this is a spontaneous phenomenon or the reflection of possible partnerships between companies and fashion bloggers for the management of corporate crisis situations in the context of fashion brands.

Understanding the selfie phenomenon: current insights and future research directions

2016 ◽  
Vol 50 (9/10) ◽  
pp. 1773-1788 ◽  
Author(s):  
Weng Marc Lim
Keyword(s):  
Social Media ◽  
Future Research ◽  
Research Directions ◽  
Content Type ◽  
Marketing Performance ◽  
The Social ◽  
Future Research Directions ◽  
Marketing Tool ◽  
The Impact

Purpose This paper aims to define the conceptual boundary of the selfie and to discuss the role of the selfie in the social media marketplace. Design/methodology/approach This paper extensively reviews and draws themes from the extant literature on consumer identities in the social media marketplace to explain the selfie phenomenon and to identify potentially fruitful directions for further research. Findings Current insights into the selfie phenomenon can be understood from socio-historical, technological, social media, marketing and ethical perspectives. Research limitations/implications Despite the limitations of a general review (e.g. absence of empirical data and analysis), this paper identifies multiple avenues to extend existing lines of inquiry on the selfie phenomenon. Thus, this paper should encourage further research on the topic in the academic and scientific community. Practical implications The selfie can be used as a marketing tool to improve marketing performance and accomplish marketing-related goals. Originality/value This paper sheds light on how marketing academics and practitioners can better understand the impact of the selfie in the social media marketplace.

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