scholarly journals The Impact of Artificial Intelligence on Branding

2021 ◽  
Vol 29 (4) ◽  
pp. 221-246
Author(s):  
Varsha P. S. ◽  
Shahriar Akter ◽  
Amit Kumar ◽  
Saikat Gochhait ◽  
Basanna Patagundi
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Citation Analysis ◽  
Brand Choice ◽  
Media Analysis ◽  
Social Media Analytics ◽  
Information Cascades ◽  
Luxury Brands ◽  
Value Propositions ◽  
The Impact

Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using co-occurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis—Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-time Knowledge-Based Systems—and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.

Public relations in the age of data: corporate perspectives on social media analytics (SMA)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kathy R. Fitzpatrick ◽  
Paula L. Weissman
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Social Media Analytics ◽  
Content Type ◽  
The Public ◽  
Management Function ◽  
Use Of Social Media ◽  
The Impact ◽  
Corporate Brands ◽  
Depth Interviews

PurposeThe aim of this study was to understand how public relations leaders view and use social media analytics (SMA) and the impact of SMA on the public relations function.Design/methodology/approachThe research involved in-depth interviews with chief communication officers (CCOs) from leading multinational corporate brands.FindingsThe findings revealed that although CCOs perceive social media analytics as strategically important to the advancement of public relations, the use of social media data is slowed by challenges associated with building SMA capacity.Theoretical and practical implications – The research extends public relations theory on public relations as a strategic management function and provides practical insights for building SMA capabilities.Originality/valueThe study is among the first to provide empirical evidence of how companies are using social media analytics to enhance public relations efforts.

scholarly journals Social Media Analytics: Data Utilization of Social Media for Research

2019 ◽  
Vol 1 (2) ◽  
pp. 193-205
Author(s):  
Ria Andryani ◽  
Edi Surya Negara ◽  
Dendi Triadi
Keyword(s):  
Social Media ◽  
Public Perception ◽  
Data Retrieval ◽  
Media Analysis ◽  
Internet Technology ◽  
Mining Machine ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Visualization Techniques ◽  
Media Data

The amount of production data generated by social media opportunities that can be exploited by various parties, both government and private sectors to produce the information. Social media data can be used to know the behavior and public perception of the phenomenon or a particular event. To obtain and analyze social media data needed depth knowledge of Internet technology, social media, databases, data structures, information theory, data mining, machine learning, until the data and information visualization techniques. In this research, social media analysis on a particular topic and the development of prototype devices software used as a tool of social media data retrieval or retrieval of data applications. Social Media Analytics (SMA) aims to make the process of analysis and synthesis of social media data to produce information can be used by those in need. SMA process is done in three stages, namely: Capture, Understand and Present. This research is exploratorily focused on understanding the technology that became the basis of social media using various techniques exist and is already used in the study of social media analytic previously.

Computational Journalism Analysis on Young Adults' Body Images and Attitudes Toward Plastic Surgery

2021 ◽  
Vol 17 (4) ◽  
pp. 89-108
Author(s):  
Chutisant Kerdvibulvech ◽  
Pattaragun Wanishwattana
Keyword(s):  
Social Media ◽  
Young Adults ◽  
Plastic Surgery ◽  
Online Survey ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Body Images ◽  
Negative Body Image ◽  
Self Perceptions ◽  
The Impact

Computational journalism, especially social media analysis, is a very popular field in computational science. This study was conducted to explore and analyze the impact of the intensity of the exposure to social media on young Thai adults' body images and attitudes toward plastic surgery. The purposive sampling method was used for choosing 250 young Thai men and women aged 21 to 40 who used Facebook and/or Instagram on a regular basis. Online survey questionnaires were posted on Facebook for one month to achieve the results. It was found that young Thai adults frequently and heavily used both social media. Having appearance pressure from and repeated social comparison with idealistic media images, a considerable number of participants displayed more negative self-perceptions and engaged in appearance-changing strategies through increased appearance investment. The results showed that the more these young adults were exposed to social media, the more they were likely to develop a negative body image of themselves, which later caused their attitude toward plastic surgery to be positive.

scholarly journals Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News

2020 ◽  
Vol 6 (1) ◽  
pp. 205630512090340 ◽  
Author(s):  
Cristian Vaccari ◽  
Andrew Chadwick
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Representative Sample ◽  
Public Discourse ◽  
Civic Culture ◽  
United Kingdom Population ◽  
The United Kingdom ◽  
Experimental Treatments ◽  
The Impact ◽  
Large Representative

Artificial Intelligence (AI) now enables the mass creation of what have become known as “deepfakes”: synthetic videos that closely resemble real videos. Integrating theories about the power of visual communication and the role played by uncertainty in undermining trust in public discourse, we explain the likely contribution of deepfakes to online disinformation. Administering novel experimental treatments to a large representative sample of the United Kingdom population allowed us to compare people’s evaluations of deepfakes. We find that people are more likely to feel uncertain than to be misled by deepfakes, but this resulting uncertainty, in turn, reduces trust in news on social media. We conclude that deepfakes may contribute toward generalized indeterminacy and cynicism, further intensifying recent challenges to online civic culture in democratic societies.

scholarly journals Unpacking the impact of social media analytics on customer satisfaction: do external stakeholder characteristics matter?

2020 ◽  
Vol 40 (5) ◽  
pp. 647-669 ◽  
Author(s):  
Yichuan Wang ◽  
Minhao Zhang ◽  
Ying Kei Tse ◽  
Hing Kai Chan
Keyword(s):  
Social Media ◽  
Customer Satisfaction ◽  
Customer Engagement ◽  
Primary Data ◽  
Social Media Analytics ◽  
Content Type ◽  
External Stakeholder ◽  
Business Partners ◽  
External Stakeholders ◽  
The Impact

PurposeUnderpinned by the lens of Contingency Theory (CT), the purpose of this paper is to empirically evaluate whether the impact of social media analytics (SMA) on customer satisfaction (CS) is contingent on the characteristics of different external stakeholders, including business partners (i.e. partner diversity), competitors (i.e. localised competition) and customers (i.e. customer engagement).Design/methodology/approachUsing both subjective and objective measures from multiple sources, we collected primary data from 141 hotels operating in Greece and their archival data from TripAdvisor and the Hellenic Chamber of Hotels (HCH) database to test the hypothesised relationships. Data were analysed through structural equation modelling.FindingsThis study confirms the positive association between SMA and CS, but it remains subject to the varied characteristics of external stakeholders. We find that an increase in CS due to the implementation of SMA is more pronounced for firms that (1) adopt a selective distribution strategy where a limited number of business partners are chosen for collaboration or (2) operate in a highly competitive local environment. The results further indicate that high level of customer engagement amplifies the moderating effect of partner diversity (when it is low) and localised competition (when it is high) on the SMA–CS relationship.Originality/valueThe study provides novel insights for managers on the need to consider external stakeholder characteristics when implementing SMA to enhance firms' CS, and for researchers on the value of studying SMA implementation from the CT perspective.

Exploiting business networks in the age of social media: the use and integration of social media analytics in B2B marketing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yun Wang ◽  
Michel Rod ◽  
Qi Deng ◽  
Shaobo Ji
Keyword(s):  
Social Media ◽  
Development Model ◽  
Organizational Level ◽  
Social Media Analytics ◽  
Business Networks ◽  
Organizational Capability ◽  
Content Type ◽  
B2b Marketing ◽  
Proposed Model ◽  
The Impact

Purpose Based on an organizational capability perspective, this paper aims to propose a development model for social media analytics (SMA) capability that can be applied to business-to-business (B2B) marketing, with the aim of facilitating the use and integration of SMA in B2B marketing and maximizing the benefits of business networks in the age of social media. Design/methodology/approach This is a critical interpretive synthesis of SMA publications collected from academic journals, business magazines and the SMA service industry. In addition, an inter-disciplinary approach was adopted by drawing upon both marketing and information systems literature. In total, 123 academic papers, 106 industry case studies and 141 magazine papers were identified and analyzed. The findings were synthesized and compiled to address the predefined research question. Findings An SMA capability development model is proposed. The proposed model consists of four inter-dependent levels (technological, operational, managed and strategic) that collectively transfer the technological capability of SMA to the dynamic organizational capability. Each level of SMA capability is detailed. SMA-in-B2B marketing is highlighted as a socio-technical phenomenon, in which a technological level SMA capability is emphasized as the foundation for developing organizational level SMA capabilities and organizational capabilities, in turn, supporting and managing SMA activities and practices (e.g. strategic planning, social and cultural changes, skills and resources, measurements and values). Practical implications The proposed research framework may have implications for the operational level SMA development and the investigations on the direct and/or indirect measurements to help firms see the impact of SMA on their business. Originality/value This study may have implications for the adoption, use, integration and management of SMA in B2B marketing. The proposed model is grounded on the integrated insights from academia and industry. It is particularly relevant to B2B firms that have engaged in or plan to engage in applying SMA to extract insights from their online networks and is relevant to B2B researchers who are interested in SMA, big data and information technology organization integration studies.

Social Media to Social Media Analytics

2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
Social Media Platforms ◽  
The Individual ◽  
The Impact ◽  
Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

Analyzing the startup ecosystem of India: a Twitter analytics perspective

2019 ◽  
Vol 17 (2) ◽  
pp. 262-281 ◽  
Author(s):  
Shiwangi Singh ◽  
Akshay Chauhan ◽  
Sanjay Dhir
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Descriptive Analysis ◽  
Relevant Information ◽  
Media Analysis ◽  
Social Media Analytics ◽  
Content Type ◽  
Twitter Analytics ◽  
Bayes Algorithm

Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

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