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Advances in artificial intelligence have ushered in new opportunities for marketers in the domain of content generation. We discuss approaches that have emerged to generate text and image content. Drawing on the customer equity framework, we then discuss the potential applications of automated content generation for customer acquisition, relationship development, and customer retention. We conclude by discussing important considerations that businesses must make prior to adopting automated content generation.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.

In search of creating an extraordinary experience for customers, services have gone beyond the means of a transaction between buyers and sellers. In the event industry, where purchasing tickets online is a common procedure, it remains unclear as to how to enhance the multifaceted experience. This study aims at offering a snapshot into the most valued aspects for consumers and to uncover consumers' feelings toward their experience of purchasing event tickets on third-party ticketing platforms.

This is a cross-disciplinary study that applies knowledge from both data science and services marketing. Under the guise of natural language processing, latent Dirichlet allocation topic modeling and sentiment analysis were used to interpret the embedded meanings based on online reviews.

The findings conceptualized ten dimensions valued by eventgoers, including technical issues, value of core product and service, word-of-mouth, trustworthiness, professionalism and knowledgeability, customer support, information transparency, additional fee, prior experience and after-sales service. Among these aspects, consumers rated the value of the core product and service to be the most positive experience, whereas the additional fee was considered the least positive one.

Drawing from the intersection of natural language processing and the status quo of the event industry, this study offers a better understanding of eventgoers' experiences in the case of purchasing online event tickets. It also provides a hands-on guide for marketers to stage memorable experiences in the era of digitalization.

While the dynamic capabilities perspective is the most cited strategic theory in the information systems field of research, little effort has been made to review and integrate the associate literature of this perspective in the field. Accordingly, this paper aims to systematically analyze the information systems literature on dynamic capabilities and provide a holistic understanding of the topical composition and trend of dynamic capabilities studies in information systems research.

Using latent Dirichlet allocation as the text analysis algorithm, the author conducted a topic modeling of the dynamic capabilities corpus in the information systems field of research to quantitatively review, summarize and classify the prior literature. The review covered 191 articles published on dynamic capabilities between 1998 and 2018 in pioneering information systems journals and conference proceedings.

In accordance with the topic modeling results, the topical composition of the dynamic capabilities corpus in information systems research dominantly includes seven themes titled T1. Information systems value, T2. Information systems change, T3. Digitalization, T4. Information systems agility, T5. Big data, T6. Information systems innovation and T7. Information systems alignment. Also, the overall and topical trend of dynamic capabilities studies in the information systems field of research were revealed. The trends indicated that the investigated domain and its prominent sub-domains have generally had positive productivity over the past years.

The current study contributes to the domain by developing knowledge and improving literature on dynamic capabilities in information systems research, discovering the main topics of interest for information systems researchers to deploying the dynamic capabilities perspective in their studies, and prioritizing the future information systems research on dynamic capabilities based on the identified trends of topics.

This paper aims to explore consumer perception of “brand voice” authenticity, brand authenticity and brand attitude when the source of text is disclosed as either artificial intelligence (AI)-generated or human-written.

A 3 × 3 experimental design using Adidas marketing texts disclosed as either “AI” or “human”, or not disclosed was applied to data gathered online from 624 English-speaking students.

Text disclosed as AI-generated is not perceived as less authentic than that disclosed as human-written. No negative effect on brand voice authenticity and brand attitude results if an AI-source is disclosed.

Findings offer brand managers the potential for cost and time savings but emphasise the strong effect of AI technology on perceived brand authenticity and brand attitude.

Results show that brands can afford to be transparent in disclosing the use of AI to support brand voice as communicated in product description or specification or in chatbot text.

The purpose of this study is to identify the most frequent themes related with social media governance in government by year, analyzing if these themes have evolved over time, as well as highlighting the main risks and challenges found as further research opportunities.

First, the authors have extracted 431 abstracts from Scopus database. Then, abstracts were grouped by year to apply topic modeling to discover the underlying topics. Specifically, the authors have applied latent Dirichlet allocation algorithm to identify the most frequent topics by year.

The results reveal 19 important topics related with social media governance in government. Then, these topics were assigned to each year to identify the evolution of the research themes over the years, proposing interesting avenues for further research based on the identification of the main risks and challenges.

The proposed research methodology can be applied not only for research purposes but also to discover themes in any discourse with applications in politics, marketing, business, etc. In addition, it can be used to save time and costs analyzing citizen comments in public debates to identify the most important topics.

This study can serve to highlight gaps in the literature, opening the possibility that researchers can adequately position their inquiries, as well as to be aware of overstudied themes to pay less attention to them in future projects. In addition, the results of this study could serve as a starting point for other researchers to analyze connections between topics, propose theories that explain what was found and validate them in future studies.

India has the biggest number of active users on social media platforms, particularly Twitter. The purpose of this paper is to examine public sentiment on COVID-19 vaccines and COVID Appropriate Behaviour (CAB) by text mining (topic modeling) and network analysis supported by thematic modeling.

A sample dataset of 115,000 tweets from the Twitter platform was used to examine the perception of the COVID-19 vaccination and CAB from January 2021 to August 2021. The research applied a machine-learning algorithm and network analysis to extract hidden and latent patterns in unstructured data to identify the most prevalent themes. The COVID-19 Vaccine Hesitancy Amplification Model was formulated, which included five key topics based on sample big data from social media.

The identified themes are Social Media Adaptivity, Lack of Knowledge Providing Mechanism, Perception of Vaccine Safety Measures, Health Care Infrastructure Capabilities and Fear of Coronavirus (Coronaphobia). The study implication assists communication strategists and stakeholders design effective communication strategies using digital platforms. The study reveals CAB themes as with Mask Wearing Issues and Employment Issues as relevant themes discussed on digital channels.

The themes extracted in the present study provide a roadmap for policy-makers and communication experts to utilize social media platforms for communicating and understanding the perception of preventive measures of vaccination and CAB. As evidenced by the increased engagement on social media platforms during the COVID-19-induced lockdown, digital platforms are indeed valuable from the communication perspective to be proactive in the event of a similar situation. Moreover, significant themes, including social media adaptivity, absence of knowledge-providing mechanism and perception of safety measures of the vaccine, are the critical parameters leading to an amplified effect on vaccine hesitancy.

The COVID-19 Vaccine Hesitancy Amplification Themes (CVHAT) equips stakeholders and government strategists with a preconfigured paradigm to tackle dedicated communication campaigns and assess digital community behavior during health emergencies COVID-19.

The increased acceptance of vaccines and the following of CAB decrease the advocacy of mutation of the virus and promote the healthy being of the people. As CAB has been mentioned as a preventive strategy against the COVID-19 pandemic, the research preposition promotes communication intervention which helps to mitigate future such pandemics. As developing, economies require effective communication strategies for vaccine acceptance and CAB, this study contributes to filling the gap using a digital environment.

Chan et al. (2020) recommended using social media platforms for public knowledge dissemination. The study observed that the value of a communication strategy is increased when communication happens using highly trusted and accessible channels such as Twitter and Facebook. With the preceding context, the present study is a novel approach to contribute toward digital communication strategies related to vaccination and CAB.

Due to the high penetration of social media and mobile devices in the recent decade, especially with the coronavirus, digital media tools have become a priority for marketing managers. Digital content marketing (DCM) is one of the crucial ingredients of the digital marketing strategy of businesses, which proposes value to the audience through brand-related and relevant content. The tourism industry is also trapped in the digital wave and has witnessed fundamental changes in how customers communicate. The growth of investment in DCM in this industry to introduce tourist attractions and acquire tourists calls for more research to explore multiple aspects of these initiatives' implementation. Despite the importance of DCM, there is no clear understanding of its implementation's various components. Therefore, the primary goal of the current study is to design a new comprehensive framework of DCM implementation that integrates its antecedents, process, and consequences in the tourism industry.

The mixed method was applied to achieve the research goal. The initial criteria and main components of the framework were identified with a comprehensive literature review to develop the framework. To enrich the initial criteria, some semi-structured interviews with experts were conducted; then, the extracted criteria and sub-criteria were prioritized and weighted using the quantitative best-worst method (BWM).

The results indicate that the proposed integrated framework contains three categories of antecedents, processes, and consequences and 12 main concepts. The weights and ranks of the extracted concepts and their sub-criteria are calculated using BWM.

The proposed framework helps managers have a big picture of the DCM strategy to successfully implement and consider the multiple dimensions of such initiatives. The proposed framework provides actionable insight for digital marketing decision-makers to manage such projects effectively and plan appropriate actions for progress.

A review of content marketing reveals that there are few studies conducted that integrate the components of the DCM implementation process, including antecedents, process, and consequences. This research is one of the first in the field of DCM implementation in the tourism industry to fill this theoretical gap. The main contribution of this research is to design a new integrated framework for DCM implementation that offers a holistic view of antecedents, process, and consequences.