research papers for internet marketing

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• v.7(12); 2021 Dec

How digital marketing evolved over time: A bibliometric analysis on scopus database

Mohammad faruk.

a Department of Business Administration, Bangladesh Army International University of Science and Technology, Cumilla, Bangladesh

Mahfuzur Rahman

b Department of Marketing, Comilla University, Cumilla, Bangladesh

Shahedul Hasan

c East Delta University, Chattogram, Bangladesh

Associated Data

Data will be made available on request.

Nowadays, a large number of customers are spending their time on social and digital media for a variety of purposes ranging from information searching to the final purchase of products. Responding to this shift, marketers are spending a significant part of the advertising budget on digital marketing. Therefore, the purpose of this study is to review articles on digital marketing to identify top themes, determine the current status of research in digital marketing and indicate how influential works have shaped it. This research has reviewed 925 papers published between 2000 and 2019 in Scopus by applying bibliometrics analysis. These results show that on average 2.18 authors have contributed to every single paper on digital marketing and the collaboration index is 2.71. The top contributing countries in the digital marketing field are USA, India and UK. The study also identifies three dominant clusters in digital marketing research, e.g., 1) strategic planning with digital marketing 2) mobile marketing with apps development and 3) dealing with demographic profiles of customers.

Bibliometric analysis; Digital marketing; 4th industrial revolution; Scopus database.

1. Introduction

It is reported that, in December 1995, internet users were only 16 million. On the other hand, in June 2019, the number increased to 4,536 million which cover 58.8% of the total world population that amounting to 7.71 billion ( Busca and Bertrandias, 2020 ). It is estimated that everyday people spend, on average, 6 h and 42 min online and by 2021, 73% of e-commerce sales will be generated through the mobile platform ( Mandal, 2017 ). Moreover, the 4 th industrial revolution has begun with the invention of web 4.0, the internet of things (IoT), blockchain, artificial intelligence (AI), big data analytics and 4g/5g internet speed ( Kerren, 2014 ). These technological inventions have significantly affected the lifestyle of consumers and the way marketers communicate with their customers. In 2004, Facebook came into the market, followed by many other social networking sites in later years. People had accepted these social media at an exponential rate affecting the way people communicated and interacted with each other.

After the induction of world wide web technology, people have become used to the virtual world. When people (e.g., customers) shifted to the internet or virtual marketplace, marketers focused their marketing attention on this market. Consumers are spending more time on social media for a variety of purposes ranging from brand information searching to the final purchase of products. Consumers’ shifting from traditional media to digital media enables marketers to reach, notify, engage, sell to, study about and provide services to the targeted audience more effectively and efficiently. Responding to this fundamental shifting of consumers from traditional to digital media, marketers are continuously trying to grab the opportunity by devising product, price, place and promotion strategies for this marketplace. Therefore, scholars have investigated different aspects of digital marketing (DM).

While reviewing the literature, it is noticed very few research studies were focused on identifying and analyzing the development of themes and clusters in this arena by applying bibliometric analysis ( Ghorbani et al., 2021 ; Kim et al., 2019 ; León-Castro et al., 2021 ). Ghorbani et al. (2021) conducted a bibliometric analysis to identify key trends and patterns in the field of DM by investigating 924 research articles published in the Scopus database. However, given the importance of digital marketing, more systematic literature reviews are necessary for this field. Kim et al. (2019) undertook a bibliometric analysis that was more focused on digital marketing communication (DMC) and hence, studies other than DMC were ignored. León-Castro et al. (2021) covered only a web of science database to run a bibliometric analysis on digital marketing, but the keywords focused on more specific aspects of DM such as “influencer”, “ewom”, “youtube”, “instagram” and “facebook”.

However, finding out how scholarly works on digital marketing practice and theory have been developed over time and contributed to DM literature is limited. Analyzing which journals, countries and authors are contributing more in the field of digital marketing was also nascent. The authors have exactly taken the endeavour to address these issues. Since this study will analyze all the major scholarly articles that are published in the Scopus database, it will pave the way for future researchers who intend to research digital marketing.

Considering the limitations of the past studies on DM, the study has been undertaken to serve several purposes such as a) to identify the evolution of DM literature over time by applying a bibliometric analysis; b) to assess and synthesize 925 Scopus papers and offer future research directions in the field of DM.

The significant contribution of this study includes identifying which are the journals and authors that contributed the most in the development of digital marketing. It also contributes by explaining the emergent themes in DM along with identifying the most cited journals in this sector. Moreover, the co-citation networks that exist between most cited researches and the schools of thought that exist in co-citation networks have also been investigated thoroughly.

This paper is organized into several parts. The following section contains a literature review followed by the research methodology. The next section includes results and discussion from the bibliometric analysis of the articles published in Scopus between 2000 and 2019. The final part of the paper includes the conclusion and implications from theoretical and practical perspectives along with limitations and future research directions.

2. Literature review

With the advent of social media and development in the web and mobile apps technologies, communication has become much easier than that of past decades ( Khomenko et al., 2020 ). Since modern customers are spending their time in digital media, marketers have also developed strategies and tactics to reach them through these media. Therefore, a significant amount of scholarly research had been conducted on different aspects such as search engine optimization, social media marketing, affiliate marketing, content marketing, video marketing and many others ( Jimenez, 2020 ). This study presents an intensive analysis of scholarly works done and published by scholars from different countries on this revolutionary field of marketing between 2000 to 2019. Digital marketing opens up new opportunities for reaching, informing, and engaging consumers, as well as providing and selling goods and services. Digital marketing is projected to remain at the forefront of the technological transition in the future ( Ko, 2019 ; Lamberton and Stephen, 2016 ; Martín-Consuegra et al., 2018 ). Millions of people's daily lives have been transformed by digital marketing through social and mobile media, which has expanded into popular social media practices and often leads to the formation of customer relationships ( Fujita et al., 2017 ; Han et al., 2016 ; Kim, 2018 ; Woodside and Mir, 2019 ).

As more marketing researchers and professionals have dedicated themselves to digital technologies, the speed of transition has quickened. The digital marketing model has changed from selling unique goods and services to marketing campaigns that are introduced across digital platforms to now make use of digital resources. Social media has existed for over the past decade for several different purposes such as blogging, video and photography/photo-sharing using mobile phones ( Fujita et al., 2017 ; Han et al., 2016 ; Kim, 2018 ). Virtual technologies such as artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) seem to be replacing traditional approaches to marketing suggesting new territory for marketing researchers to pursue ( Brodie and Juric, 2018 ; Guercini et al., 2018 ; Kim and Yang, 2018 , J. Kim et al., 2018 ; Taylor and Costello, 2017 ; Zhang and Dholakia, 2018 ).

Marketers soon noticed the networking advantages of social networks like Facebook, YouTube, Twitter, Instagram, Snapchat, Pinterest, and LinkedIn, and invested $51.3 billion on global social network ads in 2017, up 55.4% from 2016 ( Cooper, 2020 ). The amount spent on digital ads is expected to rise 17.7% in 2018, accounting for $273 billion (44%) of the $629 billion spent on advertising globally ( McNair, 2018 ). In 2017, mobile ad spending rose by 39%, and it is projected to rise by another 27% in 2018, accounting for 55% of all digital ad spending ( Magna Global, 2017 ). The growing concentration of advertising dollars demonstrates digital marketing's effectiveness in targeting audiences and achieving growth goals such as increased revenue, brand recognition, consumer loyalty, lead generation, and lower customer acquisition and service costs ( Labrecque et al., 2013 ; Lamberton and Stephen, 2016 ; Tuten, 2020 ).

The way businesses market themselves is changing as a result of social media, posing new obstacles as well as opportunities ( Arora and Sanni, 2019 ; Dwivedi et al., 2015 , 2017 ; Hossain et al., 2019 ; Nisar et al., 2018 ; Wang and Herrando, 2019 ). Digital marketing, whether used inappropriately or by unskilled practitioners, may harm businesses ( Aswani et al., 2018 ). As a result, businesses must gain social media expertise ( Braojos-Gomez et al., 2015 ). Companies should focus on aligning their digital marketing strategies with their overall business goals ( Tafesse and Wien, 2018 ; Thorpe, 2018 ). When used strategically, social media marketing may lead to increased consumer satisfaction and perceived value ( Chen and Lin, 2019 ; Pacauskas et al., 2018 ), co-creation ( Kamboj et al., 2018 ; Zhang et al., 2017 ), brand loyalty ( Laroche et al., 2013 ; Shanahan et al., 2019 ) and positive attitude ( Laroche et al., 2013 ).

Furthermore, social media has opened up new avenues for marketers to obtain audience experience by researching online user-generated content, electronic word of mouth (eWOM) conversations ( Chang et al., 2019 ; Liu et al., 2019 ; Xu et al., 2017 ), and online communities ( Chang et al., 2019 ; Habibi et al., 2014 ; Liu et al., 2018 ). Consumer reviews are a large part of social media, and they throw up questions about content accuracy, credibility, usefulness, and validity ( Ismagilova et al., 2017 ; Kapoor et al., 2018 ; Singh et al., 2017 ). Consumer preferences and purchasing habits can be affected by online feedback, which can affect a company's results ( Ismagilova et al., 2020 ; Kawaf and Istanbulluoglu, 2019 ; Shareef et al., 2018 ; Yerasani et al., 2019 ).

A variety of factors can influence digital marketing activities and practices. Some research, for example, looked at the impact of new laws on digital marketing ( Hemsley, 2018 ; Sposit, 2019 ). Furthermore, social media marketing research has begun to concentrate on developing markets, where the adoption rate of social media marketing is lower than the developed countries ( Christino et al., 2019 ; Liu et al., 2019 ). Some businesses in these developing countries continue to rely on conventional media for product and service ads because they are more trustworthy than social media platforms ( Ali et al., 2016 ; Olanrewaju et al., 2020 ). Therefore, this article aims at assessing different paradigms of published articles on DM and finding out how these studies evolved. In addition, finding out what are the dominant themes in this area of research is also a concern of this paper.

3. Methodology

Bibliometric analysis along with a citation and co-citation analysis presents a powerful way to analyze the patterns and characteristics of already published papers in any scholarly field. It may also help to find out the school of thought, if any, in any specific area of study ( Mandal, 2017 ; Christie, 2008 ). The bibliometric analysis takes the objective philosophy and employs a quantitative investigation method on written documents (i.e., journals, books, websites). Citation and co-citation analysis focus on finding out the emergent themes in specific areas of study, the impact of different journals and different schools of thought ( Nyagadza, 2020 ). Going beyond merely counting and collating citations, previous studies have pointed out the nature and course of development of a discipline to assess which journals and authors have created value to other researchers by collaboration.

Bibliometric studies, such as citation and co-citation analyses, are useful for delving into the trends and characteristics of what has been written, making it easier to explore, organize, and articulate work done in a particular discipline ( Diodato, 1994 ; Ferreira et al., 2014 ). Bibliometric analyses can help to guide collection growth, define institutional scholarship strengths and citation/co-citation trends, and identify possible schools of thought in a discipline ( Lewis and Alpi, 2017 ). For a comprehensive investigation of written source documents (e.g., academic journal papers and books), bibliometric research uses citation and co-citation analyses as an analytical tool for inspecting part or the entirety of a scholarly discipline ( Diodato, 1994 ; Ferreira et al., 2014 ; Nerur et al., 2008 ; Ramos-Rodríguez and Ruíz-Navarro, 2004 ; Shafique, 2013 ).

The researchers have adopted objectivist research philosophy since it focuses on quantitative methods of analysis and bibliometric analysis is a powerful quantitative tool to analyze published documents in any scholarly area ( Diodato and Gellatly, 2013 ). The authors have mined the bibliometric data from the Scopus database with the keyword “Digital Marketing”. Digital marketing is the common keyword across different papers, however, articles with other related keywords such as “social networking online”, “social media sales”, “electronic commerce”, “data mining”, “information systems” were considered. Scopus database was selected by the authors since they had authorized access to this database only. Hence analysis on other prominent databases such as Web of Science can be considered in future research.

After loading the dataset, it is observed that it contains 935 articles in total starting from 1982. However, the authors have applied the “publication year” filtering strategy and kept the data from 2000 to 2019. This period is chosen since the proliferation of the internet began in the 21st century. In the case of “document type”, all types of documents (i.e., article, book, book chapter, conference paper, conference review, editorial, short survey, note review) were considered. All types of documents were considered since the author had applied only one keyword “digital marketing” for retrieving data and it only produced 935 articles. In addition, the authors wanted to investigate the theoretical and practical development of digital marketing throughout every scholarly research field and understand the relationship among them. Other studies also applied a similar technique to represent the whole DM research ( Ghorbani et al., 2021 ). About “total citation”, the full range of citations from 0 to 305 were considered since the authors wanted to consider both highly cited articles and lowly cited articles and this helps to identify the difference between good work and mediocre research work. In addition, about “source by Bradford Law Zones”, all the sources were considered. This filtration has produced 925 papers finally which are to be analyzed. After retrieving the data from the Scopus database, with the help of the bibliometrics package of R programming, the data were analyzed.

4. Results and discussion

4.1. summary statistics.

This chapter presents analysis and findings from bibliometric analysis of 925 documents related to digital marketing published between 2000 and 2019. Table 1 presents the summary findings from the analysis. The documents that were published in this period in the Scopus database received 5.076 citations on average. The higher average citations per document indicate a speedy growth of scholarly papers in the field of DM. The results also showed that 2015 unique authors have contributed to the digital marketing field in this period, who got impressions of 2359 times. In addition, single-author documents counted as 262. On average, 2.18 authors contributed to completing each document while every single author contributed to at least 0.459 documents. Documents per author counted to 0.459 while co-authors per document are 2.55. This signifies that in the development of digital marketing, a good amount of research studies are done in collaboration with other authors which is again confirmed in the collaboration index of 2.71. However, a significant amount of single-author articles are also undertaken.

Table 1

Summary statistics.

4.2. Performance analysis

Figure 1 showed the key trends in annual scientific production in the DM field. The timeline can be broadly divided into two main decades with varying trends in annual publications. Although research on digital marketing and related topics had begun as early as 2000, digital marketing studies were almost overlooked by the researchers during the first decade (2000–2010). Therefore, the actual proliferation commenced after 2010 meaning in the second decade (2010–2020) and as time passes, research in this domain has grown exponentially. This growth can be attributed to the increasing number of internet and social media users in the 2000s ( Ghorbani et al., 2021 ). When we see the research development from the perspective of Pareto's law, in only 4 years' time period (2016–2019), 70% (648) of the research papers were published in Scopus on digital marketing. Contemporary studies are found to focus more on marketing science issues accompanied by modern information technology tools and techniques such as artificial intelligence, big data, deep learning etc.

Annual scientific production in digital marketing.

4.3. Relationship between authors, keywords and sources

Figure 2 contained three field analyses showing the relationship between authors, keywords and sources where the left column contained the name of authors, the middle column contained keywords and the right column contained the journal name. This confirms that most of the authors have considered digital marketing as their keyword. However, “social media marketing”, “internet”, “machine learning”, “web 2.0”, “social networks”, “customer relationship management”, “Facebook”, “Twitter” and many others closely related keywords with digital marketing had also been used in different research articles. A new trend is represented by social media for companies that strive to communicate with their customers using both online and offline media. For example, popular social media sites like Facebook, Twitter, YouTube and corporate blogs are increasingly used by the Fortune 500 companies in their marketing communication campaigns ( Markos-Kujbus and Gati, 2012 ). Previous researchers who used digital marketing as their keyword also found to include the above-listed keywords. But this is also clear from the data that focus on digital marketing is higher than any other keywords. The reason for this consideration can be justified with the proliferation of the use of digital marketing compared to other semantic terminologies which can also be used to mean the same thing. Almost every journal contributed equally, although some journals such as Journal of Direct, Data and Digital Marketing Practice, are pioneering the advancement in this field. Although mobile marketing is a powerfully dominant domain of digital marketing ( Cheng et al., 2013 ), this area is yet to be explored. Therefore, future researchers can contribute to this domain.

Three field analyses in digital marketing.

4.4. Performance of academic journals

To identify the most contributing journals, Figure 3 showed that the “Journal of Direct Data and Digital Marketing Practice” had the highest contribution in this domain. This journal solely published 46 research papers within the specified period amounted to almost 5% of the total publications. However, this journal of Springer has last published articles in June 2016 and till then it is not being published anymore. That means the top-most contributing journal is out of the market and thereby creating a gap in the field and providing other journals to fill the gap. “Journal of Digital and Social Media Marketing” has published 10 papers and “Journal of Marketing Education” has also contributed 10 papers in this domain. Moreover, another critical point to be noted here, the Journal of Research in Interactive Marketing is not on the top contributing list, although this journal is contributing significantly in the domain with its strong editorial board.

Most contributing journal in digital marketing.

4.5. Source growth of digital marketing over time

As we explained earlier in this paper, “Journal of Direct Data and Digital Marketing Practice” had contributed the most in the digital marketing research area. However, the contribution of this journal has decreased significantly in recent times (see Figure 4 ). On the contrary, the contribution of sources like the “International Journal of Recent Technology and Engineering” is increasing exponentially. Adobe Research, Amity University, Yonsei University are found to be the most contributing parties in the scholarly publications on digital marketing. However, other universities such as Chaoyang University of Technology, Jaypee Business School, and the University of Florida have also kept significant contributions in this domain.

Source growth of digital marketing over time.

4.6. Contribution by countries

In the case of the contribution of different countries in scholarly works on digital marketing, the bibliometric analysis found that the USA had contributed the most (223 papers). Surprisingly researchers from India have achieved the second position in contributing to this emerging field of study (187 papers). However, this is a powerful point that in the case of MCP or author collaboration with the authors from other countries, the USA ranked 1st while the UK ranked 2nd. India's authors have not secured the second position in this area. In addition, the UK has kept a significant amount of contribution and other countries such as Indonesia, Spain, Korea, Portugal, Brazil, and France's contributions are average.

As the contribution from the USA, UK and India are the most, theoretically, this is expected that collaboration among researchers in these countries would be the highest. This expectation is confirmed with this Figure 5 . As a country Australia does not have many contributions, however, different authors from this country have collaborated significantly with authors from other countries. From a continental analytical point of view, it is observed that North America and Europe contributed the most followed by Asia. However, South America and Africa are completely void of any kind of notable contribution. This can be attributed to the economic and demographic development of the countries. Most of the countries in South America and Africa are not developed as other contributing countries. Another interesting insight is also observed in this analysis, which is the “North-South gap”. Countries located north of the equator are observed to contribute more than the countries located south of the equator.

Authors' collaboration around the world.

4.7. Bibliographic links of academic journals

This bibliometric analysis identified 3 dominant research clusters in digital marketing, as illustrated in Figure 6 . Each cluster has a significant amount of difference from other clusters. As identified with the analysis, the biggest cluster in digital marketing is about strategic planning with digital marketing. Another dominant research domain in digital marketing focuses on mobile marketing with apps development. In addition, the third cluster on digital marketing concentrates especially on dealing with demographic profiles of customers along with website marketing metrics. In addition, the word-cloud analysis expresses which keyword was discussed the most in these 925 papers published from 2000 to 2019 in Scopus. Digital marketing is the central word which is accompanied by other dominant keywords such as commerce, marketing, social networking online, social media sales and internet. Another amazing insight generated from this cluster analysis, e.g., three clusters represents three different facets of marketing; the green-coloured cluster represents societal and humane aspects of marketing while the red- coloured cluster represents the fundamental philosophical and strategic aspects of marketing (i.e., consumer behaviour, strategic planning, information management, social media, public relations) and the blue coloured cluster represents the recent development of digital marketing because of the proliferation of extensive data generation (i.e., database management, data mining, artificial management). But here one inconsistency can be noticed that theoretically big data analytics should fall in the blue coloured cluster but here it falls in the red coloured cluster and the authors are unable to explain the reason for this which can also be considered as a limitation of the article. Although digital marketing has received its importance after the advent of social media after 2000, it is considered in the red coloured cluster. That means digital marketing must be considered as the fundamental part of marketing and it must be given its due importance from the implicational aspect while the theory and philosophy remain the same.

Cluster analysis in digital marketing research.

5. Conclusion and implications

The bibliometric study provides a comprehensive picture of specific research fields and enables researchers to focus on unique areas to add new results and knowledge to the literature ( Ghorbani et al., 2021 ). In summary, this can be firmly claimed that the growth in the field of DM research has started in 2014. Three dominant themes of study have been developed including a strategic framework, mobile marketing and apps development and demographic analysis with web analytics. As a country, USA, UK and India contributed the most to this development. In the last half-decade, digital marketing has evolved as a buzzword. Revolution has been created by electronic commerce in business by transforming the physical aspect of delivery to the virtual aspect of marketing and selling. Digital marketing has become an integral part of any marketing and sales strategy ( Bhojaraja and Muniraju, 2018 ).

Analysis of the DM literature through bibliometric analysis will assist both the academicians and practitioners in various ways. First of all, this study will inform academic researchers and digital marketers regarding the evolution, trends and history of digital marketing. This paper will also inform about the most researched domains under DM, hence, enables researchers to identify research gaps to be filled by further studies in the future. The analysis shows that digital marketing is the single most keyword used in most of the studies. The other areas along with DM should also be investigated including consumer behaviour, social networks, machine learning, big data, advertising, mobile marketing, web 2.0, branding and so forth. Secondly, the study shows that research on DM has received tremendous focus since 2010 due to the growth of the internet and social media. As social media allure customers to speak for the brands, global companies increasingly focus on digital marketing as an effective tool of brand communication ( Bhuyan and Rahman, 2014 ). Thus, digital marketers should ensure the best use of digital media in brand communication. Third, the analysis reveals that DM literature is the most prevalent in the countries like USA, UK and India. Future researchers should focus on other parts of the world especially the developing countries regarding the prospects of digital marketing.

Finally, there are three dominant clusters are identified from the analysis. Strategic planning with digital marketing is the largest cluster suggesting the significant domain for both researchers and policymakers. After happening the latest technological revolution in businesses, digital marketing has become more prominent and widely practised. The methods of traditional marketing are completely replaced by those of digital marketing. Nowadays, marketers are forced to use the internet and digital technology for selling and promoting their products and services. Therefore, both the prospects and challenges of digital marketing must be properly detected and analyzed by marketers to set the best marketing plan and communication goals ( Bhojaraja and Muniraju, 2018 ). Mobile marketing with apps development is another domain identified from the cluster analysis. The rate of smartphone penetration and mobile applications is going up day by day due to availability and affordability. Therefore, markers should adopt mobile marketing such as banner ads on apps, SMS marketing and so forth to reach the target customers. The last research cluster is demographic profiles of customers along with website marketing metrics. The outcomes of marketing investment can easily be measured using digital marketing metrics. The effectiveness and quality of online content can also be evaluated and audited with the help of digital marketing ( Bhojaraja and Muniraju, 2018 ).

5.1. Limitations of the study and future research direction

This study does not include the major works indexed in another significant database (i.e. web of science), which is the major limitation of the study. In addition to that, documents were explored using the only keyword “Digital Marketing”, hence, other relevant keywords were not considered. Only one keyword is chosen to keep the analysis simple and to make it easier for the authors to interpret the analysis. Hence, further researches can be conducted to get a more holistic view by considering other strongly related keywords such as “online marketing”, “social media marketing”, “email marketing”, “affiliate marketing” and “mobile marketing”.

In future, how big data analytics and artificial intelligence are going to affect the digital marketing landscape can be explored. How marketing research has been shaped in the digital marketing field can also be an interesting pathway to pursue future research. Why USA, UK and India have contributed the most, on the other hand, why Canada, Australia, Germany, Russia, France and others are lagging in contributing to this field should also be analyzed.

Declarations

Author contribution statement.

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

We are grateful to Dr Md. Abul Kalam Azad, Associate Professor, BTM, Islamic University of Technology for helping to retrieve the (.bib) data file from the Scopus database.

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The future of social media in marketing

• Conceptual/Theoretical Paper
• Open access
• Published: 12 October 2019
• Volume 48 , pages 79–95, ( 2020 )

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• Gil Appel 1 ,
• Lauren Grewal 2 ,
• Rhonda Hadi 3 &
• Andrew T. Stephen 3 , 4  

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Social media allows people to freely interact with others and offers multiple ways for marketers to reach and engage with consumers. Considering the numerous ways social media affects individuals and businesses alike, in this article, the authors focus on where they believe the future of social media lies when considering marketing-related topics and issues. Drawing on academic research, discussions with industry leaders, and popular discourse, the authors identify nine themes, organized by predicted imminence (i.e., the immediate, near, and far futures), that they believe will meaningfully shape the future of social media through three lenses: consumer, industry, and public policy. Within each theme, the authors describe the digital landscape, present and discuss their predictions, and identify relevant future research directions for academics and practitioners.

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Introduction

Social media is used by billions of people around the world and has fast become one of the defining technologies of our time. Facebook, for example, reported having 2.38 billion monthly active users and 1.56 billion daily active users as of March 31, 2019 (Facebook 2019 ). Globally, the total number of social media users is estimated to grow to 3.29 billion users in 2022, which will be 42.3% of the world’s population (eMarketer 2018 ). Given the massive potential audience available who are spending many hours a day using social media across the various platforms, it is not surprising that marketers have embraced social media as a marketing channel. Academically, social media has also been embraced, and an extensive body of research on social media marketing and related topics, such as online word of mouth (WOM) and online networks, has been developed. Despite what academics and practitioners have studied and learned over the last 15–20 years on this topic, due to the fast-paced and ever-changing nature of social media—and how consumers use it—the future of social media in marketing might not be merely a continuation of what we have already seen. Therefore, we ask a pertinent question, what is the future of social media in marketing?

Addressing this question is the goal of this article. It is important to consider the future of social media in the context of consumer behavior and marketing, since social media has become a vital marketing and communications channel for businesses, organizations and institutions alike, including those in the political sphere. Moreover, social media is culturally significant since it has become, for many, the primary domain in which they receive vast amounts of information, share content and aspects of their lives with others, and receive information about the world around them (even though that information might be of questionable accuracy). Vitally, social media is always changing. Social media as we know it today is different than even a year ago (let alone a decade ago), and social media a year from now will likely be different than now. This is due to constant innovation taking place on both the technology side (e.g., by the major platforms constantly adding new features and services) and the user/consumer side (e.g., people finding new uses for social media) of social media.

What is social media?

Definitionally, social media can be thought of in a few different ways. In a practical sense, it is a collection of software-based digital technologies—usually presented as apps and websites—that provide users with digital environments in which they can send and receive digital content or information over some type of online social network. In this sense, we can think of social media as the major platforms and their features, such as Facebook, Instagram, and Twitter. We can also in practical terms of social media as another type of digital marketing channel that marketers can use to communicate with consumers through advertising. But we can also think of social media more broadly, seeing it less as digital media and specific technology services, and more as digital places where people conduct significant parts of their lives. From this perspective, it means that social media becomes less about the specific technologies or platforms, and more about what people do in these environments. To date, this has tended to be largely about information sharing, and, in marketing, often thought of as a form of (online) word of mouth (WOM).

Building on these definitional perspectives, and thinking about the future, we consider social media to be a technology-centric—but not entirely technological—ecosystem in which a diverse and complex set of behaviors, interactions, and exchanges involving various kinds of interconnected actors (individuals and firms, organizations, and institutions) can occur. Social media is pervasive, widely used, and culturally relevant. This definitional perspective is deliberately broad because we believe that social media has essentially become almost anything—content, information, behaviors, people, organizations, institutions—that can exist in an interconnected, networked digital environment where interactivity is possible. It has evolved from being simply an online instantiation of WOM behaviors and content/information creation and sharing. It is pervasive across societies (and geographic borders) and culturally prominent at both local and global levels.

Throughout the paper we consider many of the definitional and phenomenological aspects described above and explore their implications for consumers and marketing in order to address our question about the future of marketing-related social media. By drawing on academic research, discussions with industry leaders, popular discourse, and our own expertise, we present and discuss a framework featuring nine themes that we believe will meaningfully shape the future of social media in marketing. These themes by no means represent a comprehensive list of all emerging trends in the social media domain and include aspects that are both familiar in extant social media marketing literature (e.g., online WOM, engagement, and user-generated content) and emergent (e.g., sensory considerations in human-computer interaction and new types of unstructured data, including text, audio, images, and video). The themes we present were chosen because they capture important changes in the social media space through the lenses of important stakeholders, including consumers, industry/practice, and public policy.

In addition to describing the nature and consequences of each theme, we identify research directions that academics and practitioners may wish to explore. While it is infeasible to forecast precisely what the future has in store or to project these on a specific timeline, we have organized the emergent themes into three time-progressive waves, according to imminence of impact (i.e., the immediate, near, and far future). Before presenting our framework for the future of social media in marketing and its implications for research (and practice and policy), we provide a brief overview of where social media currently stands as a major media and marketing channel.

Social media at present

The current social media landscape has two key aspects to it. First are the platforms—major and minor, established and emerging—that provide the underlying technologies and business models making up the industry and ecosystem. Second are the use cases; i.e., how various kinds of people and organizations are using these technologies and for what purposes.

The rise of social media, and the manner in which it has impacted both consumer behavior and marketing practice, has largely been driven by the platforms themselves. Some readers might recall the “early days” of social media where social networking sites such as MySpace and Friendster were popular. These sites were precursors to Facebook and everything else that has developed over the last decade. Alongside these platforms, we continue to have other forms of social media such as messaging (which started with basic Internet Relay Chat services in the 1990s and the SMS text messaging built into early digital mobile telephone standards in the 2000s), and asynchronous online conversations arranged around specific topics of interest (e.g., threaded discussion forums, subreddits on Reddit). More recently, we have seen the rise of social media platforms where images and videos replace text, such as Instagram and Snapchat.

Across platforms, historically and to the present day, the dominant business model has involved monetization of users (audiences) by offering advertising services to anyone wishing to reach those audiences with digital content and marketing communications. Prior research has examined the usefulness of social media (in its various forms) for marketing purposes. For example, work by Trusov et al. ( 2009 ) and Stephen and Galak ( 2012 ) demonstrated that certain kinds of social interactions that now happen on social media (e.g., “refer a friend” features and discussions in online communities) can positively affect important marketing outcomes such as new customer acquisition and sales. More recently, the value of advertising on social media continues to be explored (e.g., Gordon et al. 2019 ), as well as how it interacts with other forms of media such as television (e.g., Fossen and Schweidel 2016 , 2019 ) and affects new product adoption through diffusion of information mechanisms (e.g., Hennig-Thurau et al. 2015 ).

Although the rise (and fall) of various kinds of social media platforms has been important for understanding the social media landscape, our contention is that understanding the current situation of social media, at least from a marketing perspective, lies more in what the users do on these platforms than the technologies or services offered by these platforms. Presently, people around the world use social media in its various forms (e.g., news feeds on Facebook and Twitter, private messaging on WhatsApp and WeChat, and discussion forums on Reddit) for a number of purposes. These can generally be categorized as (1) digitally communicating and socializing with known others, such as family and friends, (2) doing the same but with unknown others but who share common interests, and (3) accessing and contributing to digital content such as news, gossip, and user-generated product reviews.

All of these use cases are essentially WOM in one form or another. This, at least, is how marketing scholars have mainly characterized social media, as discussed by Lamberton and Stephen ( 2016 ). Indeed, online WOM has been—and, we contend, will continue to be—important in marketing (e.g., in the meta-analysis by Babić Rosario et al. 2016 the authors found, on average, a positive correlation between online WOM and sales). The present perspective on social media is that people use it for creating, accessing, and spreading information via WOM to various types of others, be it known “strong ties” or “weak ties” in their networks or unknown “strangers.” Some extant research has looked at social media from the WOM perspective of the consequences of the transmission of WOM (e.g., creating a Facebook post or tweeting) on others (e.g., Herhausen et al. 2019 ; Stephen and Lehmann 2016 ), the impact of the type of WOM content shared on others’ behavior (e.g., Villarroel Ordenes et al. 2017 ; Villarroel Ordenes et al. 2018 ), and on the motivations that drive consumer posting on social media, including considerations of status and self-presentation (e.g., Grewal et al. 2019 ; Hennig-Thurau et al. 2004 ; Hollenbeck and Kaikati 2012 ; Toubia and Stephen 2013 ; Wallace et al. 2014 ).

While this current characterization of WOM appears reasonable, it considers social media only from a communications perspective (and as a type of media channel). However, as social media matures, broader social implications emerge. To appropriately consider the future, we must expand our perspective beyond the narrow communicative aspects of social media and consider instead how consumers might use it. Hence, in our vision for the future of social media in marketing in the following sections, we attempt to present a more expansive perspective of what social media is (and will become) and explain why this perspective is relevant to marketing research and practice.

Overview of framework for the future of social media in marketing

In the following sections we present a framework for the immediate, near, and far future of social media in marketing when considering various relevant stakeholders. Themes in the immediate future represent those which already exist in the current marketplace, and that we believe will continue shaping the social media landscape. The near future section examines trends that have shown early signs of manifesting, and that we believe will meaningfully alter the social media landscape in the imminent future. Finally, themes designated as being in the far future represent more speculative projections that we deem capable of long-term influence on the future of social media. The next sections delve into each of the themes in Table 1 , organized around the predicted imminence of these theme’s importance to marketing (i.e., the immediate, near, and far futures).

The immediate future

To begin our discussion on the direction of social media, in this section, we highlight three themes that have surfaced in the current environment that we believe will continue to shape the social media landscape in the immediate future. These themes—omni-social presence, the rise of influencers, and trust and privacy concerns—reflect the ever-changing digital and social media landscape that we presently face. We believe that these different areas will influence a number of stakeholders such as individual social media users, firms and brands that utilize social media, and public policymakers (e.g., governments, regulators).

Omni-social presence

In its early days, social media activity was mostly confined to designated social media platforms such as Facebook and Twitter (or their now-defunct precursors). However, a proliferation of websites and applications that primarily serve separate purposes have capitalized on the opportunity to embed social media functionality into their interfaces. Similarly, all major mobile and desktop operating systems have in-built social media integration (e.g., sharing functions built into Apple’s iOS). This has made social media pervasive and ubiquitous—and perhaps even omnipotent—and has extended the ecosystem beyond dedicated platforms.

Accordingly, consumers live in a world in which social media intersects with most aspects of their lives through digitally enabled social interactivity in such domains as travel (e.g., TripAdvisor), work (e.g., LinkedIn), food (e.g., Yelp), music (e.g., Spotify), and more. At the same time, traditional social media companies have augmented their platforms to provide a broader array of functionalities and services (e.g., Facebook’s marketplace, Chowdry 2018 ; WeChat’s payment system, Cheng 2017 ). These bidirectional trends suggest that the modern-day consumer is living in an increasingly “omni-social” world.

From a marketing perspective, the “omni-social” nature of the present environment suggests that virtually every part of a consumer’s decision-making process is prone to social media influence. Need recognition might be activated when a consumer watches their favorite beauty influencer trying a new product on YouTube. A consumer shopping for a car might search for information by asking their Facebook friends what models they recommend. A hungry employee might sift through Yelp reviews to evaluate different lunch options. A traveler might use Airbnb to book future accommodation. Finally, a highly dissatisfied (or delighted) airline passenger might rant (rave) about their experience on Twitter. While the decision-making funnel is arguably growing flatter than the aforementioned examples would imply (Cortizo-Burgess 2014 ), these independent scenarios illustrate that social media has the propensity to influence the entire consumer-decision making process, from beginning to end.

Finally, perhaps the greatest indication of an “omni-social” phenomenon is the manner in which social media appears to be shaping culture itself. YouTube influencers are now cultural icons, with their own TV shows (Comm 2016 ) and product lines (McClure 2015 ). Creative content in television and movies is often deliberately designed to be “gifable” and meme-friendly (Bereznak 2018 ). “Made-for-Instagram museums” are encouraging artistic content and experiences that are optimized for selfie-taking and posting (Pardes 2017 ). These examples suggest that social media’s influence is hardly restricted to the “online” world (we discuss the potential obsolescence of this term later in this paper), but is rather consistently shaping cultural artifacts (television, film, the arts) that transcend its traditional boundaries. We believe this trend will continue to manifest, perhaps making the term “social media” itself out-of-date, as it’s omni-presence will be the default assumption for consumers, businesses, and artists in various domains.

This omni-social trend generates many questions to probe in future research. For example, how will social interactivity influence consumer behavior in areas that had traditionally been non-social? From a practitioner lens, it might also be interesting to explore how marketers can strategically address the flatter decision-making funnel that social media has enabled, and to examine how service providers can best alter experiential consumption when anticipating social media sharing behavior.

The rise of new forms of social influence (and influencers)

The idea of using celebrities (in consumer markets) or well-known opinion leaders (in business markets), who have a high social value, to influence others is a well-known marketing strategy (Knoll and Matthes 2017 ). However, the omnipresence of social media has tremendously increased the accessibility and appeal of this approach. For example, Selena Gomez has over 144 million followers on Instagram that she engages with each of her posts. In 2018, the exposure of a single photo shared by her was valued at $3.4 million (Maxim 2018 ). However, she comes at a high price: one post that Selena sponsors for a brand can cost upwards of $800,000 (Mejia 2018 ). However, putting high valuations on mere online exposures or collecting “likes” for specific posts can be somewhat speculative, as academic research shows that acquiring “likes” on social media might have no effect on consumers’ attitudes or behaviors (John et al. 2017 ; Mochon et al. 2017 ). Moreover, Hennig-Thurau et al. ( 2015 ), show that while garnering positive WOM has little to no effect on consumer preferences, negative WOM can have a negative effect on consumer preferences.

While celebrities like Selena Gomez are possible influencers for major brands, these traditional celebrities are so expensive that smaller brands have begun, and will continue to, capitalize on the popularity and success of what are referred to as “micro-influencers,” representing a new form of influencers. Micro-influencers are influencers who are not as well-known as celebrities, but who have strong and enthusiastic followings that are usually more targeted, amounting anywhere between a few thousand to hundreds of thousands of followers (Main 2017 ). In general, these types of influencers are considered to be more trustworthy and authentic than traditional celebrities, which is a major reason influencer marketing has grown increasingly appealing to brands (Enberg 2018 ). These individuals are often seen as credible “experts” in what they post about, encouraging others to want to view the content they create and engage with them. Furthermore, using these influencers allows the brand via first person narration (compared to ads), which is considered warmer and more personal, and was shown to be more effective in engaging consumers (Chang et al. 2019 ).

Considering the possible reach and engagement influencers command on social media, companies have either begun embracing influencers on social media, or plan to expand their efforts in this domain even more. For example, in recent conversations we had with social media executives, several of them stated the growing importance of influencers and mentioned how brands generally are looking to incorporate influencer marketing into their marketing strategies. Further, recent conversations with executives at some globally leading brands suggest that influencer marketing spending by big brands continues to rise.

While influencer marketing on social media is not new, we believe it has a lot of potential to develop further as an industry. In a recent working paper, Duani et al. ( 2018 ) show that consumers enjoy watching a live experience much more and for longer time periods than watching a prerecorded one. Hence, we think live streaming by influencers will continue to grow, in broad domains as well as niche ones. For example, streaming of video game playing on Twitch, a platform owned by Amazon, may still be niche but shows no signs of slowing down. However, live platforms are limited by the fact that the influencers, being human, need to sleep and do other activities offline. Virtual influencers (i.e., “CGI” influencers that look human but are not), on the other hand, have no such limitations. They never get tired or sick, they do not even eat (unless it is needed for a campaign). Some brands have started exploring the use of virtual influencers (Nolan 2018 ), and we believe that in coming years, along with stronger computing power and artificial intelligence algorithms, virtual influencers will become much more prominent on social media, being able to invariably represent and act on brand values and engage with followers anytime.

There are many interesting future research avenues to consider when thinking about the role of influencers on social media. First, determining what traits and qualities (e.g., authenticity, trust, credibility, and likability) make sponsored posts by a traditional celebrity influencer, versus a micro-influencer, or even compared to a CGI influencer, more or less successful is important to determine for marketers. Understanding whether success has to do with the actual influencer’s characteristics, the type of content being posted, whether content is sponsored or not, and so on, are all relevant concerns for companies and social media platforms when determining partnerships and where to invest effort in influencers. In addition, research can focus on understanding the appeal of live influencer content, and how to successfully blend influencer content with more traditional marketing mix approaches.

Privacy concerns on social media

Consumer concerns regarding data privacy, and their ability to trust brands and platforms are not new (for a review on data privacy see Martin and Murphy 2017 ). Research in marketing and related disciplines has examined privacy and trust concerns from multiple angles and using different definitions of privacy. For example, research has focused on the connections between personalization and privacy (e.g., Aguirre et al. 2015 ; White et al. 2008 ), the relationship of privacy as it relates to consumer trust and firm performance (e.g., Martin 2018 ; Martin et al. 2017 ), and the legal and ethical aspects of data and digital privacy (e.g., Culnan and Williams 2009 ; Nill and Aalberts 2014 ). Despite this topic not seeming novel, the way consumers, brands, policy makers, and social media platforms are all adjusting and adapting to these concerns are still in flux and without clear resolution.

Making our understanding of privacy concerns even less straightforward is the fact that, across extant literature, a clear definition of privacy is hard to come by. In one commentary on privacy, Stewart ( 2017 ), defined privacy as “being left alone,” as this allows an individual to determine invasions of privacy. We build from this definition of privacy to speculate on a major issue in privacy and trust moving forward. Specifically, how consumers are adapting and responding to the digital world, where “being left alone” isn’t possible. For example, while research has shown benefits to personalization tactics (e.g., Chung et al. 2016 ), with eroding trust in social platforms and brands that advertise through them, many consumers would rather not share data and privacy for a more personalized experiences, are uncomfortable with their purchases being tracked and think it should be illegal for brands to be able to buy their data (Edelman 2018 ). These recent findings seem to be in conflict with previously established work on consumer privacy expectations. Therefore, understanding if previously studied factors that mitigated the negative effects of personalization (e.g., perceived utility; White et al. 2008 ) are still valued by consumers in an ever-changing digital landscape is essential for future work.

In line with rising privacy concerns, the way consumers view brands and social media is becoming increasingly negative. Consumers are deleting their social media presence, where research has shown that nearly 40% of digitally connected individuals admitted to deleting at least one social media account due to fears of their personal data being mishandled (Edelman 2018 ). This is a negative trend not only for social media platforms, but for the brands and advertisers who have grown dependent on these avenues for reaching consumers. Edelman found that nearly half of the surveyed consumers believed brands to be complicit in negative aspects of content on social media such as hate speech, inappropriate content, or fake news (Edelman 2018 ). Considering that social media has become one of the best places for brands to engage with consumers, build relationships, and provide customer service, it’s not only in the best interest of social media platforms to “do better” in terms of policing content, but the onus of responsibility has been placed on brands to advocate for privacy, trust, and the removal of fake or hateful content.

Therefore, to combat these negative consumer beliefs, changes will need to be made by everyone who benefits from consumer engagement on social media. Social media platforms and brands need to consider three major concerns that are eroding consumer trust: personal information, intellectual property and information security (Information Technology Faculty 2018 ). Considering each of these concerns, specific actions and initiatives need to be taken for greater transparency and subsequent trust. We believe that brands and agencies need to hold social media accountable for their actions regarding consumer data (e.g., GDPR in the European Union) for consumers to feel “safe” and “in control,” two factors shown necessary in cases of privacy concerns (e.g., Tucker 2014 ; Xu et al. 2012 ). As well, brands need to establish transparent policies regarding consumer data in a way that recognizes the laws, advertising restrictions, and a consumer’s right to privacy (a view shared by others; e.g., Martin et al. 2017 ). All of this is managerially essential for brands to engender feelings of trust in the increasingly murky domain of social media.

Future research can be conducted to determine consumer reactions to different types of changes and policies regarding data and privacy. As well, another related and important direction for future research, will be to ascertain the spillover effects of distrust on social media. Specifically, is all content shared on social media seen as less trustworthy if the platform itself is distrusted? Does this extend to brand messages displayed online? Is there a negative spillover effect to other user-generated content shared through these platforms?

The near future

In the previous section, we discussed three areas where we believe social media is immediately in flux. In this section, we identify three trends that have shown early signs of manifesting, and which we believe will meaningfully alter the social media landscape in the near, or not-too-distant, future. Each of these topics impact the stakeholders we mentioned when discussing the immediate social media landscape.

Combatting loneliness and isolation

Social media has made it easier to reach people. When Facebook was founded in 2004, their mission was “to give people the power to build community and bring the world closer together... use Facebook to stay connected with friends and family, to discover what’s going on in the world, and to share and express what matters to them” (Facebook 2019 ). Despite this mission, and the reality that users are more “connected” to other people than ever before, loneliness and isolation are on the rise. Over the last fifty years in the U.S., loneliness and isolation rates have doubled, with Generation Z considered to be the loneliest generation (Cigna 2018 ). Considering these findings with the rise of social media, is the fear that Facebook is interfering with real friendships and ironically spreading the isolation it was designed to conquer something to be considered about (Marche 2012 )?

The role of social media in this “loneliness epidemic” is being hotly debated. Some research has shown that social media negatively impacts consumer well-being. Specifically, heavy social media use has been associated with higher perceived social isolation, loneliness, and depression (Kross et al. 2013 ; Primack et al. 2017 ; Steers et al. 2014 ). Additionally, Facebook use has been shown to be negatively correlated with consumer well-being (Shakya and Christakis 2017 ) and correlational research has shown that limiting social media use to 10 min can decrease feelings of loneliness and depression due to less FOMO (e.g., “fear of missing out;” Hunt et al. 2018 ).

On the other hand, research has shown that social media use alone is not a predictor of loneliness as other factors have to be considered (Cigna 2018 ; Kim et al. 2009 ). In fact, while some research has shown no effect of social media on well-being (Orben et al. 2019 ), other research has shown that social media can benefit individuals through a number of different avenues such as teaching and developing socialization skills, allowing greater communication and access to a greater wealth of resources, and helping with connection and belonging (American Psychological Association 2011 ; Baker and Algorta 2016 ; Marker et al. 2018 ). As well, a working paper by Crolic et al. ( 2019 ) argues that much of the evidence of social media use on consumer well-being is of questionable quality (e.g., small and non-representative samples, reliance on self-reported social media use), and show that some types of social media use are positively associated with psychological well-being over time.

Managerially speaking, companies are beginning to respond as a repercussion of studies highlighting a negative relationship between social media and negative wellbeing. For example, Facebook has created “time limit” tools (mobile operating systems, such as iOS, now also have these time-limiting features). Specifically, users can now check their daily times, set up reminder alerts that pop up when a self-imposed amount of time on the apps is hit, and there is the option to mute notifications for a set period of time (Priday 2018 ). These different features seem well-intentioned and are designed to try and give people a more positive social media experience. Whether these features will be used is unknown.

Future research can address whether or not consumers will use available “timing” tools on one of many devices in which their social media exists (i.e., fake self-policing) or on all of their devices to actually curb behavior. It could also be the case that users will actually spend less time on Facebook and Instagram, but possibly spend that extra time on other competing social media platforms, or attached to devices, which theoretically will not help combat loneliness. Understanding how (and which) consumers use these self-control tools and how impactful they are is a potentially valuable avenue for future research.

One aspect of social media that has yet to be considered in the loneliness discussion through empirical measures, is the quality of use (versus quantity). Facebook ads have begun saying, “The best part of Facebook isn’t on Facebook. It’s when it helps us get together” (Facebook 2019 ). There have been discussions around the authenticity of this type of message, but at its core, in addition to promoting quantity differences, it’s speaking to how consumers use the platform. Possibly, to facilitate this message, social media platforms will find new ways to create friend suggestions between individuals who not only share similar interests and mutual friends to facilitate in-person friendships (e.g., locational data from the mobile app service). Currently there are apps that allow people to search for friends that are physically close (e.g., Bumble Friends), and perhaps social media will go in this same direction to address the loneliness epidemic and stay current.

Future research can examine whether the quantity of use, types of social media platforms, or the way social media is used causally impacts perceived loneliness. Specifically, understanding if the negative correlations found between social media use and well-being are due to the demographics of individuals who use a lot of social media, the way social media works, or the way users choose to engage with the platform will be important for understanding social media’s role (or lack of role) in the loneliness epidemic.

Integrated customer care

Customer care via digital channels as we know it is going to change substantially in the near future. To date, many brands have used social media platforms as a place for providing customer care, addressing customers’ specific questions, and fixing problems. In the future, social media-based customer care is expected to become even more customized, personalized, and ubiquitous. Customers will be able to engage with firms anywhere and anytime, and solutions to customers’ problems will be more accessible and immediate, perhaps even pre-emptive using predictive approaches (i.e., before a customer even notices an issue or has a question pop into their mind).

Even today, we observe the benefits that companies gain from connecting with customers on social media for service- or care-related purposes. Customer care is implemented in dedicated smartphone apps and via direct messaging on social media platforms. However, it appears that firms want to make it even easier for customers to connect with them whenever and wherever they might need. Requiring a customer to download a brand specific app or to search through various social media platforms to connect with firms through the right branded account on a platform can be a cumbersome process. In those cases, customers might instead churn or engage in negative WOM, instead of connecting with the firm to bring up any troubles they might have.

The near future of customer care on social media appears to be more efficient and far-reaching. In a recent review on the future of customer relationship management, Haenlein ( 2017 ) describes “invisible CRM” as future systems that will make customer engagement simple and accessible for customers. New platforms have emerged to make the connection between customer and firm effortless. Much of this is via instant messaging applications for businesses, which several leading technology companies have recently launched as business-related features in existing platforms (e.g., contact business features in Facebook Messenger and WhatsApp or Apple’s Business Chat).

These technologies allow businesses to directly communicate via social media messaging services with their customers. Amazon, Apple, Facebook, and Google are in the process, or have already released early versions of such platforms (Dequier 2018 ). Customers can message a company, ask them questions, or even order products and services through the messaging system, which is often built around chatbots and virtual assistants. This practice is expected to become more widespread, especially because it puts brands and companies into the social media messaging platforms their customers already use to communicate with others, it provides quicker—even instantaneous—responses, is economically scalable through the use of AI-driven chatbots, and, despite the use of chatbots, can provide a more personalized level of customer service.

Another area that companies will greatly improve upon is data collection and analysis. While it is true that data collection on social media is already pervasive today, it is also heavily scrutinized. However, we believe that companies will adapt to the latest regulation changes (e.g., GDPR in Europe, CCPA in California) and improve on collecting and analyzing anonymized data (Kakatkar and Spann 2018 ). Furthermore, even under these new regulations, personalized data collection is still allowed, but severely limits firm’s abilities to exploit consumers’ data, and requires their consent for data collection.

We believe that in the future, companies will be able recognize early indications of problems within customer chatter, behavior, or even physiological data (e.g., monitoring the sensors in our smart watches) before customers themselves even realize they are experiencing a problem. For example, WeWork, the shared workspace company, collects data on how workers move and act in a workspace, building highly personalized workspaces based on trends in the data. Taking this type of approach to customer care will enable “seamless service,” where companies would be able to identify and address consumer problems when they are still small and scattered, and while only a small number of customers are experiencing problems. Customer healthcare is a pioneer in this area, where using twitter and review sites were shown to predict poor healthcare quality (Greaves et al. 2013 ), listen to patients to analyze trending terms (Baktha et al. 2017 ; Padrez et al. 2016 ), or even predict disease outbreaks (Schmidt 2012 ).

Companies, wanting to better understand and mimic human interactions, will invest a lot of R&D efforts into developing better Natural Language Processing, voice and image recognition, emotional analysis, and speech synthesis tools (Sheth 2017 ). For example, Duplex, Google’s latest AI assistant, can already call services on its own and seamlessly book reservations for their users (Welch 2018 ). In the future, AI systems will act as human ability augmenters, allowing us to accomplish more, in less time, and better results (Guszcza 2018 ).

For marketers, this will reduce the need for call centers and agents, reducing points of friction in service and increasing the convenience for customers (Kaplan and Haenlein 2019 ). However, some raise the question that the increased dependence on automation may result in a loss of compassion and empathy. In a recent study, Force (2018) shows that interacting with brands on social media lowered people’s empathy. In response to such concerns, and to educate and incentivize people to interact with machines in a similar way they do with people, Google programmed their AI assistant to respond in a nicer way if you use a polite, rather than a commanding approach (Kumparak 2018 ). While this might help, more research is needed to understand the effect of an AI rich world on human behavior. As well, future research can examine how consumer generated data can help companies preemptively predict consumer distress. Another interesting path for research would be to better understand the difference in consumer engagement between the various platforms, and the long-term effects of service communications with non-human AI and IoT.

Social media as a political tool

Social media is a platform to share thoughts and opinions. This is especially true in the case of disseminating political sentiments. Famously, President Barack Obama’s victory in the 2008 election was partially attributed to his ability to drive and engage voters on social media (Carr 2008 ). Indeed, Bond et al. ( 2012 ) have shown that with simple interventions, social media platforms can increase targeted audiences’ likelihood of voting. Social media is considered one of the major drivers of the 2010 wave of revolutions in Arab countries, also known as the Arab Spring (Brown et al. 2012 ).

While social media is not new to politics, we believe that social media is transitioning to take a much larger role as a political tool in the intermediate future. First evidence for this could be seen in the 2016 U.S. presidential election, as social media took on a different shape, with many purported attempts to influence voter’s opinions, thoughts, and actions. This is especially true for then-candidate and now-President Donald Trump. His use of Twitter attracted a lot of attention during the campaign and has continued to do so during his term in office. Yet, he is not alone, and many politicians changed the way they work and interact with constituents, with a recent example of Congresswoman Alexandria Ocasio-Cortez that even ran a workshop for fellow congress members on social media (Dwyer 2019 ).

While such platforms allow for a rapid dissemination of ideas and concepts (Bonilla and Rosa 2015 ; Bode 2016 ), there are some, both in academia and industry that have raised ethical concerns about using social media for political purposes. Given that people choose who to follow, this selective behavior is said to potentially create echo chambers, wherein, users are exposed only to ideas by like-minded people, exhibiting increased political homophily (Bakshy et al. 2015 ). People’s preference to group with like-minded people is not new. Social in-groups have been shown to promote social identification and promote in-group members to conform to similar ideas (Castano et al. 2002 ; Harton and Bourgeois 2004 ). Furthermore, it was also shown that group members strongly disassociate and distance themselves from outgroup members (Berger and Heath 2008 ; White and Dahl 2007 ). Thus, it is not surprising to find that customized newsfeeds within social media exacerbate this problem by generating news coverage that is unique to specific users, locking them in their purported echo chambers (Oremus 2016 ).

While social media platforms admit that echo chambers could pose a problem, a solution is not clear (Fiegerman 2018 ). One reason that echo chambers present such a problem, is their proneness to fake news. Fake news are fabricated stories that try to disguise themselves as authentic content, in order to affect other social media users. Fake news was widely used in the 2016 U.S. elections, with accusations that foreign governments, such as Iran and Russia, were using bots (i.e., online automatic algorithms), to spread falsified content attacking Hillary Clinton and supporting President Trump (Kelly et al. 2018 ). Recent research has furthermore shown how the Chinese government strategically uses millions of online comments to distract the Chinese public from discussing sensitive issues and promote nationalism (King et al. 2017 ). In their latest incarnation, fake news uses an advanced AI technique called “Deep Fake” to generate ultra-realistic forged images and videos of political leaders while manipulating what those leaders say (Schwartz 2018 ). Such methods can easily fool even the sharpest viewer. In response, research has begun to explore ways that social media platforms can combat fake news through algorithms that determine the quality of shared content (e.g., Pennycook and Rand 2019 ).

One factor that has helped the rise of fake news is echo chambers. This occurs as the repeated sharing of fake news by group members enhance familiarity and support (Schwarz and Newman 2017 ). Repetition of such articles by bots can only increase that effect. Recent research has shown that in a perceived social setting, such as social media, participants were less likely to fact-check information (Jun et al. 2017 ), and avoided information that didn’t fit well with their intuition (Woolley and Risen 2018 ). Schwarz and Newman ( 2017 ) state that misinformation might be difficult to correct, especially if the correction is not issued immediately and the fake news has already settled into the minds of users. It was also shown that even a single exposure to fake news can create long term effect on users, making their effect larger than previously thought (Pennycook et al. 2019 ).

Notably, some research has found that exposure to opposing views (i.e., removing online echo chambers) may in fact increase (versus decrease) polarization (Bail et al. 2018 ). Accordingly, more work from policy makers, businesses, and academics is needed to understand and potentially combat political extremism. For example, policy makers and social media platforms will continually be challenged to fight “fake news” without censoring free speech. Accordingly, research that weighs the risk of limited freedom of expression versus the harms of spreading fake news would yield both theoretical and practically meaningful insights.

The far future

In this section, we highlight three emerging trends we believe will have a have long-term influence on the future of social media. Note that although we label these trends as being in the “far” future, many of the issues described here are already present or emerging. However, they represent more complex issues that we believe will take longer to address and be of mainstream importance for marketing than the six issues discussed previously under the immediate and near futures.

Increased sensory richness

In its early days, the majority of social media posts (e.g., on Facebook, Twitter) were text. Soon, these platforms allowed for the posting of pictures and then videos, and separate platforms dedicated themselves to focus on these specific forms of media (e.g., Instagram and Pinterest for pictures, Instagram and SnapChat for short videos). These shifts have had demonstrable consequences on social media usage and its consequences as some scholars suggest that image-based posts convey greater social presence than text alone (e.g., Pittman and Reich 2016 ). Importantly however, a plethora of new technologies in the market suggest that the future of social media will be more sensory-rich.

One notable technology that has already started infiltrating social media is augmented reality (AR). Perhaps the most recognizable examples of this are Snapchat’s filters, which use a device’s camera to superimpose real-time visual and/or video overlays on people’s faces (including features such as makeup, dog ears, etc.). The company has even launched filters to specifically be used on users’ cats (Ritschel 2018 ). Other social media players quickly joined the AR bandwagon, including Instagram’s recent adoption of AR filters (Rao 2017 ) and Apple’s Memoji messaging (Tillman 2018 ). This likely represents only the tip of the iceberg, particularly given that Facebook, one of the industry’s largest investors in AR technology, has confirmed it is working on AR glasses (Constine 2018 ). Notably, the company plans to launch a developer platform, so that people can build augmented-reality features that live inside Facebook, Instagram, Messenger and Whatsapp (Wagner 2017 ). These developments are supported by academic research suggesting that AR often provides more authentic (and hence positive) situated experiences (Hilken et al. 2017 ). Accordingly, whether viewed through glasses or through traditional mobile and tablet devices, the future of social media is likely to look much more visually augmented.

While AR allows users to interact within their current environments, virtual reality (VR) immerses the user in other places, and this technology is also likely to increasingly permeate social media interactions. While the Facebook-owned company Oculus VR has mostly been focusing on the areas of immersive gaming and film, the company recently announced the launch of Oculus Rooms where users can spend time with other users in a virtual world (playing games together, watching media together, or just chatting; Wagner 2018 ). Concurrently, Facebook Spaces allows friends to meet online in virtual reality and similarly engage with one another, with the added ability to share content (e.g., photos) from their Facebook profiles (Whigham 2018 ). In both cases, avatars are customized to represent users within the VR-created space. As VR technology is becoming more affordable and mainstream (Colville 2018 ) we believe social media will inevitably play a role in the technology’s increasing usage.

While AR and VR technologies bring visual richness, other developments suggest that the future of social media might also be more audible. A new player to the social media space, HearMeOut, recently introduced a platform that enables users to share and listen to 42-s audio posts (Perry 2018 ). Allowing users to use social media in a hands-free and eyes-free manner not only allows them to safely interact with social media when multitasking (particularly when driving), but voice is also said to add a certain richness and authenticity that is often missing from mere text-based posts (Katai 2018 ). Given that podcasts are more popular than ever before (Bhaskar 2018 ) and voice-based search queries are the fastest-growing mobile search type (Robbio 2018 ), it seems likely that this communication modality will accordingly show up more on social media use going forward.

Finally, there are early indications that social media might literally feel different in the future. As mobile phones are held in one’s hands and wearable technology is strapped onto one’s skin, companies and brands are exploring opportunities to communicate to users through touch. Indeed, haptic feedback (technology that recreates the sense of touch by applying forces, vibrations, or motions to the user; Brave et al. 2001 ) is increasingly being integrated into interfaces and applications, with purposes that go beyond mere call or message notifications. For example, some companies are experimenting with integrating haptics into media content (e.g., in mobile ads for Stoli vodka, users feel their phone shake as a woman shakes a cocktail; Johnson 2015 ), mobile games, and interpersonal chat (e.g., an app called Mumble! translates text messages into haptic outputs; Ozcivelek 2015 ). Given the high levels of investment into haptic technology (it is predicted to be a $20 billion industry by 2022; Magnarelli 2018 ) and the communicative benefits that stem from haptic engagement (Haans and IJsselsteijn 2006 ), we believe it is only a matter of time before this modality is integrated into social media platforms.

Future research might explore how any of the new sensory formats mentioned above might alter the nature of content creation and consumption. Substantively-focused researchers might also investigate how practitioners can use these tools to enhance their offerings and augment their interactions with customers. It is also interesting to consider how such sensory-rich formats can be used to bridge the gap between the online and offline spaces, which is the next theme we explore.

Online/offline integration and complete convergence

A discussion occurring across industry and academia is on how marketers can appropriately integrate online and offline efforts (i.e., an omnichannel approach). Reports from industry sources have shown that consumers respond better to integrated marketing campaigns (e.g., a 73% boost over standard email campaigns; Safko 2010 ). In academia meanwhile, the majority of research considering online promotions and advertisements has typically focused on how consumers respond to these strategies through online only measures (e.g., Manchanda et al. 2006 ), though this has begun to change in recent years with more research examining offline consequences to omnichannel strategies (Lobschat et al. 2017 ; Kumar et al. 2017 ).

Considering the interest in integrated marketing strategies over the last few years, numerous strategies have been utilized to follow online and offline promotions and their impacts on behavior such as the usage of hashtags to bring conversations online, call-to-actions, utilizing matching strategies on “traditional” avenues like television with social media. While there is currently online/offline integration strategies in marketing, we believe the future will go even further in blurring the lines between what is offline and online to not just increase the effectiveness of marketing promotions, but to completely change the way customers and companies interact with one another, and the way social media influences consumer behavior not only online, but offline.

For brands, there are a number of possible trends in omnichannel marketing that are pertinent. As mentioned earlier, a notable technology that has begun infiltrating social media is augmented reality (AR). In addition to what already exists (e.g., Snapchat’s filters, Pokémon Go), the future holds even more possibilities. For example, Ikea has been working to create an AR app that allows users to take photos of a space at home to exactly , down to the millimeter size and lighting in the room, showcase what a piece of furniture would look like in a consumer’s home (Lovejoy 2017 ). Another set of examples of AR comes from beauty company L’Oréal. In 2014 for the flagship L’Oréal Paris brand they released a mobile app called Makeup Genius that allowed consumers to virtually try on makeup on their phones (Stephen and Brooks 2018 ). Since then, they have developed AR apps for hair color and nail polish, as well as integrating AR into mobile ecommerce webpages for their luxury beauty brand Lancôme. AR-based digital services such as these are likely to be at the heart of the next stage of offline/online integration.

AR, and similar technology, will likely move above and beyond being a tool to help consumers make better decisions about their purchases. Conceivably, similar to promotions that currently exist to excitse consumers and create communities, AR will be incorporated into promotions that integrate offline and online actions. For example, contests on social media will advance to the stage where users get to vote on the best use of AR technology in conjunction with a brand’s products (e.g., instead of users submitting pictures of their apartments to show why they should win free furniture, they could use AR to show how they would lay out the furniture if they were to win it from IKEA).

Another way that the future of online/offline integration on social media needs to be discussed is in the sense of a digital self. Drawing on the extended self in the digital age (Belk 2013 ), the way consumers consider online actions as relevant to their offline selves may be changing. For example, Belk ( 2013 ) spoke of how consumers may be re-embodied through avatars they create to represent themselves online, influencing their offline selves and creating a multiplicity of selves (i.e., consumers have more choice when it comes to their self-representation). As research has shown how digital and social media can be used for self-presentation, affiliation, and expression (Back et al. 2010 ; Gosling et al. 2007 ; Toubia and Stephen 2013 ; Wilcox and Stephen 2012 ), what does it mean for the future if consumers can create who they want to be?

In addition, when considering digital selves, what does this mean for how consumers engage with brands and products? Currently, social media practice is one where brands encourage consumer engagement online (Chae et al. 2017 ; Godes and Mayzlin 2009 ), yet the implications for how these types of actions on the part of the brand to integrate online social media actions and real-life behavior play out are unclear. Research has begun to delve into the individual-level consequences of a consumer’s social media actions on marketing relevant outcomes (Grewal et al. 2019 ; John et al. 2017 ; Mochon et al. 2017 ; Zhang et al. 2017 ), however much is still unknown. As well, while there is recent work examining how the device used to create and view content online impacts consumer perceptions and behaviors (e.g., Grewal and Stephen 2019 ), to date research has not examined these questions in the context of social media. Therefore, future research could address how digital selves (both those held offline and those that only exist online), social media actions, and if the way consumers reach and use various platforms (i.e., device type, app vs. webpage, etc.) impact consumer behavior, interpersonal relationships, and brand-related measures (e.g., well-being, loyalty, purchase behaviors).

Social media by non-humans

The buzz surrounding AI has not escaped social media. Indeed, social bots (computer algorithms that automatically produce content and interact with social media users; Ferrara et al. 2016 ) have inhabited social media platforms for the last decade (Lee et al. 2011 ), and have become increasingly pervasive. For example, experts estimate that up to 15% of active Twitter accounts are bots (Varol et al. 2017 ), and that percentage appears to be on the rise (Romano 2018 ). While academics and practitioners are highly concerned with bot detection (Knight 2018 ), in the vast majority of current cases, users do not appear to recognize when they are interacting with bots (as opposed to other human users) on social media (Stocking and Sumida 2018 ). While some of these bots are said to be benign, and even useful (e.g., acting as information aggregators), they have also been shown to disrupt political discourse (as mentioned earlier), steal personal information, and spread misinformation (Ferrara et al. 2016 ).

Of course, social bots are not only a problem for social media users but are also a nagging concern plaguing marketers. Given that companies often assess marketing success on social media through metrics like Likes, Shares, and Clicks, the existence of bots poses a growing threat to accurate marketing metrics and methods for ROI estimation, such as attribution modelling (Bilton 2014 ). Similarly, when these bots act as “fake followers,” it can inflate the worth of influencers’ audiences (Bogost 2018 ). This can also be used nefariously by individuals and firms, as shown in a New York Times Magazine expose that documented the market used by some influencers to purchase such “fake” followers to inflate their social media reach (Confessore et al. 2018 ). As discussed above in relation to influencer marketing, where it has been commonplace for influencers to be paid for posts at rates proportionate to their follower counts, there have been perverse incentives to game the system by having non-human “fake” bot followers. This, however, erodes consumer trust in the social media ecosystem, which is a growing issue and a near-term problem for many firms using social media channels for marketing purposes.

However, there are instances when consumers do know they are interacting with bots, and do not seem to mind. For example, a number of virtual influencers (created with CGI, as mentioned earlier) seem to be garnering sizeable audiences, despite the fact they are clearly non-human (Walker 2018 ). One of the most popular of these virtual influencers, Lil Miquela, has over 1.5 million followers on Instagram despite openly confessing, “I am not a human being... I’m a robot” (Yurieff 2018 ). Future research might try to understand the underlying appeal of these virtual influencers, and the potential boundary conditions of their success.

Another category of social bots gaining increasing attention are therapy bots. These applications (e.g., “Woebot;” Molteni 2017 ) aim to support the mental health of users by proactively checking in on them, “listening” and chatting to users at any time and recommending activities to improve users’ wellbeing (de Jesus 2018 ). Similar bots are being used to “coach” users, and help them quit maladaptive behaviors, like smoking (e.g., QuitGenius; Crook 2018 ). Interestingly, by being explicitly non-human, these agents are perceived to be less judgmental, and might accordingly be easier for users to confide in.

Finally, the Internet of Things revolution has ushered in with it the opportunity for a number of tangible products and interfaces to “communicate” via social media. For example, in what started as a design experiment, “Brad,” a connected toaster, was given the ability to “communicate” with other connected toasters, and to tweet his “feelings” when neglected or under-used (Vanhemert 2014 ). While this experiment was deliberately designed to raise questions about the future of consumer-product relationships (and product-product “relationships”), the proliferation of autonomous tangible devices does suggest a future in which they have a “voice,” even in the absence of humans (Hoffman and Novak 2018 ).

Going forward, we believe the presence of bots on social media will be more normalized, but also more regulated (e.g., a recent law passed in California prevents bots from masquerading as humans; Smith 2018 ). Further, consumers and companies alike will be become increasingly interested in how bots communicate and interact with each other outside of human involvement. This brings up interesting potential research questions for academics and practitioners alike. How will the presence of non-humans change the nature of content creation and conversation in social media? And how should companies best account for the presence of non-humans in their attribution models?

Future research directions and conclusion

This article has presented nine themes pertinent to the future of social media as it relates to (and is perhaps influenced by) marketing. The themes have implications for individuals/consumers, businesses and organizations, and also public policymakers and governments. These themes, which represent our own thinking and a synthesis of views from extant research, industry experts, and popular public discourse, are of course not the full story of what the future of social media will entail. They are, however, a set of important issues that we believe will be worth considering in both academic research and marketing practice.

To stimulate future research on these themes and related topics, we present a summary of suggested research directions in Table 2 . These are organized around our nine themes and capture many of the suggested research directions mentioned earlier. As a sub-field within the field of marketing, social media is already substantial and the potential for future research—based on identified needs for new knowledge and answers to perplexing questions—suggests that this sub-field will become even more important over time. We encourage researchers to consider the kinds of research directions in Table 2 as examples of issues they could explore further. We also encourage researchers in marketing to treat social media as a place where interesting (and often very new) consumer behaviors exist and can be studied. As we discussed earlier in the paper, social media as a set of platform businesses and technologies is interesting, but it is how people use social media and the associated technologies that is ultimately of interest to marketing academics and practitioners. Thus, we urge scholars to not be overly enticed by the technological “shiny new toys” at the expense of considering the behaviors associated with those technologies and platforms.

Finally, while we relied heavily (though not exclusively) on North American examples to illustrate the emergent themes, there are likely interesting insights to be drawn by explicitly exploring cross-cultural differences in social media usage. For example, variations in regulatory policies (e.g., GDPR in the European Union) may lead to meaningful differences in how trust and privacy concerns manifest. Further, social media as a political tool might be more influential in regions where the mainstream media is notoriously government controlled and censored (e.g., as was the case in many of the Arab Spring countries). While such cross-cultural variation is outside the scope of this particular paper, we believe it represents an area of future research with great theoretical and practical value.

In reviewing the social media ecosystem and considering where it is heading in the context of consumers and marketing practice, we have concluded that this is an area that is very much still in a state of flux. The future of social media in marketing is exciting, but also uncertain. If nothing else, it is vitally important that we better understand social media since it has become highly culturally relevant, a dominant form of communication and expression, a major media type used by companies for advertising and other forms of communication, and even has geopolitical ramifications. We hope that the ideas discussed here stimulate many new ideas and research, which we ultimately hope to see being mentioned and shared across every type of social media platform.

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The authors thank the special issue editors and reviewers for their comments, and the Oxford Future of Marketing Initiative for supporting this research. The authors contributed equally and are listed in alphabetical order or, if preferred, order of Marvel superhero fandom from highest to lowest and order of Bon Jovi fandom from lowest to highest.

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Appel, G., Grewal, L., Hadi, R. et al. The future of social media in marketing. J. of the Acad. Mark. Sci. 48 , 79–95 (2020). https://doi.org/10.1007/s11747-019-00695-1

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• Florida Realtors News

Define and Deploy Your Video Marketing Strategy

Video marketing helps create a strong online presence, which is essential to buyers who use the internet. The first step to making great videos is getting past fear.  

CHICAGO – Just like 360-degree tours, using video as part of your overall marketing strategy isn’t a luxury anymore. It’s a necessity.

Video marketing is the cornerstone of a strong online presence, which is essential since about 97% of buyers use the internet in their home search, according to a National Association of Realtors® (NAR) report.  Adding consistent, branded and valuable video content can help you stand out from the competition — which is urgent, especially in an ever-changing market — said Los Angeles real estate broker Ivan Estrada.

“Video completely changed my business,” Estrada said.

He’s a well-known video expert and top producer, and his YouTube channel is chock-full of resources for buyers, sellers and industry professionals. He’s covered topics like the future of Los Angeles transportation and what fees tenants will pay when renting in the city. He also does walk-throughs of homes and provides market projections.

“Video is no longer a piece of the marketing,” he said. “Video is the marketing.”

Still, Estrada admitted that it’s taken time to build up his online video library, and he encouraged attendees to start doing video at a pace that feels manageable to them. Estrada began by answering some important personal and business questions and creating a marketing plan.

Overcoming fear

“Video is vulnerable,” Estrada told the crowd. He added that negative self-talk is common when creating video content.

Often, people don’t like the sound of their voice or how they look on camera. Breaking down your team’s fears and overcoming the vulnerability together is step one. The main fears people have when it comes to video content are:

• They don’t like how they look.
• They don’t like how they sound.
• They’re afraid of public scrutiny and judgment.

To help with the fear of public scrutiny and judgment, Estrada had his team of eight meet in the office every so often for confidence-building exercises. For example, Estrada might have one of his team members stand at the front of the room while the others clapped loudly and cheered him or her on. Estrada said he noticed each team member becoming more confident in front of others and building a stronger support system among each other.

Next, Estrada had his team members commit to keeping a personal video diary on their smartphones. For three days, each team member had to produce a three-minute video. They didn’t have to share it with the group; they just had to create one.

“As time went on and we kept doing this, the agents became more authentic and comfortable in front of the camera,” Estrada said.

Finally, he suggested a mirror exercise to get comfortable looking at yourself. “Look at yourself in the mirror for 10 minutes per day. This allows you to connect with yourself over the course of time.”

It’s important to take an objective stance for this practice, he said. The goal is not to judge your reflection but rather to connect with that reflection in the mirror.

Identifying brand pillars

Knowing your brokerage’s most important values is key to achieving a consistent message and presence in your video marketing, said Estrada. This isn’t just for brokers, though. As an agent, it’s important to know your brokerage’s brand pillars for your video content, and it’s equally as important to figure out your own brand pillars. Figure out who you are as an agent. What expertise do you have, and what do you want to be known for in the business?

“Pillars solidify the voice of a company and tell people who you are and what you stand for as a brand,” he explained. “Figure out what your brand pillars are and create content based on that. Do not stray outside of those pillars.”

This, he said, ensures your marketing videos are consistently on-brand. Let’s say your brokerage wants to focus on creating content around market data, local happenings, and home design. Here’s the breakdown Estrada gave as an example for each of these pillars:

Real estate market content

• Market updates
• Celebrity homes (which is relevant to Estrada’s L.A. market)

Local area content

• Restaurants and bars
• Art galleries
• Family-friendly events
• Interior design trends
• Curb appeal
• Smart home technology

Estrada says breaking down each pillar into different subjects creates a roadmap for your video content.

Educational: Answer common questions about the home buying and selling process. Give specific information about local laws and ordinances. What’s most important, though, is that your videos provide information about the types of properties in which your brokerage specializes. “Stay in your lane,” said Estrada. “We’re not CPAs or lawyers.”

Interviews: If your goal is to create content about the local area, interview a restaurant or shop owner. If you want to provide useful information outside your professional expertise, consider interviewing a CPA or lawyer on a particular subject that would be of interest to your audience.

Day in the Life: “Audiences love [peeking] behind the scenes,” said Estrada. Consider giving the audience a look into what it takes to be a real estate agent. How do you organize your day or get ready for an open house?

Home tours: Make a video about the distinctive features of a listing, or do a walkthrough. Estrada said a simple video using a camera phone works well for social media.

Take a look at your pillars and choose a couple of video styles that would best complement those pillars.

Video content is one of the most effective ways to market right now, Estrada said.

“Video is a must to stay relevant and the number one way to build your brand. It’s also inexpensive to get started and doesn’t have to be perfect.”

© 2024 National Association of Realtors® (NAR)

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After 40 Years, How Representative Are Labor Market Outcomes in the NLSY79?

In 1979, the National Longitudinal Study of Youth 1979 (NLSY79) began following a group of US residents born between 1957 and 1964. It has continued to re-interview these same individuals for more than four decades. Despite this long sampling period, attrition remains modest. This paper shows that after 40 years of data collection, the remaining NLYS79 sample continues to be broadly representative of their national cohorts with regard to key labor market outcomes. For NLSY79 age cohorts, life-cycle profiles of employment, hours worked, and earnings are comparable to those in the Current Population Survey. Moreover, average lifetime earnings over the age range 25 to 55 closely align with the same measure in Social Security Administration data. Our results suggest that the NLSY79 can continue to provide useful data for economists and other social scientists studying life-cycle and lifetime labor market outcomes, including earnings inequality.

We thank Kevin Bloodworth II, Elizabeth Harding, and Siyu Shi for research assistance. The views in this paper are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or of the National Bureau of Economic Research.

Richard Rogerson acknowledges financial support in excess of $10,000 over the last three years from the Federal Reserve Bank of Atlanta, the Federal Reserve Bank of Minneapolis and the World Bank.

MARC RIS BibTeΧ

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About 1 in 5 U.S. teens who’ve heard of ChatGPT have used it for schoolwork

Roughly one-in-five teenagers who have heard of ChatGPT say they have used it to help them do their schoolwork, according to a new Pew Research Center survey of U.S. teens ages 13 to 17. With a majority of teens having heard of ChatGPT, that amounts to 13% of all U.S. teens who have used the generative artificial intelligence (AI) chatbot in their schoolwork.

Teens in higher grade levels are particularly likely to have used the chatbot to help them with schoolwork. About one-quarter of 11th and 12th graders who have heard of ChatGPT say they have done this. This share drops to 17% among 9th and 10th graders and 12% among 7th and 8th graders.

There is no significant difference between teen boys and girls who have used ChatGPT in this way.

The introduction of ChatGPT last year has led to much discussion about its role in schools , especially whether schools should integrate the new technology into the classroom or ban it .

Pew Research Center conducted this analysis to understand American teens’ use and understanding of ChatGPT in the school setting.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, via Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the  questions used for this analysis , along with responses, and its  methodology .

Teens’ awareness of ChatGPT

Overall, two-thirds of U.S. teens say they have heard of ChatGPT, including 23% who have heard a lot about it. But awareness varies by race and ethnicity, as well as by household income:

• 72% of White teens say they’ve heard at least a little about ChatGPT, compared with 63% of Hispanic teens and 56% of Black teens.
• 75% of teens living in households that make $75,000 or more annually have heard of ChatGPT. Much smaller shares in households with incomes between $30,000 and $74,999 (58%) and less than $30,000 (41%) say the same.

Teens who are more aware of ChatGPT are more likely to use it for schoolwork. Roughly a third of teens who have heard a lot about ChatGPT (36%) have used it for schoolwork, far higher than the 10% among those who have heard a little about it.

When do teens think it’s OK for students to use ChatGPT?

For teens, whether it is – or is not – acceptable for students to use ChatGPT depends on what it is being used for.

There is a fair amount of support for using the chatbot to explore a topic. Roughly seven-in-ten teens who have heard of ChatGPT say it’s acceptable to use when they are researching something new, while 13% say it is not acceptable.

However, there is much less support for using ChatGPT to do the work itself. Just one-in-five teens who have heard of ChatGPT say it’s acceptable to use it to write essays, while 57% say it is not acceptable. And 39% say it’s acceptable to use ChatGPT to solve math problems, while a similar share of teens (36%) say it’s not acceptable.

Some teens are uncertain about whether it’s acceptable to use ChatGPT for these tasks. Between 18% and 24% say they aren’t sure whether these are acceptable use cases for ChatGPT.

Those who have heard a lot about ChatGPT are more likely than those who have only heard a little about it to say it’s acceptable to use the chatbot to research topics, solve math problems and write essays. For instance, 54% of teens who have heard a lot about ChatGPT say it’s acceptable to use it to solve math problems, compared with 32% among those who have heard a little about it.

Note: Here are the  questions used for this analysis , along with responses, and its  methodology .

• Artificial Intelligence
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• Teens & Tech

Olivia Sidoti is a research assistant focusing on internet and technology research at Pew Research Center

Jeffrey Gottfried is an associate director focusing on internet and technology research at Pew Research Center

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This paper is in the following e-collection/theme issue:

Published on 25.4.2024 in Vol 26 (2024)

Digital Therapeutic (Mika) Targeting Distress in Patients With Cancer: Results From a Nationwide Waitlist Randomized Controlled Trial

Authors of this article:

Original Paper

• Franziska Springer 1 * , MSc   ; 
• Ayline Maier 2 * , PhD   ; 
• Michael Friedrich 1 , PhD   ; 
• Jan Simon Raue 2 , PhD   ; 
• Gandolf Finke 2 , PhD   ; 
• Florian Lordick 3, 4 , Prof Dr   ; 
• Guy Montgomery 5 , Prof Dr   ; 
• Peter Esser 1 , PhD   ; 
• Hannah Brock 1 , MSc   ; 
• Anja Mehnert-Theuerkauf 1 , Prof Dr  

1 Department of Medical Psychology and Medical Sociology, Comprehensive Cancer Center Central Germany, University Medical Center Leipzig, Leipzig, Germany

2 Fosanis GmbH, Berlin, Germany

3 Department of Medicine II, University Medical Center Leipzig, Leipzig, Germany

4 University Cancer Center Leipzig, Comprehensive Cancer Center Central Germany, Leipzig, Germany

5 Center for Behavioral Oncology, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States

*these authors contributed equally

Corresponding Author:

Anja Mehnert-Theuerkauf, Prof Dr

Department of Medical Psychology and Medical Sociology, Comprehensive Cancer Center Central Germany, University Medical Center Leipzig

Philipp-Rosenthal-Str. 55, Haus W

Leipzig, 04103

Phone: 49 341 97 18800

Email: [email protected]

Background: Distress is highly prevalent among patients with cancer, but supportive care needs often go unmet. Digital therapeutics hold the potential to overcome barriers in cancer care and improve health outcomes.

Objective: This study conducted a randomized controlled trial to investigate the efficacy of Mika, an app-based digital therapeutic designed to reduce distress across the cancer trajectory.

Methods: This nationwide waitlist randomized controlled trial in Germany enrolled patients with cancer across all tumor entities diagnosed within the last 5 years. Participants were randomized into the intervention (Mika plus usual care) and control (usual care alone) groups. The participants completed web-based assessments at baseline and at 2, 6, and 12 weeks. The primary outcome was the change in distress from baseline to week 12, as measured by the National Comprehensive Cancer Network Distress Thermometer. Secondary outcomes included depression, anxiety (Hospital Anxiety and Depression Scale), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue), and quality of life (Clinical Global Impression-Improvement Scale). Intention-to-treat and per-protocol analyses were performed. Analyses of covariance were used to test for outcome changes over time between the groups, controlling for baseline.

Results: A total of 218 patients (intervention: n=99 and control: n=119) were included in the intention-to-treat analysis. Compared with the control group, the intervention group reported greater reductions in distress ( P =.03; ηp²=0.02), depression ( P< .001; ηp²=0.07), anxiety ( P= .03; ηp²=0.02), and fatigue ( P= .04; ηp²=0.02). Per-protocol analyses revealed more pronounced treatment effects, with the exception of fatigue. No group difference was found for quality of life.

Conclusions: Mika effectively diminished distress in patients with cancer. As a digital therapeutic solution, Mika offers accessible, tailored psychosocial and self-management support to address the unmet needs in cancer care.

Trial Registration: German Clinical Trials Register (DRKS) DRKS00026038; https://drks.de/search/en/trial/DRKS00026038

Introduction

In addition to somatic symptoms such as pain [ 1 ], patients with cancer report elevated levels of distress, anxiety, and depression [ 2 , 3 ]. Epidemiological data show that the prevalence of clinically substantial psychological distress typically ranges from 30% to 60% among patients with cancer [ 2 , 4 ]. Psychological distress can persist long after the end of treatment and is associated with reduced quality of life (QoL), lower cancer treatment adherence, and lower survival rates [ 5 ].

Supportive care interventions to prevent and manage the adverse psychological and physical effects of cancer across the cancer trajectory effectively improve outcomes such as emotional distress, QoL, and fatigue [ 6 ]. Optimal supportive care is holistic and patient centered, that is, based on the needs of each individual patient [ 7 ]. However, access to supportive care is often limited by a lack of specialist staff, organizational deficiencies, and barriers that cause patients to avoid or delay their treatment [ 8 - 10 ]. Thus, emerging or persistent supportive care needs across the cancer trajectory often go unmet, with detrimental psychosocial and emotional impacts on patients with cancer [ 11 ]. Moreover, the number of patients living with cancer has increased rapidly in recent years [ 12 ] due to improved early detection, diagnosis, and oncological treatments, posing a growing challenge to health systems worldwide to ensure adequate and long-term care for all patients with cancer [ 13 ].

The increasing use of digital health has ushered in a new era of patient-centered cancer care due to its potential for cancer care delivery [ 14 ]. Digital health interventions provide multiple benefits: they facilitate easy and low-threshold access to care, can overcome barriers to care (eg, location, time, and health status), may enhance symptom management through real-time symptom assessment, are scalable, and provide cost-effective and efficient information sharing [ 14 ]. Growing literature suggests that digital therapeutics, a subset of digital health interventions providing evidence-based treatments driven by software, play a useful role in addressing the unmet needs of patients with cancer [ 15 ]. For instance, various mobile apps have proven to be effective in catering to specific needs of patients with cancer, such as pain, anxiety, or QoL, by using different types of interventions, such as psychoeducation, physical exercises, or coping skills training (eg, [ 16 - 19 ]). Moreover, large analyses such as systematic reviews and meta-analyses evaluating the efficacy of app-based interventions for patients with cancer show positive effects on patient-relevant outcomes, such as distress, QoL, anxiety, depression, pain, and fatigue [ 20 - 23 ].

Existing app-based supportive care interventions provide various intervention modules, such as symptom monitoring, psychoeducation, mindfulness exercises, physical exercises, and cognitive behavioral therapy (CBT) techniques [ 24 ]. However, most of these apps are limited in their scope, targeting only specific symptoms (eg, fatigue) [ 25 ] and health behaviors (eg, physical activity) [ 26 ], or provide only a single function (eg, mindfulness training or symptom tracking) [ 27 - 29 ]. Furthermore, some of these apps were originally developed for non–oncology patient populations and have only been slightly adapted for patients with cancer [ 30 ]. Only a few apps offer a broader range of intervention modules [ 25 , 31 ], but they target specific subgroups of patients with cancer (eg, patients with 1 tumor entity or with specific symptoms).

Despite the evident need, there is yet no digital therapeutic that comprehensively addresses the problems faced by all patients with cancer and simultaneously offers tailored support for each individual patient. Therefore, we investigated the efficacy of Mika (developed by Fosanis GmbH), an app-based digital therapeutic that addresses all patients with cancer transdiagnostically and provides a holistic supportive care intervention. The app incorporates evidence-based supportive care elements, such as distress and symptom monitoring [ 32 ], CBT-based coping skills training [ 33 ], mindfulness-based stress reduction (MBSR) [ 34 , 35 ], strength and flexibility training [ 36 ], and patient education [ 37 ], thus targeting different aspects of psychological distress. An artificial intelligence algorithm individually tailors the content of the app to patients’ needs, considering cancer type, cancer treatment stage, and use behavior. A previously conducted pilot study of 70 patients with gynecological cancer indicated Mika’s feasibility and potential efficacy [ 38 ]. Considering the significant prevalence and impact of psychological distress among patients with cancer, this condition was selected as the primary end point of our study. This is underscored by the app’s integrated features for distress tracking and management alongside the widespread recommendation for distress screening in routine clinical care. Distress is recognized as a crucial clinical marker for assessing the efficacy of interventions across various tumor types and catering to the immediate and long-term supportive care needs of this patient group.

The primary aim of this waitlist randomized controlled trial (RCT) was to examine the efficacy of the Mika app for general distress in patients with cancer. The secondary aim was to assess the efficacy of the Mika app on anxiety, depression, fatigue, and QoL. We hypothesized that participants receiving access to the Mika app plus usual care (UC) for 12 weeks would report greater reductions in distress, anxiety, depression, and fatigue and greater improvements in QoL compared to participants receiving UC only.

Study Design

This nationwide unblinded 2-arm waitlist RCT evaluated the efficacy of the app-based digital therapeutic Mika in reducing distress in patients with cancer and was conducted fully decentralized in Germany, that is, participant recruitment, delivery of the study intervention, and outcome data collection were conducted without involving in-person contact between the study team and the participants. In this RCT, participants were assigned to either (1) access to the Mika app plus UC (intervention group [IG]), or (2) UC alone (control group [CG]). Participants were assessed at baseline (t0), 2 weeks (t1), 6 weeks (t2), and 12 weeks (t3) using self-report questionnaires. Once the participants in the CG completed the 12-week questionnaire, they also received access to the Mika app.

Ethical Considerations

The trial was approved by the Ethics Committee of the Medical Faculty of Leipzig University (404/21-ek) and was registered at the German Clinical Trials Register (DRKS00026038) in October 2021. All participants provided written informed consent prior to their participation in the study and retained the autonomy to withdraw from the study at any time. All personal data collected and used for this study underwent deidentification to safeguard the anonymity of participants. Monetary compensation was not provided to participants for their involvement in the study.

Participants

Textbox 1 shows the inclusion and exclusion criteria for this study. We only included patients who had been diagnosed with cancer or relapse within the last 5 years as they are likely to feel burdened by the physical and psychological effects of the disease and its treatment and therefore require supportive care. Epidemiological data indicate that supportive care needs typically decline in the years of long-term survivorship (cancer or relapse diagnosis ≥5 years ago) [ 39 ]. Participants were required to confirm their cancer diagnosis during the course of the study by submitting a letter from their treating physician. The study team enrolled patients after they had provided written informed consent, which had to be completed at home and submitted by email or mail.

Inclusion criteria

• Age≥18 years
• Cancer diagnosis or relapse diagnosis within the last 5 years (10th revision of the International Statistical Classification of Diseases and Related Health Problems: C00-C97)
• Access to a smartphone or tablet
• Ability to provide informed consent

Exclusion criteria

• Insufficient German language skills
• Inability to use a smartphone or tablet
• Prior use of the investigated digital therapeutic

Random Assignment

Participants were randomly assigned (1:1) to either the IG or CG using permuted block randomization with blocks of 4 based on an a priori created randomization list. The allocation sequence was concealed from the study investigators until assignment. Due to the nature of the intervention, it was not feasible to blind participants or the study team to the group assignment.

Recruitment and Procedure

Between September and November 2021, patients were recruited via social media advertising campaigns (Facebook and Instagram, Meta Inc) and informational emails to cancer support groups that directed patients to the trial website with a contact form for study registration. In addition, patients were recruited from a participant pool consisting of participants from previous independent studies at the University Medical Center Leipzig. Patients from the participant pool were approached directly by the study team via phone.

All interested patients were screened by phone to determine eligibility. To identify patients who were already users of the digital therapeutic, the study team asked participants about their use of digital support, however, without referring to the publicly available digital therapeutic by name to prevent CG patients from accessing the digital therapeutic before their enrollment in the study. Eligible patients received study information in the form of a video and text via email. Patients were informed that they were required to submit a physician’s letter confirming their cancer diagnosis via a secure cloud data-sharing service (TeamDrive, Crunchbase) during the course of their study participation. After providing informed consent, the participants were randomized into the IG or CG and completed the baseline questionnaires. Participants were informed about their group assignment following a completed baseline assessment. IG participants received a study access code to activate the app after downloading it from the app stores for either Android or iOS smartphones, allowing free use. The questionnaire battery was administered electronically using LimeSurvey (LimeSurvey GmbH). All participants received email invitations and reminders at 2, 6, and 12 weeks to complete the questionnaire. This RCT focused on changes in outcomes from baseline (t0) to week 12 (t3). The 2 assessments in between (t1 and t2) were not part of the analysis; an analysis of the trajectory of the symptoms is planned for the future. Once the CG participants completed the 12-week questionnaire, they also received a study access code that could be used to activate the app. All the participants received information about the app’s content and technical application via a standardized telephone introduction to the app. All participants were contacted for an exploratively structured telephone interview after completing the 12-week questionnaire. During this interview, the use of psychotherapeutic support during study participation was assessed. Data collection ended in March 2022.

Data monitoring was performed via standardized phone calls following questionnaire completion of each participant across all measurement time points to ensure data validity. These phone calls served to ask participants to provide missing questionnaire data, to allow participants to clarify difficulties in understanding single questionnaire items, and to provide assistance with limited app functionality. Missing questionnaire data were entered directly into the database by the study team, with a study team member reading the unanswered questions and associated response options to participants verbatim, prompting them to select their response option.

Self-reported adverse reactions and side effects of the investigated digital therapeutic were assessed at each measurement time point as part of the web-based questionnaire battery.

Intervention

Mika is an app-based digital therapeutic that provides a personalized supportive intervention aiming to reduce distress associated with cancer and its medical treatment, thus improving patients’ QoL. Mika comprises 3 modules: Check-Up , Discover, and Journeys . The Check-Up module allows for the monitoring of distress and symptom monitoring with electronic patient-reported outcomes that can be shared and discussed with the attending physician. The Discover module delivers coaching via articles and videos on cancer types and medical treatments, psychological well-being, physical activity, diet, and social and financial issues, which are based on scientific evidence and presented in a clear and understandable manner for patients. The Journeys module provides users with evidence-based, resource-activating training courses combining psychoeducation and exercises to help patients cope with the mental and physical effects of cancer, for example, coping with stress and fatigue, making decisions, or living with immunotherapy (for more details on the app modules, refer to Table 1 and Figure 1 ). An artificial intelligence algorithm within the app customizes the content for each patient. This includes personalized recommendations based on cancer type, cancer treatment stage, and crucially; the nature and severity of reported symptoms; and ensuring personalized support for each individual. This customization process not only accounts for general patient information but also actively incorporates real-time symptom tracking data and user reading behavior using an attentional factorization machine that predicts a patient’s likelihood of engaging with specific content. This approach focuses on important feature interactions related to content consumption [ 40 ], ensuring that recommendations are dynamically adjusted as patients report changes in symptoms and interact with the content. In addition, the algorithm uses a Dirichlet loss function to estimate the uncertainty in predictions [ 41 ], allowing the content to be ranked and presented based on the estimated read probability. The model undergoes monthly updates using historical data, optimizing through hyperparameter tuning evaluated by 7-fold time series cross-validation.

It is hypothesized that the digital therapeutic empowers patients with cancer by improving their health literacy and self-management along the cancer trajectory using evidence-based methods, such as symptom monitoring, patient education, MBSR, strength and flexibility training, acceptance and commitment therapy, and CBT-based coping skills training.

The Mika app was developed by Fosanis GmbH in collaboration with leading research institutions, such as the Charité University Hospital Berlin, University Hospital Leipzig, and the National Center for Tumor Diseases Heidelberg. All content of the app was carefully reviewed by experts (eg, oncologists, psychotherapists, nutritionists, and physiotherapists) before publication. The feasibility and preliminary efficacy of Mika were investigated in a previously conducted randomized pilot study involving 70 patients with gynecological cancer [ 38 ]. Mika is available for download free of charge in German and the United Kingdom app stores for Android and iOS smartphones.

IG participants could freely choose the modules to work on. While regular app use was recommended, participants were instructed to use the app at least 3 times a week.

a PRO: patient-reported outcome.

UC Condition

UC consisted of all health care that patients in Germany usually receive. There were no restrictions on health care use.

Outcome Assessment

Primary outcome.

The primary outcome was the change in psychological distress from baseline to 12 weeks, measured using the validated German version of the National Comprehensive Cancer Network Distress Thermometer [ 42 ]. Distress Thermometer is a well-established single-item self-report measure that assesses the global level of distress on a 0 (no distress) to 10 (extreme distress)-point Likert scale. It shows excellent psychometric properties across various cancer populations worldwide and is recommended as a clinical tool for routine clinical care [ 43 ]. A score ≥5 indicates clinically significant levels of distress.

Secondary Outcomes

The secondary outcomes included changes in anxiety and depression symptoms, fatigue from baseline to 12 weeks, and QoL at 12 weeks. Anxiety and depression symptoms were measured using the Hospital Anxiety and Depression Scale [ 44 ], a 14-item self-report measure of anxiety and depression, with 7 items measuring each subscale. Scores for each subscale range from 0 to 21, with a higher score indicating higher levels of anxiety or depression and a cutoff score of ≥8 for each subscale. Fatigue was assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue [ 45 ], a 13-item measure that assesses self-reported tiredness, weakness, and difficulty in performing usual activities due to fatigue. The Functional Assessment of Chronic Illness Therapy-Fatigue score ranges from 0 to 52, with higher scores representing less fatigue. Self-reported QoL was measured using an adapted version of the Clinical Global Impression-Improvement Scale [ 46 ], a single-item 7-point measure that assesses the overall improvement of a patient’s disease relative to a baseline state at the beginning of the intervention. In this trial, the Clinical Global Impression-Improvement Scale measured improvement in QoL relative to the beginning of the study, with a value of 4 indicating no change, <4 indicating improvement, and >4 indicating deterioration in QoL.

Intervention Safety

The safety of the digital therapeutic was assessed by the number and type of self-reported adverse reactions and side effects during the trial duration.

Intervention Adherence and Engagement

Adherence to the intervention was assessed by tracking app activities. IG participants were considered active once they activated the app using the study access code and consented to the Mika app’s privacy terms. Subsequently, their pseudonymized in-app activities were automatically recorded as log data. These log data facilitated the evaluation of intervention adherence, defined as the number of days with ≥1 app activity during each of the three 4-week periods (0-4, 5-8, and 9-12 weeks) within the 12-week intervention. Such an approach enabled us to capture the frequency and diversity of app engagement, thus embodying a comprehensive definition of adherence. In addition, engagement across the app’s 3 modules—Check-Up, Discover, and Journeys—was analyzed.

Statistical Analysis

Given an estimated dropout rate of 20% (50/250), a priori sample calculations showed that a sample of 2×125 (N=250) at baseline was needed to detect a change of 1 scale point (SD 2; α=.05; 1−β=.8) in the primary outcome.

Primary analyses were performed using the intention-to-treat (ITT) principle, which included all randomized participants with a confirmed cancer diagnosis by a physician’s letter. Analyses were also performed per-protocol (PP), which was restricted to participants who (1) completed the self-report questionnaire at all measurement time points, (2) did not receive psychotherapeutic support during study participation, (3) did not use the investigated digital therapeutic before receiving access during study participation, and (4) used the investigated digital therapeutic at least 1 time per period up to the 5- to 8-week period of the 12-week intervention period (only IG).

Analysis of covariance was used to examine changes in distress, depression, anxiety, and fatigue outcomes between the trial arms from baseline to 12 weeks, controlling for baseline scores. Exploratory regression analyses were conducted to investigate potential variables influencing the primary outcome. These analyses focused exclusively on sociodemographic and clinical factors that showed differences between the IG and CG in the initial group comparison. Partial eta–squared was reported as the effect size for all analyses of covariance, with effect sizes interpreted as small, medium, and large at ≥0.01, ≥0.06, and ≥0.14 [ 47 ], respectively. Differences in QoL between trial arms at follow-up (12 weeks) were analyzed with a 2-tailed 2-sample t test, using Hedges g ' as a measure of effect size (≥0.2=small effect, ≥0.5=medium effect, and ≥0.8=large effect [ 47 ]).

Missing outcome data at random were imputed using the expectation-maximization algorithm. For dropouts, the last observation carried forward was used. For deceased participants, the worst possible values were assumed. Dropouts were participants who failed to complete the baseline or follow-up questionnaires or failed to provide a physician’s letter confirming their cancer diagnosis. A dropout analysis was performed to compare the variables of age, sex, and baseline distress between study noncompleters (dropouts) and study completers using chi-square and t tests. Furthermore, to model the robustness of the primary efficacy analysis under different assumptions for missing data mechanisms, an explorative sensitivity analysis using reference-based multiple imputation (jump-to-reference) [ 48 ] was performed in the extended ITT population (all randomized participants). For this purpose, monotone missing values were replaced using the jump-to-reference approach, whereas sporadic missing values were replaced under the assumption of missing at random. For jump-to-control and jump-to-reference imputation, 50 data sets were generated to minimize the loss of statistical power. The results were then aggregated across the imputed data sets [ 49 ].

All statistical tests were 2-tailed, with a significance level of 5%. Analyses were performed using R (version 4.1.0; R Foundation for Statistical Computing) [ 50 ].

Study Sample

Over the 3-month recruitment period, 517 persons were screened for eligibility and 321 were determined eligible. Of the 321 participants, 248 (77.3%) gave informed consent and were randomly assigned to the IG and the CG ( Figure 2 ). Of the 248 participants, 37 (14.9%) were considered dropouts because they did not complete baseline or follow-up assessments (n=7), failed to confirm their cancer diagnosis by submission of a physician’s letter (n=7), or both (n=23). Age and sex of study dropouts and study completers did not differ ( P age =.89 and P sex =.23), but participants who dropped out showed higher distress levels at baseline compared to study completers ( P =.02). Participants without a verified cancer diagnosis (30/248, 12.1%) were excluded from the ITT analysis, resulting in an ITT population of 218 participants (n=99, 45.4% IG and n=119, 54.6% CG). Of the 218 participants, 173 (79%) were recruited via social media advertisements and cancer support groups and 45 (21%) were recruited using the participant pool of prior studies.

Baseline characteristics were balanced between the groups ( Table 2 ), but participants in the IG were younger compared with those in the CG ( P =.02). No baseline differences in the primary and secondary outcome parameters were observed between the groups, with P values as follows: P =.99 (distress), P =.25 (depression), P =.47 (anxiety), and P =.21 (fatigue). On average, participants were 56 (SD 11) years old, and 60.6% (132/218) of the participants were female and had been diagnosed with cancer 25 (SD 17) months earlier. The most frequently reported cancer types were breast cancer (74/218, 33.9%) and hematological cancer (61/218, 28%), with 8.7% (19/218) of participants reporting a diagnosis of relapsed cancer. The PP population comprised 124 participants, following the exclusion of 94 participants. The primary reasons for exclusion were psychotherapeutic support during study participation and prior use of the investigated digital therapeutic.

a Intervention=12-week access to digital therapeutic app intervention+usual care.

b Control=usual care.

c NCCN Distress Thermometer: National Comprehensive Cancer Network Distress Thermometer (at baseline, clinically significant level of distress≥5).

d HADS-A: Hospital Anxiety and Depression Scale, anxiety subscale (German version, at baseline, cutoff score ≥8).

e HADS-D: Hospital Anxiety and Depression Scale, depression subscale (German version, at baseline, cutoff score ≥8).

f Multiple reasons are possible within 1 patient, and cases do not add up to the total number.

After 12 weeks, participants in the IG reported a reduced level of distress compared to participants in the CG in the ITT population ( F 1,215 =4.7; P =.03; ηp²=0.02; Table 3 ). The observed treatment effect was more pronounced in the PP population ( F 1,121 =6.9; P =.01; ηp²=0.05). The analysis revealed that higher levels of baseline distress predicted a greater change in distress after 12 weeks in the IG. An exploratory regression analysis yielded no predictive effect of age on the change in distress. The explorative sensitivity analysis among all randomized participants (n=248) showed comparable treatment effects (jump-to-control: F 1,19375.1 =5.3; P =.02; ηp²=0.02 and jump-to-intervention: F 1,15314.8 =5.9; P =.02; ηp²=0.02).

a An analysis of covariance was used to test for differences in change in distress levels between groups from baseline to follow-up (12 weeks), controlling for baseline. The partial eta–squared is the reported standardized effect size for the mean difference. The effect sizes can be interpreted as small, medium, or large at ≥0.01, ≥0.06, and ≥0.14, respectively. The results of the intention-to-treat and per-protocol analysis are reported.

b Intervention=12-week access to digital therapeutic app intervention+usual care.

c Control=usual care.

d N/A: not applicable.

In the ITT population, symptoms of anxiety ( F 1,215 =4.8; P =.03; ηp²=0.02), depression ( F 1,215 =15.5; P <.001; ηp²=0.07), and fatigue ( F 1,215 =4.4; P =.04; ηp²=0.02) improved in participants in the IG from baseline to 12 weeks compared to participants in the CG ( Table 4 ). The observed treatment effects on anxiety and depression were more pronounced in the PP population (anxiety: F 1,121 =7.2; P =.01; ηp²=0.06 and depression: F 1,121 =14.9; P <.001; ηp²=0.11). A trend-to-significant treatment effect was observed for fatigue symptoms in the PP population ( F 1,121 =3.8; P =.05; ηp²=0.03). QoL did not differ significantly between the groups at 12 weeks (ITT: t 216 =0.88; P =.38; g=0.12 and PP: t 122 =1.63; P =.11; g=0.30).

c HADS-A: Hospital Anxiety and Depression Scale, anxiety subscale (German version, at baseline, cutoff score ≥8).

d ITT: intention-to-treat.

e N/A: not applicable.

f Italicized values are significant at P <.05.

g PP: per-protocol.

h HADS-D: Hospital Anxiety and Depression Scale, depression subscale (German version, at baseline, cutoff score ≥8).

i FACIT-F Functional Assessment of Chronic Illness Therapy–Fatigue.

j CGI-I: Clinical Global Impression Improvement.

Safety Outcomes

IG participants reported no adverse reactions or side effects of digital therapeutic during the study.

Of the 99 participants in the IG (ITT), 98 (99%), 78 (79%), and 67 (68%) used the digital therapeutic intervention at 0- to 4-, 5- to 8-, and 9- to 12-week periods of the 12-week intervention, respectively, demonstrating good initial adherence to the intervention, which decreased moderately over time. App use (module use and days spent on the app) decreased over time ( Table 5 ). IG participants accessed content from various categories at different frequencies. The most accessed categories were cancer therapy, symptoms and side effects, and nutrition in cancer, with 80% (79/99), 83% (82/99), and 80% (79/99) of users accessing the content in these categories, respectively. Conversely, partnership and family, relaxation, and recipes were accessed less, with 29% (28/99), 34% (33/99), and 32% (31/99) of users, respectively.

Principal Findings

This nationwide waitlist RCT examined the efficacy of Mika, an app-based digital therapeutic that provides a personalized supportive intervention for patients with cancer. Participants who had access to the Mika app for 12 weeks showed significant improvements in perceived distress (ie, the primary outcome) and symptoms of anxiety, depression, and fatigue (ie, the secondary outcomes) compared to participants who received UC. The observed treatment effects were similar in the ITT and PP populations but more pronounced in the PP population, indicating the overall robustness of the findings. We observed no group difference in the QoL after 12 weeks. Intervention adherence was good, and no adverse reactions or side effects of the investigated digital therapeutic were reported.

Comparison With Prior Work

While a growing body of research shows evidence of the efficacy of app-based interventions for oncological populations on distress, fatigue, anxiety, and depression [ 20 , 25 , 27 , 31 , 51 ], this is the first study to examine the efficacy of a single holistic app-based digital therapeutic based on multiple intervention modules on these patient-relevant outcomes. Although the improvement in the primary outcome was modest, it reflects the nuanced nature of psycho-oncological interventions, where even modest changes can have significant clinical relevance. Furthermore, we conducted comprehensive testing of the effects of the investigated digital therapeutic on patients with cancer across all tumor entities, using a larger sample size compared to most previous studies [ 25 , 27 , 31 , 51 ].

In contrast to the findings of this study, however, other studies found an effect of app-based supportive interventions on QoL [ 23 , 27 , 31 ]. This difference in findings could be due to differences in the operationalization and measurement of QoL. In this study, participants’ global QoL was assessed using a single-item questionnaire after a 12-week intervention period. However, global QoL has been shown to be less affected in patients with cancer compared to specific components of QoL, such as social or cognitive functioning and symptom burden from fatigue or insomnia [ 52 ]. Further research using different QoL assessment tools could provide more insights into the efficacy of the investigated digital therapeutic on specific aspects of QoL.

A significant level of intervention adherence and engagement with the digital therapeutic, with varying degrees of interaction across the different app modules, indicates good acceptability and perceived subjective benefit of the investigated digital therapeutic and allows for reliable conclusions about its efficacy in oncological settings. The broad range of engagement, as illustrated by the IQRs, underscores the personalized nature of app use, catering to diverse participant needs and preferences. The variability in engagement levels across different app modules highlights the importance of personalizing digital therapeutics to increase adherence and maximize therapeutic effects.

As we evaluated the app intervention holistically, future studies should examine the impact of the app’s individual components.

While the dropout rate in the IG was slightly higher than that in the CG, the dropout rate in the IG as well as the overall dropout rate was low compared to other app-based supportive interventions [ 25 , 30 ]. Considering that patients with cancer have been found to have a positive attitude toward digital health [ 53 , 54 ], the findings of this study add to the notion that digital health interventions have the potential to overcome barriers associated with access to supportive care in oncological populations [ 55 ].

We found a positive effect of the investigated digital therapeutic on general psychological distress and a broad range of specific distress-associated parameters. Importantly, improvements in psychological symptoms, that is, depression and anxiety, can also have a positive tertiary preventive effect on cancer progression [ 5 ]. The effect sizes in this study ranged from small (ηp²=0.02) to medium (ηp²=0.07) in the ITT population and were more pronounced in the PP population (ηp²=0.05-0.11). The primary outcome improvement, while subtle, aligns with the expected outcomes in psycho-oncological interventions, highlighting the importance of considering the broad spectrum of therapeutic impacts. The medium to large effects observed in secondary end points, together with the primary outcome, illustrate the broad therapeutic impact and highlight the digital therapeutic’s capacity to significantly improve key aspects of psychological well-being in patients with cancer. Small-to-medium effect sizes are common in in-person supportive care interventions [ 6 ]. Our results also compare well with other app-based supportive care interventions, such as small effect sizes reported for a CBT and psychoeducation self-management apps on fatigue [ 25 ] or small to medium effects of a web-based mindfulness-based intervention on anxiety and depression [ 56 ]. This is further supported by the results of several systematic reviews [ 20 , 21 ]. The fact that such effect sizes can be achieved with minimal cost and personnel effort via a digital approach further supports the significant potential for accessibility, reach, and impact of digital therapeutics.

Clinical Implications

The multifaceted intervention modules of the investigated digital therapeutic aim to support patients holistically. The investigated digital therapeutic hereby translates widely used evidence-based intervention methods within supportive care, such as symptom monitoring; patient education; modules of CBT, MBSR, and acceptance and commitment therapy; and strength and flexibility training, into a digital format. The intervention modules of the app are designed to help patients learn about their disease and prepare for discussions with clinicians in an informed decision-making process. This may reduce anxiety and insecurities across the cancer trajectory, while empowering patients and strengthening their self-efficacy.

While it is acknowledged that digital therapeutic interventions might not fully replicate the “in-person” experience, the scope and utility of these tools in the realm of oncology are substantial. For instance, a study evaluating a mobile app designed for tracking patient-reported daily activities found that when supervised by a physician, the data collected were more accurate than when used without guidance [ 57 ]. Conversely, a music app was equally effective in alleviating pain and anxiety in emergency department patients irrespective of supervision [ 58 ]. This suggests that certain interventions, such as symptom tracking, might be more prone to inaccuracies without proper guidance than passive activities, such as listening to music. In addition, CBT, which is traditionally the most effective in face-to-face settings, has generated interest in the digital domain. A study on the digital adaptation of mindfulness-based cognitive therapy for patients with cancer experiencing distress found the therapeutic connection between therapist and patient to be as potent as in in-person sessions [ 59 ]. This underlines the evolving role of digital therapeutics and its potential to reshape therapeutic avenues in oncology, thus paving the way for enhanced patient care.

Furthermore, considering the increasing number of patients with cancer experiencing psychosocial distress and the limited availability of health care professionals, digital therapeutics could present scalable and cost-effective solutions. These solutions can address symptoms and bolster the quality and accessibility of supportive care [ 55 , 60 , 61 ]. Recognizing patients’ diverse needs, tools such as the Mika app leverage artificial intelligence to deliver real-time, tailored support. This has the potential to benefit a broad spectrum of patients with cancer globally while also reducing the pressure on health care infrastructure and professionals. Therefore, digital therapeutics offer a patient-focused approach that is adaptable to specific clinical and lifestyle challenges such as disease management, emotional support, and health-related determinants. They might also further enhance medication adherence, tolerance to chemotherapy, and overall survival rate in the cancer care continuum [ 15 ]. Incorporating these digital tools into routine oncological supportive care can augment patient-centric care and enrich patient experience, safety, and interactions with clinicians [ 15 , 61 ]. However, while there is a consensus among medical professionals and stakeholders regarding the revolutionary potential of digital health in addressing cancer treatment challenges, the path to universal adoption remains intricate. Future studies should delve into the assimilation of digital therapeutics, such as Mika, into standard care across varied clinical environments and evaluate hurdles such as digital literacy and the acceptance of digital tools by both patients and health care professionals [ 62 - 64 ].

Strengths and Limitations

The main strength of this study was the app itself. It addresses the overreaching problem areas faced by all patients with cancer while providing tailored support for population-specific areas of burden (ie, cancer type, treatment status, and use behavior). Its flexible and easily accessible use allows for seamless integration into patients’ daily lives and continuity of supportive treatment. In addition, the low overall dropout rate and data monitoring led to very little missing data. Similar findings in the ITT, PP, and extended ITT populations suggest overall robustness of the results.

This study has several limitations. First, the web-based recruitment procedure may have led to study registration from patients with cancer who were particularly motivated, digitally literate, and highly functioning in seeking support during their cancer journey, which may limit the generalizability of the study. However, the use of additional recruitment pathways (support groups and participant pool) likely resulted in the recruitment of a more heterogeneous sample, possibly compensating for potential selection bias. Future studies might investigate the impact of various recruitment channels on the efficacy of digital therapeutics, and thus, which population may be particularly responsive to digital interventions. Second, the higher number of dropouts in the IG compared to the CG may reflect treatment dissatisfaction or lost interest in the treatment of some participants, potentially confounding the study’s results. Dropouts, who are more likely to show elevated levels of distress, may have been made aware of the increased need for support through the intervention modules. Patients with clinically significant levels of distress or mental disorders might have accessed support services with more guidance from a health care professional, such as psychotherapy or psycho-oncological counseling. However, no side effects or adverse events were reported in the IG, and the overall robust pattern of results in the ITT, PP, and extended ITT populations suggests a low risk of attrition bias. The fact that participants who dropped out of the study showed higher baseline distress levels may have led to an underestimation of the intervention effect as higher baseline distress levels predicted a greater change in outcome after treatment. Third, due to the nature of the intervention, the group allocation could not be blinded. While experimenter bias was reduced due to a predefined, standardized monitoring procedure and statistical analysis plan, IG participants may have anticipated potential effects. Fourth, the intervention, along with its adherence, was assessed as a whole, which requires the evaluation of specific modules and any potential dose-response relationship in the future. In addition, there was no specific measure to evaluate the subjective usefulness or satisfaction with the digital therapeutic under investigation. Incorporating such a measure could have provided targeted insights into the participants’ perceptions and experiences with the app. However, the observed use behavior, characterized by participants repeatedly accessing the app and actively engaging with its content, may serve as an indirect indicator of the app’s value to the participants. Future studies should aim to validate this interpretation. Finally, the study sample included participants with a wide variety of cancer diagnoses, which did not allow for the examination of diagnosis-specific intervention effects. However, the sample composition is consistent with the target population of the investigated digital therapeutic, which includes patients with cancer of all entities, and strengthens the study’s generalizability and clinical utility. Moreover, a large body of data shows that while variables such as cancer type, treatment status, disease progression, and sex may influence the magnitude of treatment response to supportive therapy, the beneficial effects of supportive therapy are present across various cancer subpopulations [ 65 - 67 ]. In addition, there is a consensus that psychosocial support needs to be integrated into routine cancer care for all cancer types [ 68 , 69 ].

Conclusions

In summary, this RCT demonstrated that Mika, an app-based digital therapeutic that provides a personalized supportive care intervention, can effectively reduce psychological distress and further alleviate symptoms of anxiety, depression, and fatigue in patients with cancer. Digital therapeutics, such as Mika, deliver easily accessible, patient-centered, and effective psychosocial and self-management support for patients with cancer across the course of the disease. Digital therapeutics may present scalable solutions to support patients with cancer worldwide and thus help fill the supportive care gap. Further research is needed to explore the integration of Mika into routine cancer care and its efficacy in diverse clinical settings.

Acknowledgments

The clinical trial was funded by Fosanis GmbH, Berlin.

Data Availability

The data set generated during and analyzed during this study, including individual participant data that underlie the results reported in this article after deidentification (text, tables, and figures), clinical study report, informed consent form, and analytic code, are available from AMT beginning 3 months and ending 5 years following article publication. Access to the data will be granted to investigators whose proposed use of the data has been approved by an independent review committee identified for this purpose, for individual patient data meta-analysis. Proposals for accessing the data may be submitted up to 36 months following article publication.

Authors' Contributions

JSR and GF provided financial support. FS, AM, and HB provided administrative support, with FS also contributing to the collection and assembly of data. MF contributed to data curation. FS, AM, and MF contributed to data analysis and interpretation. FS and AM equally contributed to writing the original draft. All authors contributed to reviewing and editing the draft and provided final approval of the manuscript. AMT, FS, and JSR contributed to the conception and design.

Conflicts of Interest

FS, MF, and HB received research funding for this trial from Fosanis GmbH, which was paid to their institution. AM is an employee at the company Fosanis GmbH. JSR and GF work for the company Fosanis GmbH. They are the managing directors and board members of Fosanis GmbH and own shares of Fosanis GmbH. All other authors declare no other conflicts of interest.

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Abbreviations

Edited by YH Lin; submitted 21.08.23; peer-reviewed by F Denis, A Haussmann, N Schaeffeler, P Chow; comments to author 24.01.24; accepted 23.02.24; published 25.04.24.

©Franziska Springer, Ayline Maier, Michael Friedrich, Jan Simon Raue, Gandolf Finke, Florian Lordick, Guy Montgomery, Peter Esser, Hannah Brock, Anja Mehnert-Theuerkauf. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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