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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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published research papers in marketing

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Cause-related marketing: a systematic review of the literature

  • Original Article
  • Open access
  • Published: 08 January 2022
  • Volume 20 , pages 25–64, ( 2023 )

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  • Hina Yaqub Bhatti   ORCID: orcid.org/0000-0003-2157-7418 1 , 2 ,
  • M. Mercedes Galan-Ladero 1 &
  • Clementina Galera-Casquet 1  

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Cause-Related Marketing (CRM) is one of the most versatile activities among the Corporate Social Responsibility (CSR) initiatives. Though CRM is extensively researched, however, only a few authors have performed systematic literature reviews on CRM. Therefore, more systematic reviews of CRM are still needed to complete and bring together the more contributions, advances, and different existing research lines. Thus, this paper provides a comprehensive overview of the existing literature in CRM from the two keywords: “Cause-Related Marketing” and “Cause Marketing”, and the time period ranges from 1988 to 2020. In this study, rigorous protocol is used in synthesizing 344 English articles drawing upon e-journal database searches. These articles were categorized by time-wise development, country-wise development, methodological development, cross-cultural analysis, the role of journals. This study also carried out the Bibliometric Analyses. The review highlights that the concept of CRM has evolved from being considered a marketing mix tool (a promotion tool), to being considered as a CSR initiative, with a more strategic character. Our findings revealed that only a few journals published articles on CRM. Geographically, the CRM study was initiated in North America, followed by Europe and Oceania, and Asian and Sub-Saharan African countries. From the third decade, there was more collaboration in cross-cultural studies and the use of mixed-method (qualitative and quantitative studies) approach. Lastly, this study shows the most manifest research gaps in CRM that opens avenue for future research.

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Avoid common mistakes on your manuscript.

1 Introduction

Cause-Related Marketing (CRM) is a versatile and growing activity in the marketing field. It provides opportunities to profit and non-profit organizations, and consumers, to participate in a social cause (Varadarajan & Menon, 1988 ). Since 1988, CRM initiatives have gradually increased for more than three decades. CRM allows to achieve the societal and financial corporate establishment’s goals, as well as provide the opportunity to consumers to participate in an altruistic act.

Formally, the first CRM campaign named in this way was carried out by American Express (AMEX) in 1983, in the United States. The purpose of this program was to increase the usage of the AMEX credit card, but also collect money to be donated for the renovation of the Statue of Liberty. This project was developed from September to December, and the donation was $1.7 million (Varadarajan & Menon, 1988 ). Since then, and according to the IEG Sponsorship Report, cause sponsorship spending in North America has grown continuously Footnote 1 from $120 million in 1990 until $2.23 billion in 2019, as shown in Fig.  1 . In 2020, due to the COVID-19 pandemic, U.S. sponsorship value was $10 Billion (annually), which approximately increased 38% (IEG, 2020 ).

figure 1

IEG Sponsorship Report from 2002 to 2019. Note: We only include data since 2002, because previous data are not available on the IEG Sponsorship Report. Source: IEG (2020)

Consequently, the practice of CRM has also increased for the last three decades because more profit organizations have engaged in CRM activities (Adomaviciute et al., 2016 ), non-profit organizations have maintained environmental protection, health, and other worthy causes (Grolleau et al., 2016 ), and when consumers purchase the CRM products for support the cause, they have a prosocial behavior (Chang & Chu, 2020 ) and feel happy (Jeong & Kim, 2020 ; Vrontis et al., 2020 ).

During this time, CRM has become a topic of considerable debate in both managerial and academic circles worldwide. Although some systematic literature has been presented on this topic (see, for example, Guerreiro et al., 2016 ; Lafferty et al., 2016 ; Natarajan et al., 2016 ; or Thomas et al., 2020 ), an updated systematic literature review is required. Thus, we present a new systematic literature review: (1) To complete the review of the academic research articles in the area of CRM, from 1988 to 2020, with the perspective of profit organizations, non-profit organizations, and consumers over the last three decades; (2) To include cross- cultural studies; (3) To include studies carried out in developed and developing countries; (4) To include studies executed in different societies (e.g. Muslim societies, Western societies with Christian traditions, etc.); and (5) To conduct a bibliometric analysis using VOSViewer Software.

Thus, the main objective of this paper is to provide a systematic literature review of the existing research in the field of Cause-Related Marketing. More specifically, our aim is to find influential papers that have shaped this field and provide the overview of historical development in the field of research, focusing first on previously analyzed criteria: Time-Wise Development of CRM Literature, Country-Wise Development of CRM Literature, Methodological development in CRM Literature, and Role of Journal in Development of CRM literature. But also, this study carries out a systematic review with a bibliometric analysis. On the one hand, the systematic review helps the researchers to improve the rigor of prior reviewing literature. On the other hand, bibliometric analysis helps to analyze divergent views and examine the development of the CRM field.

Hence, this paper has followed two steps in the systematic literature review on CRM: (1) to select the inclusion and exclusion criteria, and (2) to analyze the evolution of CRM in seven different categories.

First step : Inclusion and Exclusion Criteria.

This research only included published papers in journals, from 1988 to 2020 (data sources such as working papers, reports, newspapers, textbooks, conference papers, or theses / dissertations, were not included).

Two keywords, “Cause-Related Marketing” and “Cause Marketing”, were used to search the databases (SAGE Publications, JSTOR, Emerald Full Text, Springer, John Wiley Publications, Elsevier, Taylor and Francis, and Google Scholar).

This research also used conceptual review and empirical studies of different countries.

This research only included papers written in English (i.e., non-English language research articles were excluded).

This study considered the date of publication of the journal as the date of the research articles.

Second step : Academic researchers have used qualitative and quantitative methods for literature review to organize and provide the above underlying findings on CRM. And according to Liu et al., ( 2015 ), Bibliometric Analysis is a tool to examine literature review. Thus, this study has also provided a static and systematic picture of the research (Aria & Cuccurullo, 2017 ). This study relies on bibliometric techniques such as author-citation analysis, or co-words or co-occurrence analysis, and co-citation analysis of authors through VOSviewer software (version 1.6.5). Following Thomas et al. ( 2020 ), we selected Time-Wise Development of CRM Literature, Country-Wise Development of CRM Literature, and Methodological Development in CRM Literature. And we added other analysis such as Role of Journal in Development of CRM literature, author-citation analysis, and Co-words or Co-occurrence analysis, proposed by Poje & Groff ( 2021 ). We also considered adding a new category that had not been considered in previous studies: cross-cultural analysis.

Therefore, the structure of this paper is organized as follow: firstly, we show the CRM theoretical background (with a previous introduction to CSR, to link it with CRM, because CRM is generally considered under the umbrella of CSR – e.g., Kotler and Lee, 2005 ; Galan-Ladero, 2012 ); secondly, we offer the results of our literature analysis in the CRM field; later, we discuss these results; and finally, we offer the main conclusions, also considering the main limitations of this study and further research.

2 Background

Since the inauguration of the third millennium, Corporate Social Responsibility (CSR) has become a globally hot issue by the rapid change of the environment. A large number of organizations, from developed and developing countries, have focused on CSR.

CSR, defined as “a concept whereby companies integrate social and environmental concerns in their business operations and in their interaction with their stakeholders on a voluntary basis ” (European Commission, 2001), has a wide range of history: it started in Western countries, but later, it spread all over the world. Antecedents of CSR can be found at 18th and 19th Centuries, with the creation of welfare schemes adopted with a paternalistic approach, to protect companies and retain employees with improved life quality (Carroll 2008 ). But it is in the twentieth Century, and specifically after World War II, when scholars and practitioners discussed about the social responsibilities, and successful businesses also adopted such responsibilities (Heald, 1970 ).

Thus, CSR started to be established and, in the last seven decades, it has played different roles:

The 1950s was the first era that established the current CSR. Successful business leaders and board of directors moved towards the ethics of society. Bowen, the first who coined the term, introduced the concept, and provided the initial definition of CSR, described as “ the obligations of businessmen to pursue those policies, to make those decisions, or to follow those lines of action which are desirable the objectives and values of our society ” (Bowen, 1953 , p. 6). In this area, Heald ( 1957 ), discussed that businesses do not only serve on economic performance work, but they also serve on humane and constructive social policies.

The 1960s : Many of the definitions of CSR are formalized. Walton ( 1967 ) was a prime thinker who addresses the different aspects of CSR: “ In short, the new concept of social responsibility recognizes the intimacy of the relationships between the corporation and society and realizes that such relationships must be kept in mind by top managers as the corporation and the related groups pursue their respective goals ( Walton, 1967 , p. 18)”.

The 1970s : Friedman described that the social responsibility of business is to enhance profits and maximize shareholder value. Therefore, Carroll ( 1979 ) came in this decade with the new concept of CSR, defined as “ the social responsibility of business encompasses the economic, legal, ethical, and discretionary expectations that society has of organizations at a given point in time ”.

The 1980s : the notions of stakeholder management and business ethics had become the main integral part of the business (Carroll, 2008 ). In 1980, Jones proposed that CSR is a process, not the outcome, and CSR, when engaged in as a process of decision making, should constitute CSR behavior by the corporation (Jones, 1980 ). Also, Aupperle et al., ( 1983 ) suggested that four aspects include CSR: economic, legal, ethical, and voluntary or philanthropic responsibilities.

The 1990s : Carroll ( 1991 ) revised the concept of CSR and introduced the “Pyramid of Corporate Social Responsibility”. He described four main responsibilities of the company: economic responsibility (“be profitable”), legal responsibility (“obey the laws and regulations”), ethical responsibility (“do what is just and fair”), and philanthropic responsibility (“be a corporate citizen”). During this decade, Elkington ( 2001 ) introduced another concept of CSR, the “Triple Bottom Line”, which focuses on three issues: social responsibility (“people”), environmental responsibility (“planet”), and economic responsibility (“profit”).

In the first decade of the twenty-first century (The 2000s), CSR extends to emerging markets. After the collapse of Enron, Footnote 2 many organizations and corporations focused on establishing CSR departments, hiring CSR consultants and CSR managers. On the other hand, in 2002, ISO Committee on Consumer Policy play an important role in ISO 26000, an international standard that present a guideline on Corporate Social Responsibility. Footnote 3

In the second decade of the twenty-first century (The 2010s), Kramer and Porter ( 2011 ) introduced the concept of “creating shared value”, which becomes the core objective of business strategies. 2015 is an important year because the “2030 Agenda for Sustainable Development”, with the “Sustainable Development Goals” (SDGs), was launched. SDGs covered a wide range of global areas, such as fighting against climate change, removing poverty and hunger, as well as promoting sustainable consumption, among others.

Therefore, different theories have been created and adapted during all this time. The most important theories are Carroll’s CSR Pyramid Theory, Footnote 4 Triple Bottom Line Theory, Footnote 5 Stakeholder Theory, Footnote 6 and Corporate Citizenship Theory. Footnote 7

On the other hand, CSR initiatives, formed as a part of the core business activities, provide long-term financial security and growth for stakeholders but also increase the market position (Bhattacharyya et al., 2008 ). Under the big umbrella of CSR, different initiatives have appeared, and they have become growing popular among profit organizations worldwide. Kotler et al., ( 2012 ) explained six different types of CSR initiatives (see Table  1 ), which included cause promotion, cause-related marketing, corporate social marketing, corporate philanthropy, community volunteering, and socially responsible business practices.

According to Thomas et al., ( 2011 ), CSR has received significant attention in both academic and business societies. CRM, as one of these initiatives, has progressed in social responsibility and allows firms to link their philanthropic activities and strategic marketing goals. On the other hand, CRM activities also have been an increasing part of the corporate marketing plans (Gupta & Pirsch, 2006a ). Therefore, this study especially focuses on this CSR initiative: Cause-Related Marketing (CRM).

The first definition of Cause-Related Marketing (CRM) was introduced by Varadarajan and Menon ( 1988 , p. 60), as “ the process of formulating and implementing marketing activities that are characterized by an offer from the firm to contribute a specified amount to a designated cause when consumers engage in revenue-providing exchanges that satisfy organizational and individual objectives”. This definition provides two main streams: to support the charitable cause and to satisfy organizational and individual objectives.

On the other hand, the most essential and significant benefit of the CRM is shown as a win-win-win situation (for the profit organizations, non-profit organizations, and consumers - Adkins,  1999 ). CRM campaigns increase the number of sales for the organization, as well as enhance the number of donations to the non-profit organizations (Deb & Amawate, 2019 ). CRM campaigns also give the best chance to profit organizations to attract the customers towards organization and enhance customer loyalty (Galan-Ladero et al., 2013b ), as well as they create or enhance emotional engagement with target customers, build a strong relationship with them (Cone et al., 2003 ; Docherty & Hibbert, 2003 ), and also maintain the company’s goodwill (Chang & Chu, 2020 ). Consequently:

The for-profit organizations use CRM as a strategic tool to build a strong brand image in the customer’s mind (Ahluwalia & Bedi, 2015 ). And the internal benefit of the for-profit organization is to help increase the employee’s self-esteem, commitment, and loyalty (Cone et al., 2003 ; Polonsky & Wood, 2001 ).

The non-profit organizations try to increase awareness about the cause, educate the customers, and support the charitable cause (Nowak and Clarke, 2003 ). On the other hand, CRM in non-profit organizations increases the number of donors (Docherty & Hibbert, 2003 ; Polonsky & Wood, 2001 ).

For consumers , charitable causes, linked to their purchases, boost their feeling of happiness and inner satisfaction (Chaabane & Parguel, 2016 ), and they also feel good when helping others (Imas, 2014

3 Analysis and Main results

Due to an increasing number of CRM research papers that identify the most essential and main contributions in the field, and to objectify the outcomes, then bibliometric analysis is introduced. Zupic and Čater ( 2015 ) explained the five main bibliographic methods, which consists of citation analysis, co-citation analysis, bibliographic coupling, co-author analysis and co-word analysis. In this study, we apply Co-words or Co-occurrence analysis, Co-citation analysis, and cited journals analysis. These analyses were run on VOS-software.

3.1 Analysis of the different definitions of CRM

A wide variety of definitions of CRM have been contributed since 1988 (see Appendix 1, Table  7 ). In Table  2 , we summarize the main CRM definitions, from 1988 to 2020, according to the main keywords included in them: CRM as an activity (a marketing activity and/or a CSR activity), as a strategy, as a marketing mix tool, and as an alliance (between profit and nonprofit organizations). Thus, we can observe that there is not a general, unanimous agreement about its definition yet. However, the concept of CRM has evolved from being considered a short-term marketing mix tool (a promotion tool), to being considered a CSR initiative, with a more strategic character.

3.2 Time-wise development of CRM literature

However, in this study, we start from the time-wise development of Cause-Related Marketing. First, we identify the number of research articles into three time periods (decades): 1988–2000, 2001–2010, and 2011–2020 (previous systematic literature did not classify them into decades). With the growing body of Cause-Related Marketing, it is better to organize it in three decades because differences are appreciated, depending on the time.

Varadarajan and Menon introduced the CRM term in academia in 1988, and the following three decades witnessed gradual growth in CRM literature. Table  3 shows the annual evolution of this figure from 1988 to 2020.

Thus, we can classify the three decades based on CRM literature progression:

Introductory decade (1988–2000). The field of CRM was introduced in this period with a limited number of published articles (13). However, these articles were very innovative and aroused interest in this new solidarity initiative.

Emerging decade (2001–2010). The CRM field grabbed the attention of researchers in this second decade, with a notable increase in the published literature, especially in the last two years of this decade. The number of published articles reached 74. Consequently, CRM became an interesting and novel research topic, broadening its scope.

Most thriving decade (2011–2020). CRM literature witnessed a boom in the third decade, especially in the last two years of this decade. Thus, 257 articles related to the field of CRM were published in different journals only in this third decade.

In summary, we can indicate that CRM publications have grown significantly over the three decades analyzed, because more and more research papers have been published on this topic.

3.3 Author-based citation analysis

Author-based studies have long been one of the most important aspects of bibliometric analysis. This analysis includes the ranking of authors by the number of researches carried out, the citations of their research articles, their evolutions, or the analysis of co-authors’ collaborations. Table  4 shows the five most cited authors (and their specific works) from first decade (1988–2000), second decade (2001–2010), and third decade (2011–2020).

In this analysis, the most cited authors (and their corresponding works) for each decade have been the following:

From the first decade, the most cited authors are: Varadarajan ( 1988 ), with 734 citations; followed by Webb ( 1998 ), with 498; Smith ( 1991 ), with 121; File ( 1998 ), with 110; and Ross (1992), with 15 citations.

In the second decade, Barone (2007) is the most quoted, with 243 citations; followed by Gupta ( 2006a ), with 189; Lafferty ( 2005 ), with 187; Cui ( 2003 ), with 152; and Berglind ( 2005 ), with 103 citations.

During the third decade, Christofi (2020a) has been cited 176 times; Robinson (2012) has 150; Bae (2020), 149; Priporas (2020), 135; and Koschate-Fischer (2012), 129 citations.

In summary, we can highlight that Varadarajan ( 1988 ) is the most cited author of all time, with the first academic paper published on CRM, and serves as a reference for researchers around the world. And by far the next most cited authors are Webb ( 1998 ) and Barone (2007).

3.4 Co-words or co-occurrence analysis

A co-word analysis may be described as “ a content analysis technique that uses patterns of co-occurrence of pairs of items… in a corpus of texts to identify the relationships between ideas within the subject areas ” (He, 1999 , p. 134). Thus, co-words or co-occurrence analysis is a content analysis that connects words in the title of the research paper or abstract. The main idea of the co-word analysis is to connect any identified patterns into a map of contextual space. We also applied this analysis to each decade.

3.4.1 First phase (period 1988–2000)

For the 13 articles published from 1988 to 2000, the co-word analysis identifies four clusters consisting of the following words (with the minimum number of occurrences of keywords defined as 1; out of 26 keywords in this period, 26 met the threshold).

The first cluster includes consumer attitude, market segmentation, marketing strategy, profitability, and social responsibility (as shown in Fig.  2 , red color).

The second cluster deals with cause-related marketing, consumer perceptions, and philanthropy (as shown in Fig. 2 , green color).

The third cluster consists of charitable organizations and crm (as shown in Fig. 2 , blue color).

The last cluster relates to corporate philanthropy (as shown in Fig. 2 , yellow color).

figure 2

Co-Word analysis for the period 1988 to 2000

In summary, the co-word analysis shows that, for the period from 1988 to 2000, the focused keyword is Cause-Related Marketing . Other important keywords are philanthropy and consumer perception .

3.4.2 Second phase (period 2001–2010)

Based on the selection of 74 articles for the period 2001–2010, the co-word analysis shows a more precise picture than it does in the introductory decade (to narrow down the result, the minimum number of the occurrence of keywords was defined as 2; out of 160 keywords, and 27 meet the reduction criteria).

A notable cluster derived by the co-word analysis (Fig.  3 , red color) consists of the words brand alliances, cause-related marketing, corporate philanthropy, corporate social responsibility, donations, reputation, social responsibility, sponsorship, and work .

A second cluster (Fig. 3 , green color) comprises brand, company, consumer, framework, impact, information, price, responses, and skepticism.

The third cluster (Fig. 3 , blue color) is related to advertising, brand, cause marketing, consumer behavior, marketing, and purchase intention .

The fourth cluster (Fig. 3 , yellow color) consists of choice, corporate images, and purchase intention.

figure 3

Co-word analysis for the period 2001 to 2010

In summary, the co-word analysis shows that, from 2001 to 2010, the Cause-Related Marketing keyword related to the other striking keywords, such as corporate social responsibility, reputation, corporate image , and purchase intention .

3.4.3 Third phase (period 2011–2020)

Based on the selection of 257 articles for the period 2011–2020, the co-word analysis shows a more precise picture than it does in the previous two decades. To narrow down the result, the minimum number of the occurrence of keywords was defined as 5 (out of 825 keywords, and 40 meet the reduction criteria). The most notable clusters derived from the co-word analysis are:

First cluster (Fig. 4 , red color): it consists of the words attitude, attitudes, brand, choice, consumer responses, credibility, fit, impact, knowledge, motivation, responses, social-responsibility, sponsorship, and sustainability.

figure 4

Co-word analysis for the period 2011 to 2020

The second cluster (Fig. 4 , green color) comprises altruism, cause marketing, cause-related marketing, co-branding, consumer behavior, purchase intention, and skepticism.

The third cluster (Fig. 4 , blue color) comprises  altruism, behavior, charity, consumer choice, corporate social responsibility, mediating role, and s trategy .

The fourth cluster (Fig. 4 , yellow color) comprises consumption behavior, corporate strategy, ethics, marketing, millennials, and social media.

The fifth cluster (Fig. 4 , purple color) comprises brand-cause fit, corporate social responsibility, and perceptio n.

In summary, the co-word analysis shows that, also for the period from 2011 to 2020, the most focused keyword is Cause-Related Marketing . Other significant keywords are ethics , purchase intention , consumer behavior , and attitudes .

Consequently, the whole co-word analysis shows that all around the world, the researchers are focused on the one keyword that is “ Cause-Related Marketing ”, and the other most emphasis keywords are philanthropy and consumer perception , in the first decade; to evolve toward CSR, reputation, corporate image , and purchase intention , in the second decade; and finally focused on ethics, purchase intention, consumer behavior , and attitudes , in the third decade.

3.5 Co-citation analysis

A co-citation analysis is described as “ the scholarly contributions of authors who are frequently co-cited are likely to embody similar or related concepts ” (Nerur et al., 2008 , p. 321). Co-citation analysis can explain how referential frameworks of the Cause-Related Marketing field at different stages of its development affected evolutions in its general construction.

3.5.1 First phase for the period 1988–2000

Based on the co-citation analysis, for the period 1988-2000 (Fig. 5 ), it has been seen that there is predominately one cluster with a total 58 authors distributed in one cluster namely cluster – 1 with red color (minimum of the documents for an author should 1 and minimum citation of an author should be 1).

figure 5

Co-citation analysis of authors for the period 1988–2000

There is a wide variety of authors cited in the papers on CRM in the first decade (but only once). Schurr is the only author who receives 2 citations in this decade.

3.5.2 Second phase for the period 2001–2010

According to co-citation analysis for the period 2001-2010 (Fig. 6 ), it has been noted that there are predominately two clusters with a total 1502 authors (minimum citation of an author should be 10 and the maximum citation of the author should be 24).

figure 6

Co-citation analysis of authors for the period 2001–2010

In this analysis, we selected five top co-authors who have a high citation, such as Mohr (34 citations), Webb (32 citations), Menon (30 citations), Miyazaki (24 citations), and Varadarajan (21 citations).

3.5.3 Third phase for the period 2011–2020

According to co-citation analysis for the period 2011-2020 (Fig. 7 ), it has been noted that there are predominately four clusters, with a total of 10,108 authors and 193 thresholds (minimum of the documents for an author should be 20 and minimum citation of an author should be 5; the maximum citation of the author should be 200).

figure 7

Co-citation analysis of authors for the period 2011–2020

In this analysis, we selected the five top co-authors who had the highest citation, such as Lafferty (200 citations), Mohr (176 citations), Webb (167 citations), Barone (136 citations), and Bhattacharya (136 citations).

In summary, the co-citation analysis shows that Lafferty is the most co-cited author in all time. And the next most co-cited authors are Mohr and Webb.

3.6 Cross-cultural analysis

Cultural norms and beliefs have a significant impact on shaping people’s perceptions, attitudes, and behavior (Steenkamp, 2001 ). Lavack and Kropp ( 2003 ) identified the research gap of cross-cultural studies in the field of CRM. Hence, they conducted the first cross-cultural research in the field of CRM by including four countries from different regions such as Australia (Oceania), Canada (North America), Korea (East Asia) and Norway (Europe), and investigated the consumers’ role values towards the CRM. Since the third decade, more researchers have been participating and collaborating in cross-cultural studies. Table  5 details transversal research that has studied CRM comparing different countries.

According to the cross-sectional analysis, the nation has a different background of consumer and corporate cultures that varies from country to country. Sekaran ( 1983 , p. 68) defined it as “ Culturally normed behavior and patterns of socialization could often stem from a mix of religious beliefs, economic and political exigencies and so on. Sorting these out in a clear-cut fashion would be extremely difficult, if not totally impossible ”. Therefore, the scholars are taking more consideration in cross-cultural CRM study from the second decade. In this study, Table 5 shows that researchers from USA (i.e., North America) and South Korea (i.e., East Asia) studied together two times on culture analysis, one times with India (i.e., South Asia), and one time with Poland (i.e., Europe), one times with Philippines (i.e., East Asia) as well as China (i.e., East Asia). In addition, Italian researchers (i.e., Europe) studied one time on culture analysis with Japan (i.e., East Asia) and one time with Brazil (i.e., South America). Furthermore, India (i.e., South Asia) collaborated with Philippines (i.e., East Asia). Cross-cultural analysis in Table 5 shows that the researchers worked on four different cultures analysis rather than two cultures (i.e., Lavack & Kropp, 2003 ; and Schyvinck & Willem, 2019 ).

3.7 Country-wise development of CRM literature

Figure  8 reports regional (i.e., country-wise) participation of different researchers in the development of CRM literature. As this concept was introduced in the USA (Varadarajan and Menon, 1988 ), the studies from the first and second decades usually belonged to this geographical region. Thus, most of the research in CRM literature was published by researchers from US Universities: 92% of the contributions in the first decade (i.e., 12 research articles), and 40% (i.e., 30 research articles) in the second decade. However, some British researchers also contributed to CRM literature in the second decade, with 11% (i.e., 8 research articles) share of total CRM publications. Nevertheless, in the third decade, most of the CRM literature was published by Asian researchers. Hence, Indian researchers, with 9% (i.e., 24 research articles), and Taiwanese scholars, with 5% (i.e., 12 research articles), jointly published almost 14% of the articles in that decade. Although the American contributions fell to 33% (but only in relative terms, since in absolute terms they reached 85 research articles), their overall contribution remains the highest of all countries. And participation of British scholars was 6% (i.e., 15 research articles) in the third decade. In this Fig.  8 , we observed that the USA research publications from every decade are very extensive, in comparison to other countries.

figure 8

List of cited Country-Wise Development of CRM Literature. Source: Own Elaboration

3.8 Methodological development in CRM literature

Methodological development in CRM literature is graphically shown in Fig.  9 . It is observed that most research work is employed by experimental design. In the first decade (1988–2000), researchers focused on qualitative or quantitative research in the field of CRM; whereas a mixed-method approach has been used in the second (2001–2010) and third decade (2011–2020). In this analysis, we observe that, in general, quantitative studies significantly outnumber qualitative studies, especially in the third decade.

figure 9

Methodological development in CRM Literature. Source: Own Elaboration

3.9 Role of journal in development of CRM literature

Although a total of 141 journals have published articles explaining the concept of CRM from different perspectives (see Appendix 2, Table  8 ), only six journals published more than ten CRM papers. These journals are International Marketing Review (26), International Review on Public and Nonprofit Marketing (20), Journal of Nonprofit & Public Sector Marketing (17), International Journal of Nonprofit and Voluntary Sector Marketing (15), Journal of Business Ethics (13), and Journal of Business Research (10). In the field of CRM, almost 100 journals have been published only a single article since its conceptualization in 1988 (they are also shown in Appendix 2, Table 8 ).

There are two indicators to measure the scientific influence of scholarly journals, such as Journal Citation Reports and Scimago Journal & Country Rank. This study only considers the Scimago Journal & Country Rank because there are more research articles in this rank, which divides the set of journals into four quartiles (i.e., Q1, Q2, Q3, and Q4). According to Scimago Journal & Country Rank (SJR), we observed that 51 journals publishing about CRM are Q1, 36 are Q2, 16 are Q3, and 5 are Q4. On the other hand, 8 journals are not included yet in any Quartil. and 25 journals are not in this index.

According to Persson et al. ( 2009 ), for the bibliographical data, we used BibExcel, which presents co-occurrence of references in the bibliographic of research articles. Therefore, in this study we find that five most cited journals by each decade, from the first decade (1988–2000), are: Journal of Marketing (1 document; cited in 734 articles), Journal of Public Policy & Marketing (1 document; cited in 498 articles), Journal of Consumer Marketing (1 document; cited in 121 articles), Journal of Business Ethics (1 document; cited in 110 articles), and Journal of the Academy of Marketing Science (2 document; cited in 51 articles). From the second decade (2001–2010): Journal of Consumer Marketing (3 documents; cited in 369 articles), Journal of Business Research (3 documents; cited in 280 articles), Journal of Retailing (1 document; cited in 243 articles), Journal of Nonprofit and Public Sector Marketing (11 documents; cited in 215 articles), and Journal of Advertising (5 documents; cited in 172 articles). And from the third decade (2011–2020): Journal of Marketing Review (25 documents; cited in 842 articles), Journal of Business Ethics (11 documents; cited in 458 articles), International Journal of Nonprofit and Voluntary Sector Marketing (9 documents; cited in 407 articles), International Journal of Advertising (8 documents; cited in 347 articles), and Journal of Marketing (2 documents; cited in 244 articles).

For this analysis, we observed that the percentage of the most cited paper, published in the Journal of Marketing , dropped due to the introduction of different journals, such as Journal of Marketing Review , International Journal of Advertising , or International Journal of Nonprofit and Voluntary Sector Marketing . But, on the other hand, the Journal of Business Ethics has increased the citations.

4 Discussion

This research provides an inclusive review of the systematic literature with respect to three decades: the introductory decade (1988–2000), the emerging decade (2001–2010), and the thriving decade (2011–2020).

In this study, we observed that North American researchers are more involved in Cause-Related Marketing. This may be due to the importance that CRM has had in the USA since its inception, and the acceptance that CRM has had among American companies and consumers. Such as Cone ( 2010 ) showed, 88% of the American consumers supported the cause, 85% of the consumers had a good image of the company or product supporting a noble cause and cared about it, and 90% of the consumers demanded companies to find the right cause to support. More recently, another research also studied that 87% of American consumers would purchase a CRM product if the company supported the charitable cause (business2community, 2020 ).

The graphical presentation of the Time-Wise Development (see Fig. 1 ) shows 13 articles published until 2000 (first decade), 74 articles from 2001 to 2010 (second decade), and 257 articles from 2011 to 2020 (third decade). Natarajan et al. ( 2016 , p. 248) and Thomas et al. ( 2020 , p. 5) verified almost similar findings of the time-wise development from 1988 to 2016. But after that, the research on Cause-Related Marketing has abruptly increased in the last two years (2019–2020). We noticed that, in 2020, the researchers are more actively involved in the CRM field than the previous years to publish the research articles.

As observed in Fig. 8 , the academicians and researchers from 37 different countries have significantly contributed to CRM studies. A large portion of CRM studies are conducted in two regions (i.e., North America and Europe). Thomas et al. ( 2020 ) shows similar results. Asian (i.e., Indian and Taiwanese) researchers have taken more interest in CRM and they have been publishing more and more articles since the third decade. On the other hand, we also noticed that the CRM topic was first introduced in Western societies (with Christian tradition). But after the first decade and during the second decade, CRM studies were also introduced in Muslim countries, such as Pakistan (1 research article) and Oman (1 research article). From the third decade, the researchers also explored other Muslim countries, such as Malaysia (5 research articles), Iran (4 research articles), Egypt (4 research articles), Turkey (3 research articles), Pakistan (3 research articles), Jordan (1 research article), Indonesia (1 research article), and Bangladesh (1 research article). So, in summary, we can highlight that researchers have been exploring the Muslim world in the field of CRM after the second decade.

And about the methodological development in CRM literature (see Fig. 9 ), the researchers have used more quantitative studies, compared to qualitative studies. Thomas et al. ( 2020 , p. 7) also found a similar result. Thus, the trend seems to be for quantitative studies to continue to predominate over qualitative ones in the coming years, although mixed methods are experiencing slight growth. However, the combination of both types of studies, qualitative and quantitative, could offer more complete studies on CRM.

Lastly, Table 6 presents the journals involvement to publish CRM research articles. In our study, the key publications journals are International Marketing Review, and International Review on Public and Nonprofit Marketing. Our results have been slightly different from Thomas et al. ( 2020 )‘s and Natarajan et al. ( 2016 )‘s. These researchers found that the Journal of Nonprofit & Public Sector Marketing was the one that had published more research articles on this topic. But probably this difference is because they only considered up to 2016.

Our research also discovered different results from previous studies with respect to databases, partly due to the number of databases considered and the greater number of years analyzed in our study.

5 Conclusion

Cause-Related Marketing (CRM) is considered as an initiative that involves a donation to a specific cause, at a specific period of time, usually with a limited donation amount, and which depends on product sales and consumer behavior.

Therefore, the main objective of this study was to provide a comprehensive systematic review of the literature on CRM, categorizing each article by time-wise development, country-wise development, methodological development, and role of journals. Cross-cultural analysis and bibliometric analysis were also included, as a new contribution of this research, in comparison to previous studies.

The main studies have been classified in three decades, which present significant differences. In the introductory decade (1990–2000), the field of CRM was introduced with limited published articles with the role of CRM in two different regions, such as North America, and Oceania.

First three Journals such as International Marketing Review, International Review on Public and Nonprofit Marketing, or Journal of Nonprofit and Public Sector Marketing, play a starring role to publish CRM research papers. In the emerging decade (2001–2010), researchers explored more regions, such as East Asia, South Asia, and the Middle East. In this time frame, mix approach studies and cross-cultural studies were introduced for the first time in the field of CRM. And in the most thriving decade (2011–2020), scholars analyzed CRM in two more regions, such as North Africa and Sub-Saharan Africa. In this era, more scholars were interested in collaborating with other geographical regions such as North America and Europe. The number of published papers on CRM grew significantly.

However, this current study has some limitations. First, this research considered only two keywords: “Cause-Related Marketing” and “Cause Marketing”. Thus, other terms might be also considered, such as “Social Cause” or “Cause–brand alliance”. Secondly, the selection of the studies was limited only to the peer-reviewed journal research articles published in English. Maybe research articles in other languages could be also interesting. Thirdly, this current study just focuses on the full-text journal papers. Abstracts, theses, working papers, and conference proceedings were ignored. Fourthly, this study has used a limited number of databases to find the research articles: SAGE Journals, JSTOR, Emerald Insight, Springer, Wiley Online Library, Elsevier, Taylor & Francis Online, and Google Scholar. Other databases, such as EBSCO and ABI/INFORM, could have been also considered.

Anyway, we also found different gaps in CRM research, so further research could be developed in these aspects. First, most academic scholars have largely focused on the developed countries, such as the USA and the UK, and less in developing countries (especially in the first and second decades). Although studies about CRM in developing countries increased in the third decade, the gap still exists. More studies are needed about developing countries because the researchers may find different results. In addition, more studies are also required to compare developed and developing countries, because researchers could find different interesting outcomes about CRM campaigns.

Secondly, the growing popularity of the internet and social media could be more considered by the companies, which could focus on digital marketing. Therefore, consumers could be more involved in a digital CRM campaign (Handa & Gupta, 2020 ). Only few studies have been conducted in this area, so the gap still exists, both in developing and developed countries.

Thirdly, various studies are conducted on the cross-culture context. But more research is needed to investigate the cross-cultural context, comparing developing and developed countries, and also Western and Eastern countries. Causes and consumer preferences or attitudes could be different from one country to another country.

Fourthly, few studies have been conducted in the mix approach (including qualitative and quantitative studies). More research is required for a better understanding of the mixed methodological approach in CRM. The most common and well-known approaches to mixing methods are Triangulation Design, Embedded Design, the Explanatory Design, and the Exploratory Design. These methodologies could be discussed in CRM programs.

Fifthly, profit and non-profit organizations depend on each other in CRM campaigns. When both organizations develop CRM strategies, they can acquire risk. Few studies have been conducted on profit and non-profit organizations with CRM programs; therefore, this also needs to be discussed.

Finally, and according to Chéron et al. ( 2012 ), consumers positively view those CRM campaigns that take place for extended periods of time, and they might be disappointed with short duration campaigns. Thus, time frame of the CRM campaign can have a significant impact on the consumers’ perception. Consequently, the campaign’s time duration is another factor that is needed to be more discussed by researchers.

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Bhatti, H.Y., Galan-Ladero, M.M. & Galera-Casquet, C. Cause-related marketing: a systematic review of the literature. Int Rev Public Nonprofit Mark 20 , 25–64 (2023). https://doi.org/10.1007/s12208-021-00326-y

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Mapping research in marketing: trends, influential papers and agenda for future research

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This study aims to map the conceptual structure and evolution of the recent scientific literature published in marketing journals to identify the areas of interest and potential future research directions.

Design/methodology/approach

The 100 most influential marketing academic papers published between 2018 and 2022 were identified and scrutinized through a bibliometric analysis.

The findings further upheld the critical role of emerging technologies such as Blockchain in marketing and identified artificial intelligence and live streaming as emerging trends, reinforcing the importance of data-driven marketing in the discipline.

Research limitations/implications

The data collection included only the 100 most cited documents between 2018 and 2022, and data were limited only to Scopus database and restrained to the Scopus-indexed marketing journals. Moreover, documents were selected based on the number of citations. Nevertheless, the data set may still provide significant insight into the marketing field.

Practical implications

Influential authors, papers and journals identified in this study will facilitate future literature searches and scientific dissemination in the field. This study makes an essential contribution to the marketing literature by identifying hot topics and suggesting future research themes. Also, the important role of emerging technologies and the shift of marketing toward a more data-driven approach will have significant practical implications for marketers.

Originality/value

To the best of the authors’ knowledge, this is the first comprehensive study offering a general overview of the leading trends and researchers in marketing state-of-the-art research.

  • Bibliometric analysis
  • Citation analysis
  • Research publications
  • Science mapping
  • Análisis bibliométrico
  • Análisis de citas
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Ramos, R. , Rita, P. and Vong, C. (2023), "Mapping research in marketing: trends, influential papers and agenda for future research", Spanish Journal of Marketing - ESIC , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SJME-10-2022-0221

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Copyright © 2023, Ricardo Ramos, Paulo Rita and Celeste Vong.

Published in Spanish Journal of Marketing - ESIC. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Marketing is vital to all businesses’ survival, long-term growth, development and success ( Czinkota et al. , 2021 ). Generally, the domain of marketing encompasses (1) the identification of marketing opportunities, (2) the creation of competitive advantages, (3) the effective utilization of resources, (4) the communication and delivery of products or services to customers, (5) the creation of value to customers and (6) the satisfaction of customers’ needs profitably ( Simkin, 2000 ).

The evaluation of academic marketing literature has progressively become relevant in recent years ( Das et al. , 2022 ; Hair and Sarstedt, 2021 ). The increasing number of academic publications in marketing varies in different contributions, which made it difficult for scholars to track new trends and find influential manuscripts to advance the body of knowledge. The primary objective of a research publication is to be known and influence others’ work. Nevertheless, the created knowledge is fragmented, and the emergence of new marketing topics is continuously changing the research map of marketing. Moreover, marketing is an applied discipline in that marketing research not only aims to generate scientific knowledge but also to provide insights and knowledge that can be practically used to inform marketing decisions ( Jedidi et al. , 2021 ). In addition, technological advancement has rapidly affected marketing practices and management ( Amado et al. , 2018 ). To address this challenge, this paper aims to map the conceptual structure and the evolution of knowledge to uncover the existing topics, trending areas of interest and future directions.

Despite considerable research efforts in the marketing field, little has been done to review prior research works systematically. Moreover, recent review articles have mainly focused on specific marketing domains or are limited to particular contexts, such as customer experience ( Chauhan et al. , 2022 ), marketing communication ( Domenico et al. , 2021 ), customer engagement ( Chen et al. , 2021 ), consumer behavior ( Oliveira et al. , 2022 ), advertising ( Jebarajakirthy et al. , 2021 ) and product or brand positioning ( Saqib, 2021 ), while context-specific reviews include marketing in emerging markets ( Paul et al. , 2016 ), sustainable marketing ( Lunde, 2018 ), business-to-business marketing ( Pandey et al. , 2020 ), luxury brand marketing ( Arrigo, 2018 ) and tourism marketing ( Han and Bai, 2022 ). The lack of a holistic review of marketing research created a gap in the existing research. Therefore, it is necessary to provide a big picture of the most recent marketing literature. The most recent review work in the same vein was conducted by Morgan et al. (2019) , who evaluated 257 marketing strategy articles published in the six most influential marketing journals during 1999–2017. Nevertheless, given its focus on marketing strategy and limited research sources, it does not provide a comprehensive framework that covers all aspects of the marketing field. To complement the work by Morgan et al. (2019) , this paper conducts a review with a more recent timeframe that focuses on recent trends, patterns and development in the field. The inclusiveness of journals will also enable identifying areas of interest beyond marketing strategy.

What is the knowledge structure of the state-of-the-art most influential academic research in marketing?

What are the current research trends?

What are possible pathways for future research in marketing?

The present work will facilitate the understanding and advancement of theories and knowledge in the field. Also, this paper provides valuable insights into the field’s most relevant and pressing issues and informs where future research efforts should be focused. This will, in turn, improve the practical relevance and usefulness of future research and ensure that research efforts are targeted toward topics that will yield impactful results. Moreover, it offers up-to-date information for marketing researchers.

2. Methodology

This study focuses on characterizing the most influential academic marketing articles published between 2018 and 2022 and discussing the marketing state of the art.

2.1 Search strategy

A search string was applied in the Scopus database to find the most relevant articles for this research ( Ramos et al. , 2019 ). The Scopus database was chosen for the literature review as it is generally considered one of the largest repositories with the most relevant indexed publications and one of the most universally acknowledged bibliographic databases ( Kumar et al. , 2020 ). It is recognized as the most well-organized and of the highest credibility and quality standards, with the most significant global impact and more comprehensive cover ( Muñoz-Leiva et al. , 2015 ; Rojas-Lamorena et al. , 2022 ) and is consistent with previous bibliometric reviews applied in the marketing research setting ( Kumar et al. , 2021 ; Paul and Bhukya, 2021 ). In addition, it follows Donthu et al. (2021) ’s recommendation to select only one database to minimize human errors during analysis. All marketing journals (212) indexed in Scopus were included in the current study. The journal selection takes a rather inclusive approach instead of the sole inclusion of marketing-specific journals, as marketing is a diverse and evolving field not strictly tied to a single-subject field ( Baumgartner and Pieters, 2003 ) but often intersects with other disciplines. For instance, given the rapid advancement of technology and its influence on marketing practices, topics such as information systems or big data are growing in importance and relevance to the marketing literature ( Amado et al. , 2018 ). Accordingly, journals such as the International Journal of Information Management have also contributed significantly to marketing recently ( Veloutsou and Ruiz Mafe, 2020 ). The search was conducted on June 9, 2023.

2.2 Selection process and final data set

The search was conducted in the Scopus database and limited to 2018 to 2022 to obtain state-of-the-art articles. Five years is a reasonable timeframe to capture a discipline’s essence and to conduct a bibliometric analysis ( Borgohain et al. , 2022 ). The collection of articles over five years reflects varied, robust, broad, inclusive and unrelated marketing research interests in the marketing field ( Bettenhausen, 1991 ). The focus on the most recent works permits uncovering the most recent trends without the influence of older topics. Only articles were selected as they represent the most advanced and up-to-date knowledge and are recognized for their academic value ( Rojas-Lamorena et al. , 2022 ). In total, 44,767 articles were collected. To select the most recent influential marketing articles, the top 100 most cited articles were selected. The citation metric acknowledges the impact of the articles ( Donthu et al. , 2021 ) and reflects the impact of scholarly work in subsequent research ( Purkayastha et al. , 2019 ).

In addition, it is recognized as one of the most relevant metrics of academic research ( Dowling, 2014 ). Although assessing the influence of an article based on citation analysis represents a significant limitation because articles may be cited for multiple reasons, citation analysis is considered an objective approach that exhibits less systematic biases for research impact evaluation ( Baumgartner and Pieters, 2003 ). Previous works have used citation metrics for bibliometric analysis. For instance, Law et al. (2009) analyzed the most influential articles published in Tourism journals using citation counts, whereas Brito et al. (2018) identified the areas of interest in football research and listed the articles based on citation frequency. From each article, the following variables were retrieved: authors’ names and keywords, document title, year, source title and citation count. The information was extracted in CSV file format.

2.3 Final data set

The final data set includes 100 articles from 28 journals. The authors’ names were reviewed for normalization purposes as they have different nomenclatures in different articles (e.g. Dwivedi YK vs Dwivedi Y) so that the software understands them as the same.

2.4 Data analysis

The CSV file with the final data set was input for the bibliometric analysis. Data were analyzed using the mapping analysis R-tool bibliometrix ( Aria and Cuccurullo, 2017 ). This package allows different types of analysis, offering an overview of the research field. A bibliometric analysis permits to analyzing the bibliographic material quantitatively, providing an objective and reliable analysis ( Broadus, 1987 ; Sepulcri et al. , 2020 ) and summarizing the existing literature and identifying emerging topics of research ( Hota et al. , 2020 ). The authors’ names and keywords, year of publication, source title and the number of citations were collected from each article. A performance analysis was performed to acknowledge the field’s citation structure, most relevant sources, authors and articles. Then, science mapping analysis through a co-occurrence analysis was performed. The co-occurrence analysis aims to overcome the descriptive nature of the bibliometric analysis, uncovering gaps and research trends ( Palmatier et al. , 2018 ; Quezado et al. , 2022 ). The gaps and research trends led to a future research agenda.

3. Results and discussion

3.1 total citations by year.

As indicated in Table 1 , the 100 articles were cited 41,888 times, an average of 418.88 citations per article. The most contributing years were 2019 and 2020, with 33 published articles yearly. The year with the highest number of citations was 2019, with 14,621 citations, corresponding to 34.90% of the total citations. This record is strongly linked to the work of Snyder (2019) , with 1,872 citations that characterized different types of literature reviews and suggested guidelines on conducting and evaluating business research literature reviews. Due to the increasing number of publications, it is challenging to keep current with state-of-the-art research ( Briner and Denyer, 2012 ). Reviewing the existing research is fundamental for understanding marketing research inconsistencies, gathering and synthesizing previous research and serving as guidance for researchers and practitioners. In addition, literature reviews contribute to identifying potential gaps, suggesting novel research lines and allowing a balanced growth of a research field ( Hulland and Houston, 2020 ).

The year with the highest mean total citations per article and year was 2021 (527.5 and 175.83, respectively). This result is highly associated with Donthu et al. (2021) ’s work, with 1,221 citations, that explained how to develop a bibliometric analysis.

The main difference between a literature review and bibliometric analysis is the focus and the methodological approach. A literature review aims to critically analyze and synthesize existing knowledge under a research topic ( Snyder, 2019 ). In turn, a bibliometric analysis is a specific approach within the field of scientometrics that uses quantitative and statistical methods to analyze the scientific production and articles’ characteristics published in a specific research domain ( Aria and Cuccurullo, 2017 ).

3.2 Most influential articles

Seminal articles in marketing assume an essential role in its development ( Berry and Parasuraman, 1993 ). The number of citations was used to define and measure the impact of the most influential articles. The most cited document (total citation = 1,872) was Snyder’s (2019) work on conducting an overview and suggesting guidelines for conducting a literature review ( Table 2 ). The normalized citation compares an article’s performance to the data set’s average performance ( Bornmann and Marx, 2015 ; Rita and Ramos, 2022 ). Snyder (2019) ’s work has the highest normalized citation index (4.13), revealing its outstanding performance compared with the remaining articles from the data set.

Among the top 10 most cited articles, three are related to PLS-SEM. The partial least squares – structural equation modeling (PLS-SEM) is relevant for marketing as it allows to examine of complex relationships between latent variables and manifest variables, permitting a flexible and less restrictive analysis in terms of statistical assumptions than other modeling techniques, such as confirmatory factor analysis and principal component analysis ( Hair et al. , 2020 ). By using PLS-SEM, marketing researchers can explore complex relationships among variables, test research hypotheses, identify the relative importance of different influencers and assess the validity and reliability of the measured variables ( Sarstedt et al. , 2019 ). It is frequently used in research involving the modeling of theoretical constructs, such as customer satisfaction ( Ramos et al. , 2022 ), brand image ( Kunkel et al. , 2020 ) or perceived quality ( Ariffin et al. , 2021 ) research.

Surprisingly, there are no articles from 2018 in the top 10 most cited articles. However, there are two articles published in 2021. One of the papers published in 2021 is the work of Verhoef et al. (2021) , which explores digital transformation and innovation in business models and suggests a research agenda for future studies. Digital transformation and innovation are highly relevant for marketing as it provokes consumer behavior change ( Lemos et al. , 2022 ). In addition, it allows companies to adapt to consumer behavior changes, seize the opportunities for segmentation and personalization, improve communication and engagement and increase operational efficiency ( Muneeb et al. , 2023 ; Zhang et al. , 2022 ).

3.3 Source impact

Table 3 depicts the top 10 most impactful sources of the 100 most influential marketing articles. The intellectual convergence is exhibited based on common sources and referencing patterns ( Donthu et al. , 2021 ), and identifying journals may facilitate future literature search and scientific dissemination.

Among the 28 journals, the International Journal of Information Management (IJIM) contributed the most papers (26 papers), followed by the Journal of Business Research (JBR) (22 papers) and the Journal of Retailing and Consumer Services (JRCS) (6 papers). These journals are all First Quartile journals based on SCImago Journal Rank (SJR) indicator, with an impact factor of 4.906, 2.895 and 2.543, respectively. The IJIM focuses on contemporary issues in information management ( Elsevier, 2023a ). Information management field of research plays a fundamental role in marketing, providing data and insights that guide marketing strategies, improve segmentation and customization, leverage automation marketing, data-driven decision-making and the performance evaluation of marketing initiatives ( Dwivedi et al. , 2020 ). The JBR aims to publish recent business research dealing with the spectrum of actual business practical settings among different business activities ( Elsevier, 2023b ), while the JRCS focuses on consumer behavior and policy and managerial decisions ( Elsevier, 2023c ). The findings indicate the contribution and importance of IJIM to the marketing field, recognizing the relevance of information management. Surprisingly, leading marketing journals listed in the Financial Times 50 ( Ormans, 2016 ), such as the Journal of Consumer Research , Journal of the Academy of Marketing Science and Journal of Marketing , only produced a small number of relevant articles in our data set. This result suggests that their papers may not be as impactful or influential as those published in other outlets. Nevertheless, the quality of the articles published in these outlets reflects the most original and well-executed research, as they have high submission rates. However, their rate of acceptance is very low.

Among the top 10 most productive journals, JBR is the one with the highest number of citations. This result confirms Table 2 ’s results as it lists six articles that were published in this journal ( Donthu et al. , 2021 ; Hair et al. , 2020 ; Sheth, 2020 ; Sigala, 2020 ; Snyder, 2019 ; Verhoef et al. , 2021 ).

3.4 Contributing authors

Key authors are essential to the field’s structure and growth ( Berry and Parasuraman, 1993 ) and positively influence the most impactful articles ( Rojas-Lamorena et al. , 2022 ). Thus, it is imperative to identify them and acknowledge their impact. Between 2018 and 2022, 100 documents were written by 312 different authors.

Table 4 characterizes the top 10 most productive authors among the most influential marketing research articles over the past five years. The authors’ indices were calculated, including h -index, g -index and m -index. The Hirsh index ( h -index) is the proposal to quantify productivity and the journal’s impact considering the number of papers and citations per publication ( Hirsch, 2005 ). The g -index aims to measure the performance of the journals ( Egghe, 2006 ), considering the citation evolution of the most cited papers over time. Furthermore, the m -index, also called the m -quotient, considers the h -index and the time since the first publication ( n ); hence, m -index = h -index/ n ( Halbach, 2011 ).

Professor Dwivedi YK is the most prolific, with seven published articles indicating more than one paper yearly. Although he is placed second as the most cited author (3,361), he has the highest h - (7), g - (7) and m -index (1.17). Professor Dwivedi’s research focuses on digital innovation and technology consumer adoption and the use of information systems and information technology for operation management and supply chain, focusing on emergent markets. Digital innovation and understanding technology consumer adoption allow companies to engage with consumers efficiently and personally ( Alalwan et al. , 2023 ). In addition, information systems and information technology applied in operation management and supply chain permit a higher efficiency and visibility in commercial activities, aiding companies to optimize processes, reduce costs and improve customer care ( Tasnim et al. , 2023 ). Professor Dwivedi is a Professor at the School of Management, Swansea University, UK ( Swansea, 2023 ). The second most productive author is Hair JF, and Hughes DL, with five articles each. Professor Hair JF is the most cited author in the list of the most productive authors. This record is highly associated with the work “Assessing measurement model quality in PLS-SEM using confirmatory composite analysis” ( Hair et al. , 2020 ), with 1,103 citations. Multiple papers gather authors from the list. For instance, the article “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy” ( Dwivedi et al. , 2021 ) was co-authored by Professors Dwivedi YK and Hughes DL. This paper has 637 citations and addresses the transformative power that artificial intelligence (AI) may have for the automation and replacement of human tasks, highlighting opportunities, challenges and impacts. AI plays a fundamental role in marketing, permitting advanced personalization, task automation, advanced data analysis, campaign optimization and improved customer experience, leading to personalized experiences and better marketing results ( Duan et al. , 2019 ; Dwivedi et al. , 2021 ).

Fractionalized frequency displays the multiauthored articles. This analysis is relevant to understand how researchers interact with each other ( Rojas-Lamorena et al. , 2022 ). A credit is attributed to each author, depending on the number of co-authors. If a paper has two authors, each receives a half-point. If a paper has three authors, each receives a third of a point, and so on ( Cuccurullo et al. , 2016 ). Professor Hughes DL has the lowest score (0.57) on the five most productive authors list, suggesting a strong relationship with colleagues through co-authorship based on shared interests.

3.5 Co-occurrence analysis

Figure 1 presents the authors’ keywords co-occurrence analysis and reflects the relationship between the keywords and the data set ( Wang et al. , 2012 ). Co-occurrence analysis aims to establish relationships and map the conceptual structure of the most influential marketing academic articles and reveal current research trends ( Eduardsen and Marinova, 2020 ). The thicker the lies among each cluster, the stronger the connection between the keywords. The size of each edge indicates the occurrence frequency. Thematic map displays the top 50 keywords and a minimum of 5 clusters. The thematic map shows six clusters, of which two are with the largest nodes, including AI (brown) and Covid-19 (blue). However, clusters with smaller nodes are bibliometric analysis (red), social media (purple), blockchain (green) and customer engagement (orange).

The brown cluster suggests a topic under AI technology. The cluster’s keywords highlight an interconnection and application of AI, machine learning and cognitive computing in the marketing research field. Deep learning, natural language processing and machine learning make part of a broader spectrum of AI ( Verma et al. , 2021 ). Cognitive computing refers to the capacity of computer systems to mimic human capacity to process information, learn and make decisions ( Duan et al. , 2019 ). These technologies handle big data efficiently, predict consumer behavior and support decision-making in actionable insights, transforming marketing strategies ( Blanco-Moreno et al. , 2023 ; Dwivedi et al. , 2021 ).

The blue cluster reflects the pandemic that affected the globe between 2020 and 2023 ( United Nations, 2023 ). This cluster reveals a close relationship between the Covid-19 pandemic and consumer behavior ( Sheth, 2020 ). The interest in understanding the attitudes and consumers’ decision-making is highly relevant for future pandemics ( Pereira et al. , 2023 ). In addition, the pandemic brought social and industry challenges that deserve academic attention ( Dwivedi et al. , 2020 ; Muneeb et al. , 2023 ). This cluster also addresses overconsumption driven by impulsive behavior promoted by the pandemic ( Islam et al. , 2021 ; Marikyan et al. , 2023 ). This cluster suggests insights on how companies can adequately develop marketing strategies to face the pandemic challenges and effectively respond to health crises.

The red cluster reveals a direct connection between bibliometric analysis and scientific assessment. The bibliometric analysis is applied to reveal research patterns and knowledge structure and access the scientific production impact ( Ramos and Rita, 2023 ). The use of bibliographic coupling, co-occurrence analysis and the Scopus database supplies the data set for the identification of relationships and patterns within the literature ( Donthu et al. , 2021 ), summarizing the existing literature and identifying emerging topics of research ( Hota et al. , 2020 ).

The purple cluster highlights the terms social media and marketing. The keyword social media highlights the role of platforms, such as Instagram or TikTok, for advertising ( Alalwan, 2018 ), understanding the role of influencers ( Lou and Yuan, 2019 ), and for co-creation in brand communities ( Kamboj et al. , 2018 ), influencer marketing. Social media platforms are fundamental for any communication strategy as they connect with the audience, create engagement and awareness and promote products and services ( Lou and Yuan, 2019 ). The strategic use of social media in marketing is fundamental for companies to establish an effective presence and build long-lasting relationships.

The orange cluster suggests a relationship between live streaming and customer engagement ( Wongkitrungrueng and Assarut, 2020 ). This interconnection suggests that live streaming can be an effective channel for developing social commerce, influencing purchase intentions ( Sun et al. , 2019 ). Real-time and direct interaction with customers promote greater involvement and improve customer experience.

The green cluster suggests a focus on applying blockchain technology in information systems. Blockchain is a decentralized and immutable technology for transaction registers studied in the supply chain context ( Min, 2019 ). It has a significant potential to transform data management ( Lemos et al. , 2022 ).

4. Conclusions and future research agenda

This study represents a map of the conceptual structure and evolution of the state-of-the-art scientific literature published in marketing journals to identify the areas of interest and potential future research directions. This review aimed to (1) acknowledge the structure of the state-of-the-art most influential academic marketing research, (2) identify current research trends and (3) suggest future research prospects.

4.1 RQ1: knowledge structure

Regarding RQ1, the most cited article among the top 100 between 2018 and 2022 was the work of Snyder (2019) , with 1,872 citations, followed by the work of Donthu et al. (2021) , with 1,221. The years 2019 and 2020 were those that most contributed to the top 100 most cited, with 33 articles each. Accordingly, these years had the most citations, 14,621 and 13,692, respectively. The IJIM was the source with the highest number of articles published from our data set ( n = 26). However, the JBR, with 22 published articles, was the journal with the highest citations ( n = 12,265). Every journal from the top 10 prolific sources is ranked in Scopus (SJR) as Q1. Professor Dwivedi YK was the most prolific author, with seven articles published, followed by Professors Hair JF and Hughes DL, with five articles each. Although placed second on the most productive authors list, the most cited author was Professor Hair JF, with 3,615 articles.

4.2 RQ2: current research trends

As for RQ2, this bibliometric analysis allowed us to identify current research trends through the co-occurrence analysis. Since a comprehensive future research agenda stimulates researchers to continue their research efforts ( Hulland and Houston, 2020 ), we suggest marketing future research questions to gain a deeper knowledge of current research trends ( Table 5 ).

Although AI has existed for over six decades ( Duan et al. , 2019 ), the development of supercomputers that analyze big data led to the exponential use of this technology. Its application in marketing varies and includes trend and prediction analysis, chatbots and marketing automation. However, particularly for data analysis, multiple research questions are yet to be answered ( Dwivedi et al. , 2021 ). Grounded on the AI (brown) cluster, it would be interesting to uncover different uses of AI to improve big data analysis.

The Covid-19 pandemic disrupted global habits ( Sheth, 2020 ). New habits emerged, changing the industry landscape in multiple dimensions, such as consumer, leisure and work behavior. Although multiple studies were published regarding the topic, much is yet to be uncovered. The effects of this pandemic are yet to be fully acknowledged, demanding future studies to comprehend the permanent changes in society ( Islam et al. , 2021 ). In addition, uncovering the best-implemented industry marketing strategies can be helpful, as it is inevitable that new pandemics occur in the future ( Pereira et al. , 2023 ).

Bibliometric analyses map and summarize existent research, extending the global understanding of a research topic and increasing the quality and success of scholarly work ( Donthu et al. , 2021 ). However, the analysis is mainly descriptive ( Ramos and Rita, 2023 ). Combining bibliometric analysis with other methods may enhance the results, leading to an advancement in using such an approach.

Social media is broadly used for marketing-related activities. Through social media platforms, it is possible to build brand image, generate leads for the company’s website, analyze and monitor data, or be an influencer marketer ( Alalwan, 2018 ; Lou and Yuan, 2019 ). Nevertheless, the implementation of gamification techniques ( Bhutani and Behl, 2023 ; Wanick and Stallwood, 2023 ), privacy concerns ( Saura et al. , 2023 ) and collective decision-making ( Dambanemuya et al. , 2023 ) are issues that deserve the attention of researchers.

Livestreaming captured the attention of digital retailing marketers in recent years and significantly changed social interaction. However, different types of live streaming exist, such as webinars, game streaming, corporate streaming, vlogs or personalized content, and can be used in different industries ( Zhang et al. , 2023 ). Investigating the influence of live streaming on consumer engagement may enhance understanding of its relevance for the industry and improve marketing effectiveness ( Wongkitrungrueng and Assarut, 2020 ).

Blockchain technology allows tracing and enhances transaction transparency, creating authenticity certificates to prevent fraud or loyalty programs to build customers’ loyalty and trust ( Lemos et al. , 2022 ). Despite several studies being conducted to understand the impact of this technology on marketing ( Marthews and Tucker, 2023 ; Tan and Salo, 2023 ), there is much to be learned and questions unanswered.

4.3 RQ3: future research agenda

Based on the comprehensive bibliometric analysis findings, potential directions for future research are presented ( Table 6 ). Topics surrounding data-driven marketing are particularly relevant ( Zhang et al. , 2022 ) due to the data abundance and technological advances, and they have the potential to be further developed. For instance, issues arising from adopting AI to uncover hidden patterns in big data or integrating data from different sectors or industries to understand consumer behavior are yet to be understood. In addition, environmental sustainability is highly relevant due to the increasing customers’ awareness of the topic and its influence on developing marketing strategies ( Jung et al. , 2020 ). However, multiple questions are yet to be answered. In particular, the influence of gamification techniques to promote positive, environmentally sustainable consumer behavior and how emerging technologies influence the customers’ perception of sustainable products. Mass personalization allows consumers to customize product features ( Qin and Lu, 2021 ). This topic is highly relevant to the industry and underexplored in marketing. For instance, how can mass personalization be efficiently implemented in highly productive industries? Or how can emerging technologies improve mass personalization programs? Finally, the wearable technologies market is exponentially growing and is increasingly essential to consumer behavior ( Ferreira et al. , 2021 ).

5. Conclusions and limitations

Through the bibliometric analysis of the 100 most influential marketing papers published between 2018 and 2022, this review presents potential directions for knowledge advancement and comprehensive information to facilitate future literature search ( Boell and Cecez-Kecmanovic, 2014 ) by identifying the current research focus, conceptual structure and trends in the marketing field. In addition, this review contributes to practice by identifying the most influential articles for the marketing scientific community interested in gaining scientific insights. Meanwhile, the important role of emerging technologies and the shift of marketing toward a more data-driven approach will have significant practical implications for marketers.

This work has limitations that need to be stated. First, data were limited to Scopus database and restrained to indexed marketing journals. However, it is essential to note that all scientific databases have limitations. Second, to select the most influential marketing documents, the only criterion was on a commonly used metric – the number of citations. Although citation metrics are commonly used, they may incorrectly demonstrate the quality of the work. There are multiple reasons for a work to be cited ( Vogel and Güttel, 2012 ), such as a journal’s prestige or factors related to the methods ( Hota et al. , 2020 ). The Mathew effect phenomenon also exists in science ( García-Lillo et al. , 2017 ). Third, articles take time to be cited. This means that the most recent articles from our data set may have fewer citations, but it does not mean that their quality is poorer. Fourth, to select the most influential marketing articles, every journal under the subject area “Business, Management and Accounting” and category “Marketing” were selected. However, there are journals listed in other subject areas and categories. Nevertheless, the data set may still provide significant insight into the marketing field.

published research papers in marketing

Thematic map based on the authors’ keywords co-occurrence

Top 100 most cited articles structure

Source impact

Co-occurrence topics and future research avenues

IoT = Internet of things

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Wanick , V. and Stallwood , J. ( 2023 ), “ Brand storytelling, gamification and social media marketing in the ‘metaverse’: a case study of The Ralph Lauren winter escape ”, Reinventing Fashion Retailing , Springer International Publishing , Cham , pp. 35 - 54 .

Wongkitrungrueng , A. and Assarut , N. ( 2020 ), “ The role of live streaming in building consumer trust and engagement with social commerce sellers ”, Journal of Business Research , Vol. 117 , pp. 543 - 556 .

Zhang , T. , Moro , S. and Ramos , R.F. ( 2022 ), “ A data-driven approach to improve customer churn prediction based on telecom customer segmentation ”, Future Internet , Vol. 14 No. 3 , p. 94 .

Zhang , P. , Chao , C.-W. , Hasan , R. , Aljaroodi , N. , Tian , H.M. , F. and Fred , Chiong . ( 2023 ), “ Effects of in-store live stream on consumers’ offline purchase intention ”, Journal of Retailing and Consumer Services , Vol. 72 , p. 103262 .

Acknowledgements

Paulo Rita’s work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project – UIDB/04152/2020 – Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.

Since submission of this article, the following authors have updated their affiliations: Ricardo Ramos is at Technology and Management School of Oliveira do Hospital, Polytechnic Institute of Coimbra, Oliveira do Hospital, Portugal; ISTAR, Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal; Centre Bio R&D Unit, Association BLC3 – Tecnology and Innovation Campus, Oliveira do Hospital, Portugal; Paulo Rita is at NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Lisboa, Portugal; and Celeste Vong is at NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Lisboa, Portugal.

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The Impact of Social Media Marketing on Consumer Engagement in Sustainable Consumption: A Systematic Literature Review

Paweł bryła.

1 Department of International Marketing and Retailing, Faculty of International and Political Studies, University of Lodz, Narutowicza 59a, 90-131 Lodz, Poland

Shuvam Chatterjee

2 Doctoral School of Social Science, University of Lodz, Matejki 22/26, 90-297 Lodz, Poland

Beata Ciabiada-Bryła

3 Department of Preventive Medicine, Faculty of Health Sciences, Medical University of Lodz, Zeligowskiego 7/9, 90-752 Lodz, Poland

Associated Data

Not applicable.

Social media have progressed drastically in building successful consumer engagement both in brand building and sustainable consumption. This paper is a review of the articles concerning the influence of social media marketing on consumer engagement in sustainable consumption practices published over the last 8 years. We follow the PRISMA technique as a methodological approach. The review investigates 70 empirical research articles published between 2014 and 2022. A total of 70% of the reviewed articles were published during the last two years. The most influential theories in this field of study are relationship marketing and consumer engagement (16 articles), social exchange (10), and sustainable consumption (8). The most commonly used methods are quantitative (in as many as 61 of the 70 reviewed articles). A careful analysis of the reviewed articles suggests that the tools that are consistently contributing to sustainable consumption are influencer marketing along with creating meaningful content with the right balance of content design, quality, and creativity, as well as the use of emojis. Consumer involvement with a brand relationship quality is key to a sustainable lifestyle. Young individuals with an entrepreneurial vision and a high drive for increased social status demonstrate the highest social media engagement in sustainable consumption.

1. Introduction

In today’s world of business, engagement in any form appears to be the buzzword [ 1 ]. Consumers expect brands to connect with them more on an emotional level than just selling their products and services. This depicts a shift from a transactional marketing perspective to a more in-demand relationship focus approach [ 2 , 3 ]. Consumer engagement happens to receive major attention from marketers if they think of building a long-term relationship with their consumers, which will help them secure brand awareness [ 4 , 5 ] and loyalty toward their brands [ 6 ]. Marketing practitioners across the globe have realized the significant potential of investing time in the digital space considering a variety of social media platforms [ 7 , 8 ]. The same has been certified by the Marketing Science Institute [ 9 ], which has included consumer engagement as their top priority for the coming years in delivering top-notch customer value. Bhattacharjee [ 10 ] discussed how the digital space comprising social media tools would have projected estimated spending of more than USD 750 billion by 2025. Consumer engagement is a multifaceted approach comprising the cognitive, behavioral, and affective aspects of the brand–consumer relationship [ 11 , 12 ].

Past studies have suggested that it is consumer engagement that acts as the initiator for brands building a long-term relationship with their consumers [ 13 ]; often, on most occasions, under its presence, consumers tend to demonstrate a favorable attitude toward the brands as well [ 14 ]. Lim et al. [ 6 ] have suggested consumer engagement as an emerging topic, which has progressed rapidly over the past decade. Hence, it is important to have an overview of past studies to build up a future trajectory to enrich our understanding of the concepts of consumer engagement in social media marketing. Several reviews have appeared in the literature over the last few years. Some of them were focusing on the implications of the managerial perspective and building a connection through social media from a B2B standpoint [ 15 , 16 ], whereas other reviews focused on the various theories adopted in the literature [ 17 ]. Reviews from the domain perspective, such as hospitality and tourism [ 18 ], were also apparently visible. Haider et al. [ 19 ] discussed the importance of sustainable consumption from the micro, meso, and macro levels to practice a better quality of life by training consumers in thoughtful consumption [ 20 ] and providing them with better infrastructural instruments. A systematic review by Fischer et al. [ 21 ] guides us on building communication as an integral tool for practicing sustainable consumption.

Epstein [ 22 ] discussed sustainable consumption as a consumer’s long-term awareness of consequences to the natural or social environment, often expressed through words such as environmentally friendly or socially friendly consumption behavior [ 23 ]. Although this field has developed intensely in recent years, nevertheless, the implementation of sustainable consumption by consumer engagement through social media is still in its nascent state. Green thinkers are individuals with a more conscious approach and responsible intentions and decision making when it comes to environmental issues [ 24 ]. De Morais et al. [ 25 ] discussed how consumers with selfless concerns for others’ well-being and culture are shaping the motives for sustainable consumption through social media. Consumers engaged in deeper participation in social media are actively trying to promote green buying for sustainable consumption. Kong et al. [ 26 ] suggest that high-end brand advertisers on social media should be respectful of consumers’ cultural orientation in building sustainable consumption interaction. Xia et al. [ 27 ] suggested how sustainable resource management by encouraging environmental innovation could contribute to improved performance for sustainable corporations through social media networks. Finally, Zafar et al. [ 28 ] suggest how a personalized advertisements approach attracts consumers to a sustainable purchase decision in social media networks.

To understand the popularity of social media as an effective tool to build consumer engagement in the sustainable consumption environment, catching the diversity and depth of the current research in this genre, a more detailed systematic review combining the future untapped research directions along with the research questions to clarify those dimensions is of utmost need. This review tries to bridge this gap by discussing the themes that emerged along with the characteristics portrayed over the last eight years, thus paving the way for future research questions and research directions for social media marketing researchers involved in consumer engagement in the social media brand community.

This work is an illustrative overview of articles on the social media brand communities involving consumer engagement with a special focus on (1) the various research methodological approaches and variables identified over this span of eight years, (2) research theories supporting previous research, and (3) future research directions along with research questions to assist social media marketing scholars in conducting fruitful and relevant research in this field.

This review provides insights into several research questions:

1. What are the characteristics of the recent literature on social media consumer engagement in sustainable consumption in terms of theories, contexts, and methods?

2. How was consumer engagement in social media brand communities operationalized in research models (independent or dependent variable, control, or moderator)?

3. What further investigations could be conducted by scholars into consumer engagement and building sustainable consumption through social media?

The remainder of the paper is organized as follows. The following section presents the literature review methodology. Subsequent sections include a substantive analysis of the research studies included in this review and discussion, including limitations, future research directions, and a conclusion.

2. Materials and Methods

The approach followed for this study is a meta-textual review further allowing the identification and extraction of the pertinent information on subjects of relevance from published research and assessing the literature [ 29 ]. This approach has the following goals:

(i) To assess relevant and quality articles focused on consumer engagement with a direct intervention with social media marketing.

(ii) To formulate an integrative framework providing a holistic understanding of the impact of social media marketing on consumer engagement in sustainable consumption.

(iii) To identify the research gaps in the literature and provide future research directions.

A systematic literature review is evidence of the previous literature that accurately and reliably analyzes the quality of peer-reviewed journals following some preferred reporting items and consisting of a meta-analytical structure (PRISMA) [ 30 ]. PRISMA is a structured review protocol, which provides a four-phase flow diagram representing the sample identification for screening and then for eligibility testing and the final demonstration of the studies included in the review. The logic for choosing PRISMA lies in its comprehensiveness and its potential to provide more consistency across its reviews. To conduct this review, four steps were followed, namely, (1) establishing the inclusion–exclusion criteria for study selection, (2) identifying relevant quality studies, (3) evaluating the literature, and (4) reporting the findings.

The sample search strategy and identification involve three activities, namely, (a) searching appropriate keywords, (b) assessing the relevance, and (c) assessing the quality.

2.1. Assessing Appropriateness of the Search Keywords

The data search was executed using a prominent multidisciplinary database of peer-reviewed research literature, Web of Science. Li et al. [ 31 ] discussed the usability of this database gaining increasing popularity in scientific instruments across countries and knowledge domains.

The search strings were created by regrouping the chosen keywords into three specific categories. The first category covers terms representing consumer engagement, and the second category is formulated using the influence of social media marketing. Finally, the third category investigates the sustainable consumption category. The search strings are presented in Figure 1 .

An external file that holds a picture, illustration, etc.
Object name is ijerph-19-16637-g001.jpg

Identification of initial inclusion criteria for articles in this review. Note: * in the search string denotes that different word endings following this symbol are included in the search.

The keywords were mainly searched in the titles, abstracts, and/or keyword sections, and consequently, 7652 articles were identified from the search process. Considering the appropriateness of the journals and limiting the time according to the inclusion and exclusion criteria set, a total of 1684 articles were identified, as shown in Figure 1 .

2.2. Assessing Relevance

Initial sorting of the articles’ titles and abstracts led to the exclusion of articles that did not focus on consumer engagement and social media marketing in the context of sustainable consumption. We excluded papers on the grounds of trade publications, editorial handbooks, overlapping studies in close contexts of consumer engagement, and dissertations, to ensure further homogeneity.

The rationale for the sample considering research papers after 2014 is manifold. First, the study tries to acknowledge the recent trends of methodologies and to understand the recent shift in research methods and techniques. Moreover, the context of studies has significantly varied from understanding consumer engagement in brands to hospitality, to influencers and their social media activities involving consumer engagement. Hence, this present study would primarily focus on understanding these pattern shifts.

Finally, 265 articles were selected for deeper reading, allowing us to discard 88 working papers. After these steps, the resulting sample consisted of 177 articles.

2.3. Assessing Quality

Many a time, an article seems relevant, but it might lack quality. Hence, a consistent focus on peer-reviewed and high-quality journals was chosen along with journal ranking criteria based on the Association of Business Studies (ABS) Journal Quality Guide and only included top journals ranked as 4*, 4, and 3 to generate high-quality articles. This refinement process led to inclusion of 70 articles in this systematic review ( Figure 2 ). A study by Mingers and Yang [ 32 ] suggested from a sample of over four hundred research articles from the ABS journal ranking list that the standard mean impact factor was around 1.25. Hence, for this study, we accommodated articles with an IF of at least 1.5 and above. Rowlinson et al. [ 33 ] suggested that ABS-ranked articles (above 3) defined quality levels as internationally excellent in terms of originality and rigor, which set up the base for our study to have articles listed in ABS 3 and above.

An external file that holds a picture, illustration, etc.
Object name is ijerph-19-16637-g002.jpg

Flowchart of the study selection process regarding relevance and quality of the initially selected studies.

3. General Overview of Articles Included in This Review

3.1. publication trends.

The year-wise distribution of articles presented in Figure 3 witnesses a sharp rise in the number of articles on consumer engagement within the context of social media in the last two years (i.e., 2021 and 2022). This implies that consumer engagement is gaining popularity and witnessing a growth phase in terms of the number of articles published in the area. In the last two years, the number of published articles increased so much that more than 65% of the total studies were published in the last two years.

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Object name is ijerph-19-16637-g003.jpg

The number of articles included in this review by the year of publication.

3.2. Classification of Articles

To measure the progress of the impact of social media marketing on consumer engagement, we classified the empirical studies [ 16 ] into either qualitative (which bring out results where the primary data points are non-numeric) or quantitative or mixed methods. Out of the 70 articles studied, we figured out that only 7 studies adopted a qualitative path, while 2 studies adopted a mixed method approach. However, most of the articles were based on research through a quantitative approach (61 articles).

4. Meta-Textual Method

4.1. theories.

Consumer engagement in the context of social media has gained momentum across various theoretical contexts from various disciplines to showcase its effects. The study identifies 57 studies that have employed at least one theory. Here, we discuss the most applied theories in consumer engagement ( Table 1 ).

Theories used in studies on consumer engagement in social media in the context of sustainable consumption.

4.1.1. Relationship Marketing and Consumer Engagement

This section deals with the combined interrelated theories of relationship marketing and consumer engagement, which account for 25% of the total studies (16 studies). According to Pansari and Kumar [ 76 ], consumer engagement has shaped out to be one of the crucial elements in contemporary marketing, with its direct effects on relationship marketing. Further, they discuss that emotion and satisfaction are the main pillars of consumer engagement. Moreover, they conclude that engagements can only be nurtured if consumers tend to show belongingness toward the brand and form relationships in due process. Gómez et al. [ 34 ] suggest that consumer engagement is stronger with social media brand engagement than just brand communication. Ma et al. [ 12 ] suggest how strong brand engagement in the form of posts, tweets, and continuous interaction with consumers contributes to relationship building impacting the consumer’s behavioral, cognitive, and emotional engagement.

4.1.2. Social Exchange

The social exchange theory, which enlists psychology, sociology, and economics [ 77 ], has also been witnessed among consumer engagement theories (16%, 10 studies). The essence of this theory is to understand the focus for consumers to get involved through social media marketing [ 78 ]. According to Zhao and Chen [ 42 ], consumers tend to develop a more psychological bond when they are satisfied with the brand and its involvement in marketing. Consumers derive perceived benefits and satisfaction responses by engaging in social media activities [ 45 ]. A study by Kim and Baek [ 47 ] suggests the impact of influencers in engaging consumers and building a network of relationships.

4.1.3. Sustainable Consumption

The sustainability theory is highly visible in online consumer engagement through social media in the recent past, as consumers tend to become extremely aware of their purchases having an environmental impact. This pushes the demand for increased green sustainable brands [ 79 ]. Kong et al. [ 26 ] discussed how effective sustainable communication is in selling luxury products, keeping the cultural orientation of the consumers in mind at the same time. Nekmahmud et al. [ 53 ] suggested that online consumers’ need to engage with a positive attitude towards green products, which would have a strong association with sustainable consumption. Further, socio-environmental and socio-economic thoughts play a crucial role in building sustainable brand performance [ 27 ]. Zafar et al. [ 28 ] attempt to understand the importance of crafted personalized advertisements playing a significant role in consumers’ sustainable purchase intentions.

4.1.4. Uses and Gratification

Katz et al. [ 80 ] discussed the uses and gratification theory to understand how communication occurs through mass media. This theory concentrates on understanding users’ selection of media based on their goals to cater to specific needs. With the invention of social media, this theory now focuses on understanding the user’s choice and use of the internet. The theory has been consistently used in consumer engagement behavior across social media (seven studies, 10%). The studies talk about consumers’ engagement in participation in social media, including the cognitive and social benefits along with personal achievements in having pleasurable experiences derived from social media interactions. Bailey et al. [ 59 ] discuss consumer socialization motivation and participation in social media engagement, which would yield results in achieving brand and marketing goals [ 60 ].

4.1.5. Other Theories

In addition to the theories discussed, the study also explains specific behavior, which is categorized in “other theories”, which account for 25% of the studies (16 studies). For example, Liu et al. [ 75 ] discuss the trust transfer theory, where consumer engagement plays a significant role in brand trust. Additionally, Lourenço et al. [ 74 ] introduced the expectancy theory, which underlines the consumer engagement dimension operational scales for measuring the level of consumer engagement.

4.2. Context

This section discusses the countries involved in the analyzed sample. The findings indicate that Europe is the biggest contributor to this study, with 31 of the 70 studies (44%). This reflects that the European countries, mainly the UK, France, Austria, Belgium, and the Netherlands, dominate the papers related to consumer engagement in sustainable consumption within the social media marketing context. Asia, surprisingly, is the second biggest contributor to this research stream (25 studies, 35% of the total empirical papers studied), followed by the USA—19 studies (27%)—and other countries. It is noted that, unlike social science, the contexts are predominantly set in the more emerging Asian market. The feasible logic is the extensive economic and technological advancements witnessed in the Asian market in the last eight years. The study also finds that there will be ample scope for future researchers in the context of consumer engagement through social media in the South American market. Emerging markets, such as Brazil, did not showcase enough contribution in this domain of study. Hence, future research should focus on these markets. Additionally, the researchers would advise future researchers in this field to focus on cross-country consumer engagements in social media; culture would play a significant role in such studies ( Table 2 ).

Articles included in the review by country.

4.3. Methods

In this section, we will discuss the articles reviewed through the prism of the research approaches and analytical techniques adopted to assess the relationships investigated in consumer engagement research. Table 3 and Table 4 demonstrate the data collection techniques and analysis techniques used in consumer engagement, respectively. Surveys are the most used quantitative method. Other methods encountered are content analysis and latent profile analysis. Concerning data analysis techniques, structural equation modeling (SEM) is the most used research tool, accounting for 42% of the total quantitative studies, followed by confirmatory factor analysis (CFA), accounting for 37% of the studies. However, it is to be noted that 10% of the studies in consumer engagement have adopted a combination of qualitative methods, such as in-depth interviews, observational research, netnography, and Google Vision AI. We observed the emergence of netnography as a methodological tool, which is a refined version of ethnographic research occurring in social media communities [ 68 ]. Most of the studies initiated conducted surveys through online and social media platforms.

Methodologies adopted in consumer engagement research.

Data analysis techniques adopted in the reviewed articles.

We believe that consumer engagement studies would benefit immensely from conducting more longitudinal studies testing relationships over a period of time. Additionally, there is a lack of studies picking up the experimental method approach to understand a more trusted consumer engagement on social media platforms. Our research validates a greater scope of a mixed-method approach for conducting studies in consumer engagement.

5. Variables Used in the Reviewed Research Studies

This section reviews the various independent, moderating, control, and dependent variables in consumer engagement studies influenced by social media marketing and their associated relationships that were tested to unfold certain phenomena concerning these variables (see Table 5 ).

Variables investigated in social media consumer engagement research in the context of sustainable consumption.

5.1. Independent Variables

The independent variables include cognitive and affective states (8 articles, 12% of the study), their relationship with brands, and consumer engagement (14 articles, 22% of studies) in the tune of social media marketing. The various mental states as demonstrated by consumers include perceived benefits from the brand and behavioral outcomes in building value co-creation and research integration. Consumer-related variables contribute to the understanding of engagement through interaction, advocacy, and connecting with the brand and trust. Social-media-related variables try to test the strength of attachment, having faith in social media channels, and the various follow-up techniques effectively used (tweet reposts, likes, comments) to build consumer engagement. Finally, brand-related variables try to focus on building consumer appeal and brand engagement activities, developing persuasiveness and brand trust, and enriching the brand’s global identity.

5.2. Dependent Variables

Our investigation of the dependent variables reveals that most of the studies focus on intentional or behavioral consumer engagement and relationship-based outcomes as well. The intentional and behavioral outcome validates consumers’ word of mouth, feedback, and recommendations along with participation in community engagements. The focus also lies in analyzing social media and brand marketers’ posts from an emotional perspective. It also judges the purchase intention of consumers. The relationship-based outcomes deal with the various engagement activities consumers and brands perform, such as frequent likes and comments of the posts along with sharing them on social media networks. There are then consumer-related variables focusing on the attitude and purchase intentions of consumers, with an overall brand experience, which finally leads to purchase decisions for consumers as well. Finally, consumer-related variables leading to sustainable consumption contribute to green buying intentions, a clear psychological state of well-being, being thought of as an environmental activist, and making a sustainable purchase decision.

5.3. Control Variables

Our research on control variables comprises mainly brand or marketer-related and consumer-related control variables. The consumer-related variables discuss the demographic origin of the consumer along with analyzing his/her activities on the networking sites. Additionally, visual perceptions, the timing of posts, and brand familiarity with social networking sites play a crucial role. The brand/marketer-related variables focus on building a buzz about their products and services, thereby maintaining the brand community engagement along with building favorable brand attachments.

5.4. Moderating Variables

Finally, the moderating variables consist of consumer-related and brand-related variables. The consumer-related variables come from culture playing a significant role in consumers’ engagement with the brand over social media, while the brand-related variables include the topic and modality of the posts in social media networks.

6. Discussion

6.1. limitations.

One of the limitations is related to the extremely fast changing social media landscape. Every high-quality indexed journal approves a research paper after considerable time spent by the reviewers understanding the paper’s quality, rigor, and contribution to the research community. Hence, during that review time, further developments can occur in the field of consumer engagement under social media influence, thereby creating a gap where the present researchers fail to accommodate the most recent articles. Second, this review followed strict guidelines to ensure a stringent process of selecting journal articles [ 86 , 87 ]. Hence, because of narrowing down the search criteria to accommodate articles complying with consumer engagement in a social media context, the review might have missed overlapping or close concepts in the literature, such as consumer engagement in a B2B context [ 16 ] or interactions in social CRM [ 88 ]. Third, social media, if not used effectively by organization salespersons, can often become a tool for exploitation, thereby resulting in consumers interpreting information and communication messages inappropriately. Fourth, “technostress”, as studied by Tarafdar et al. [ 89 ], could lead to stress due to spending excessive screen time on social networking sites (Facebook, Twitter, and Instagram) and further contribute to improper time management skills by both firm employees and consumers.

6.2. Future Research Directions

A direction for future research is an important lookout for systematic reviews [ 86 ]. Based on our review of the findings of research conducted on consumer engagement in social media networks, we noted multiple channels where we would highly encourage future research to occur. Researchers have made noteworthy progress in understanding the role of social media networks in communicating information in the business market. However, Maier et al. [ 90 ] discussed that salespersons loaded with excessive information may experience a feeling of discomfort. Hence, future research should follow the direction of understanding the optimum information chain, which would not create fatigue for the message recipients.

Second, it would be interesting to find out to what extent cross-country cultural differences influence the functioning of consumer engagement in social media networks with the use of the Hofstede [ 91 ] model and redefining the marketing strategies ensuring societal well-being by executing mindful consumption [ 19 ].

Third, it will be interesting to understand the consumer sentiment toward social media networks in building engagement in green consumption. Researchers could deploy analytical methods to forecast future consumer engagement in social media networks, measuring constructs such as the strength of the attachment and the total revenues generated for the firm through liking and sharing of tweets. Finally, future research could be carried out to understand how consumer engagement in sustainable consumption could be stimulated in social media networks in developing countries, such as Brazil, Indonesia, and India. This is because countries such as India and Brazil are dominating social networking sites [ 92 ], and hence, diversified fresh research looking for a varied research focus is much needed to understand the growth of social-media-based consumer engagement strategies.

7. Conclusions

This study constitutes an attempt to assess the state of the art in the hyper-dynamic field of social media consumer engagement in sustainable consumption. We analyzed research articles that examined the role of social media networks in engaging consumers to become attracted to a sustainable brand or product. We believe that this review will enable the scholarly community to initiate and conduct relevant research in this vital emerging research area.

According to our results, the investigated research area is gaining a rapidly increasing interest in the scientific community, as evidenced by the number of studies published. A total of 49 articles included in this review were published during the last two years. Twelve appeared in 2020 and only nine in the period of 2014–2019.

Most of the reviewed studies have been published in Europe (44%), followed by Asia (35%), and the USA (27%).

The most influential theories in this field of study are relationship marketing and consumer engagement (16 articles), social exchange (10), and sustainable consumption (8).

The most commonly used methods are quantitative (in as many as 61 of the 70 reviewed articles). The prevalent data analysis techniques are SEM (28 studies), CFA/EFA (23), and various regression models (16).

A careful analysis of the reviewed articles suggests that the tools that are consistently contributing to sustainable consumption are influencer marketing [ 73 ] along with creating meaningful content with the right balance of content design, quality, and creativity. Moreover, the meaningful use of emojis [ 69 ] is gaining immense popularity among social media practitioners for building sustainable marketing consumption through a rapid increase in likes and comments in the posts [ 44 ] and an array of text characteristics with emojis [ 64 ].

The review led to the conclusion that consistent and disciplined consumer involvement [ 49 ] with a steady brand relationship quality [ 34 ] is key to a sustainable lifestyle and behavior contributing to sustainable consumption.

This systematic review is a work that draws attention to the consumer segments, which are prone to adopting new technologies [ 48 ]. Young individuals [ 51 ] with an entrepreneurial vision [ 54 ] and a high drive for increased social status [ 25 ] are seen as actively involved in social media engagement in sustainable consumption. One very important observation that came out from this review is that consumers’ attitudes and purchase intentions toward social-media-based brand marketing activities depend largely on the consumers’ generation, and hence, all activities need to be fine-tuned respecting and understanding the age profile of the target audience [ 82 ].

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, P.B. and B.C.-B.; methodology, S.C.; formal analysis, S.C.; writing—original draft preparation, S.C.; writing—review and editing, P.B. and B.C.-B.; visualization, P.B. and S.C.; supervision, P.B. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Journal of Marketing Call for Papers: Marketing Impact with Research-Driven Apps

Journal of Marketing Call for Papers: Marketing Impact with Research-Driven Apps

published research papers in marketing

Special Issue: Journal of Marketing

Special Issue Editors: Pradeep Chintagunta (University of Chicago), Rajdeep Grewal (University of North Carolina–Chapel Hill), Detelina Marinova (University of Missouri), Rik Pieters (Tilburg University), and Shrihari Sridhar (Texas A&M University)

Jump to: Motivation | Research-Driven Apps | Submission Requirements | Special Issue Timeline | FAQs | App Tutorial Videos

Marketing scholarship has the potential to benefit society, advance managerial practice, improve consumers’ lives, and contribute to fundamental scientific knowledge.

However, given the typical form and function of academic publications in the discipline, the target audience of marketing academic research (consumers, investors, firms, frontline/middle/senior management, policy makers, as well as other marketing scholars and students) face barriers to understand and adopt research findings. These barriers limit the impact of marketing academic research.

The Journal of Marketing ’s vision is to encourage a wide range of approaches that can reduce impact barriers of academic articles and consequently catalyze the message among the target audience to whom marketing scholarship should matter.

This special issue of Journal of Marketing , titled “Marketing Impact with Research-Driven Apps,” emphasizes the integration of research-driven apps into academic articles to enhance understanding, consumption, adoption, and ongoing usage of research findings.

Pradeep Chintagunta, Rajdeep Grewal, Detelina Marinova, Rik Pieters, and Shrihari Sridhar will co-edit the special issue. The editors will not submit manuscripts to the special issue.

Research-Driven Apps

A research-driven app is an online interactive tool that provides a deeper understanding of the usability of the research contribution . It serves as a dynamic computational supplement to a research manuscript, thereby adding form and function to the otherwise static nature of a research publication.

Rather than simply adding an app to the end of an otherwise traditional research manuscript (problem, idea, intended contribution, theory, data, findings, conclusions, discussion, recommendations, and future research), submissions should think of their online interactive tool’s intended usability and implementation as the focal point of research. In this way, developing apps for marketing academic research may stimulate a solution-based mindset among marketing scholars that is reflected in their research output.

An app’s goal depends intricately on the scope and goals of the original research manuscript. The special issue encourages apps with a substantive focus covering a wide variety of approaches, paradigms, questions, and topics. While not exhaustive, the following list presents some types of apps well-suited to marketing academic research:

  • Disaggregating the strength of the behavioral effects under various conditions (e.g., three-way interactions) and depicting their underlying process;
  • Simulating game theory–based results (e.g., equilibrium outcomes) assuming various hypothetical primitives; and
  • Offering what-if predictions to various types of marketing interventions (e.g., targeted advertising) under well-defined market scenarios.
  • Recommending a range of marketing mix decision criteria that maximize or minimize firms’ marketing outcomes;
  • Offering customized plans, schedules, or interventions that help customers mitigate or enhance their life outcomes; and
  • Simulating marketing interventions that maximize or minimize societal outcomes.
  • Providing empirical results to a substantive problem across a range of subsamples and estimation methodologies;
  • Automating critical synthesis of extant literature across several subtopics; and
  • Providing interactive access to a large set of variables that accompany (but are not focal to) the specific research question studied in the manuscript.
  • Scoring unstructured data (e.g., text, video) to uncover novel marketing insights (e.g., authenticity, complexity); and
  • Modifying sample unstructured marketing input (e.g., sales pitches) into more effective unstructured material based on uncovering underlying characteristics.

Special Issue Submission Requirements

We encourage submissions similar to traditional marketing academic manuscripts submitted to Journal of Marketing that focus on novel, important, and substantive marketing topics. In addition, submissions to the special issue should include a new section titled “App Implementation.” In this section, the author(s) should:

  • Describe the problem solved by the app and how it supplements the research contribution of the manuscript.
  • Define the audience or the end users targeted by the app and the usefulness of the app to this audience over and above the current situation or available (software or other) solutions. The audience can be broad and include managers, executives, researchers, consumers, policy makers, government, media, general public, other marketing academics, and students.
  • Provide a secure, anonymous relatively permanent link to the app, with appropriate instructions on how to use the app and interpret the results.
  • Share open-source access to the app to allow accelerated dissemination upon publication. [1]

Ideally, the app implementation informs the problem statement and intended contribution of the research and manuscript.

The evaluation of manuscripts submitted for the special issue will be guided by the following:

  • The quality and veracity of the manuscript’s original contribution, like all manuscripts submitted to the Journal of Marketing . Thus, the app cannot substitute for the incremental contribution and validity of the original contribution.
  • The contribution of the app relative to alternative apps or commercial software solutions. For example, an existing implementation in software such as R, Python, or Matlab, would reduce the contribution of the app.
  • The usefulness and depth of insight enabled by the app itself. Authors are welcome to provide evidence for and/or demonstration of the application and effectiveness of the app in the field.

[1] AMA is working on obtaining official clearance that papers published in the special issue will (1) maintain status quo regarding copyright with the journal article, (2) allow the authors to retain ownership over their individual app’s intellectual property, and (3) ensure AMA has clear rights to house/distribute the app at no cost on behalf of the authors.

Special Issue Timeline

The timeline of the special issue is as follows:

Frequently Asked Questions

A research-driven app is an online interactive tool that provides a deeper understanding of the usability of the research contribution. It serves as a dynamic computational supplement to a research manuscript, thereby adding form and function to the otherwise static nature of a research publication.

Warren Nooshin L., Matthew Farmer, Tianyu Gu, and Caleb Warren (2021), “ Marketing Ideas: How to Write Research Articles that Readers Understand and Cite ,” Journal of Marketing , 85(5), 42–57. The goal of the article is to supplement the manuscript’s overarching goal to recognize and repair unclear writing to authors write more impactful articles.

Accordingly, the research-driven app accompanying the paper ( http://writingclaritycalculator.com/ ) uses the underlying method proposed in the paper to analyze input text and output scores pertaining to the concreteness of writing, number of examples, the percentage of sentences that use active voice etc.

Thus, the writing clarity calculator serves as a dynamic computational supplement to a research manuscript, thereby adding form and function to the otherwise static nature of the research publication.

We believe there could be different types of apps based on their purpose as well the research question.

  • Predictors offer model-based predictions and thereby depict the interplay among various factors affecting a marketing input, process, or outcome.
  • Optimizers offer normative solutions to improve currently suboptimal marketing decisions made by agents, and recommenders offer superior solutions to current problems marketing stakeholders face.
  • Explorers investigate sensitivity of research results to various research design assumptions.
  • Converters provide accessible marketing insights through the conversion of unstructured input (e.g., text, audio, videos) to structured data, and models developed in the research framework.

In the call for papers, we provide a non-exhaustive list of types of apps well-suited to marketing academic research.

That’s right. The special issue emphasizes the integration of research-driven apps into academic articles to enhance understanding, consumption, adoption, and ongoing usage of research findings. In other words, you could pursue research on any substantive marketing problem .

Papers submitted to the Special Issue will be identical in form to regular issues but for one additional requirement. Submissions to the special issue should include a new section titled “App Implementation.” In this section, the author(s) should:

  • Share open-source access to the app to allow accelerated dissemination upon publication.
  • Confirm during the submission process that any hyperlinks in the paper (especially to sites such as OSF, AsPredicted) lead to anonymized material that does not reveal the identity of the authors.

The short answer is no. We are only interested in a working app that serves as a companion to the paper. Our goal is to encourage submissions where the online interactive tool’s intended usability and implementation is intricately intertwined with the focal research goal. In this way, developing apps for marketing academic research may stimulate a solution-based mindset among marketing scholars that is reflected in their research output.

The American Marketing Association is working on obtaining official clearance that papers published in the special issue will (1) maintain status quo regarding copyright with the journal article but (2) allow the authors to retain ownership over their individual app’s intellectual property.

No, JM does not require you to provide your source code to the app. However, (1) all articles published in JM will require compliance to the JM Policy for Research Transparency to ensure correctness, and (2) if you would like to do so, JM will facilitate the distribution through the Special Issue website.

During the review process, the authors are asked to provide a secure, anonymous, relatively permanent link to the app, with appropriate instructions on how to use it and interpret the results. Upon acceptance, AMA will house/distribute the app for three years at no cost on behalf of the authors.

We are currently working with the AMA on this issue and hope to at least secure an extended window of open access so that researchers and practitioners from around the world can access the article for free.

We encourage users of the app (e.g., practitioners) to engage with/consult the authors before implementing the app for commercial purposes. This will not only help companies engage directly with the inventors of the app but also help users clarify the necessary assumptions, bounds, and contingencies associated with the deployment of the app in their setting.

No. The evaluation of manuscripts submitted for the special issue will firstly be guided by the quality and veracity of the manuscript’s original contribution, like all manuscripts submitted to the Journal of Marketing . Thus, the app cannot substitute for the incremental contribution and validity of the original contribution.

It is probably not sufficient if the app is mostly an accessible database-and-mini-analysis tool. It then replaces standard SPSS, Stata, or similar statistical packages without adding sufficient novelty. 

Probably not. The app needs to be associated with an incremental contribution and hence have sufficient novelty over and above existing knowledge in marketing.

Absolutely. Journal of Marketing will host an online workshop in November 2022 that will feature demonstrations by experts to help participants get started on their journey to build apps for their own research. Since we are entering a new era of research driven by apps, we believe we are all part of this continuous, exciting, and vibrant new learning opportunity!

The manuscript submission window opens on 4/1/2023. During the manuscript submission process, authors will be required to submit the following: (1) a brief description of how the paper enhances the app (200 words) and (2) a brief description of how the app enhances the paper (200 words). The manuscript submission window closes on 9/30/2023. We expect the special issue in print and published manuscripts and promotion of apps to occur in Spring 2025.

App Tutorial Videos

To facilitate the creation of research-driven apps, JM is pleased to offer two app tutorial videos recorded by ERB members :

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  • Published: 19 February 2024

Genomic data in the All of Us Research Program

The all of us research program genomics investigators.

Nature ( 2024 ) Cite this article

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  • Genetic variation
  • Genome-wide association studies

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics 1 , 2 , 3 , 4 . The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health 5 , 6 . Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Comprehensively identifying genetic variation and cataloguing its contribution to health and disease, in conjunction with environmental and lifestyle factors, is a central goal of human health research 1 , 2 . A key limitation in efforts to build this catalogue has been the historic under-representation of large subsets of individuals in biomedical research including individuals from diverse ancestries, individuals with disabilities and individuals from disadvantaged backgrounds 3 , 4 . The All of Us Research Program (All of Us) aims to address this gap by enrolling and collecting comprehensive health data on at least one million individuals who reflect the diversity across the USA 5 , 6 . An essential component of All of Us is the generation of whole-genome sequence (WGS) and genotyping data on one million participants. All of Us is committed to making this dataset broadly useful—not only by democratizing access to this dataset across the scientific community but also to return value to the participants themselves by returning individual DNA results, such as genetic ancestry, hereditary disease risk and pharmacogenetics according to clinical standards, to those who wish to receive these research results.

Here we describe the release of WGS data from 245,388 All of Us participants and demonstrate the impact of this high-quality data in genetic and health studies. We carried out a series of data harmonization and quality control (QC) procedures and conducted analyses characterizing the properties of the dataset including genetic ancestry and relatedness. We validated the data by replicating well-established genotype–phenotype associations including low-density lipoprotein cholesterol (LDL-C) and 117 additional diseases. These data are available through the All of Us Researcher Workbench, a cloud platform that embodies and enables programme priorities, facilitating equitable data and compute access while ensuring responsible conduct of research and protecting participant privacy through a passport data access model.

The All of Us Research Program

To accelerate health research, All of Us is committed to curating and releasing research data early and often 6 . Less than five years after national enrolment began in 2018, this fifth data release includes data from more than 413,000 All of Us participants. Summary data are made available through a public Data Browser, and individual-level participant data are made available to researchers through the Researcher Workbench (Fig. 1a and Data availability).

figure 1

a , The All of Us Research Hub contains a publicly accessible Data Browser for exploration of summary phenotypic and genomic data. The Researcher Workbench is a secure cloud-based environment of participant-level data in a Controlled Tier that is widely accessible to researchers. b , All of Us participants have rich phenotype data from a combination of physical measurements, survey responses, EHRs, wearables and genomic data. Dots indicate the presence of the specific data type for the given number of participants. c , Overall summary of participants under-represented in biomedical research (UBR) with data available in the Controlled Tier. The All of Us logo in a is reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Participant data include a rich combination of phenotypic and genomic data (Fig. 1b ). Participants are asked to complete consent for research use of data, sharing of electronic health records (EHRs), donation of biospecimens (blood or saliva, and urine), in-person provision of physical measurements (height, weight and blood pressure) and surveys initially covering demographics, lifestyle and overall health 7 . Participants are also consented for recontact. EHR data, harmonized using the Observational Medical Outcomes Partnership Common Data Model 8 ( Methods ), are available for more than 287,000 participants (69.42%) from more than 50 health care provider organizations. The EHR dataset is longitudinal, with a quarter of participants having 10 years of EHR data (Extended Data Fig. 1 ). Data include 245,388 WGSs and genome-wide genotyping on 312,925 participants. Sequenced and genotyped individuals in this data release were not prioritized on the basis of any clinical or phenotypic feature. Notably, 99% of participants with WGS data also have survey data and physical measurements, and 84% also have EHR data. In this data release, 77% of individuals with genomic data identify with groups historically under-represented in biomedical research, including 46% who self-identify with a racial or ethnic minority group (Fig. 1c , Supplementary Table 1 and Supplementary Note ).

Scaling the All of Us infrastructure

The genomic dataset generated from All of Us participants is a resource for research and discovery and serves as the basis for return of individual health-related DNA results to participants. Consequently, the US Food and Drug Administration determined that All of Us met the criteria for a significant risk device study. As such, the entire All of Us genomics effort from sample acquisition to sequencing meets clinical laboratory standards 9 .

All of Us participants were recruited through a national network of partners, starting in 2018, as previously described 5 . Participants may enrol through All of Us - funded health care provider organizations or direct volunteer pathways and all biospecimens, including blood and saliva, are sent to the central All of Us Biobank for processing and storage. Genomics data for this release were generated from blood-derived DNA. The programme began return of actionable genomic results in December 2022. As of April 2023, approximately 51,000 individuals were sent notifications asking whether they wanted to view their results, and approximately half have accepted. Return continues on an ongoing basis.

The All of Us Data and Research Center maintains all participant information and biospecimen ID linkage to ensure that participant confidentiality and coded identifiers (participant and aliquot level) are used to track each sample through the All of Us genomics workflow. This workflow facilitates weekly automated aliquot and plating requests to the Biobank, supplies relevant metadata for the sample shipments to the Genome Centers, and contains a feedback loop to inform action on samples that fail QC at any stage. Further, the consent status of each participant is checked before sample shipment to confirm that they are still active. Although all participants with genomic data are consented for the same general research use category, the programme accommodates different preferences for the return of genomic data to participants and only data for those individuals who have consented for return of individual health-related DNA results are distributed to the All of Us Clinical Validation Labs for further evaluation and health-related clinical reporting. All participants in All of Us that choose to get health-related DNA results have the option to schedule a genetic counselling appointment to discuss their results. Individuals with positive findings who choose to obtain results are required to schedule an appointment with a genetic counsellor to receive those findings.

Genome sequencing

To satisfy the requirements for clinical accuracy, precision and consistency across DNA sample extraction and sequencing, the All of Us Genome Centers and Biobank harmonized laboratory protocols, established standard QC methodologies and metrics, and conducted a series of validation experiments using previously characterized clinical samples and commercially available reference standards 9 . Briefly, PCR-free barcoded WGS libraries were constructed with the Illumina Kapa HyperPrep kit. Libraries were pooled and sequenced on the Illumina NovaSeq 6000 instrument. After demultiplexing, initial QC analysis is performed with the Illumina DRAGEN pipeline (Supplementary Table 2 ) leveraging lane, library, flow cell, barcode and sample level metrics as well as assessing contamination, mapping quality and concordance to genotyping array data independently processed from a different aliquot of DNA. The Genome Centers use these metrics to determine whether each sample meets programme specifications and then submits sequencing data to the Data and Research Center for further QC, joint calling and distribution to the research community ( Methods ).

This effort to harmonize sequencing methods, multi-level QC and use of identical data processing protocols mitigated the variability in sequencing location and protocols that often leads to batch effects in large genomic datasets 9 . As a result, the data are not only of clinical-grade quality, but also consistent in coverage (≥30× mean) and uniformity across Genome Centers (Supplementary Figs. 1 – 5 ).

Joint calling and variant discovery

We carried out joint calling across the entire All of Us WGS dataset (Extended Data Fig. 2 ). Joint calling leverages information across samples to prune artefact variants, which increases sensitivity, and enables flagging samples with potential issues that were missed during single-sample QC 10 (Supplementary Table 3 ). Scaling conventional approaches to whole-genome joint calling beyond 50,000 individuals is a notable computational challenge 11 , 12 . To address this, we developed a new cloud variant storage solution, the Genomic Variant Store (GVS), which is based on a schema designed for querying and rendering variants in which the variants are stored in GVS and rendered to an analysable variant file, as opposed to the variant file being the primary storage mechanism (Code availability). We carried out QC on the joint call set on the basis of the approach developed for gnomAD 3.1 (ref.  13 ). This included flagging samples with outlying values in eight metrics (Supplementary Table 4 , Supplementary Fig. 2 and Methods ).

To calculate the sensitivity and precision of the joint call dataset, we included four well-characterized samples. We sequenced the National Institute of Standards and Technology reference materials (DNA samples) from the Genome in a Bottle consortium 13 and carried out variant calling as described above. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations 14 . The overall sensitivity for single-nucleotide variants was over 98.7% and precision was more than 99.9%. For short insertions or deletions, the sensitivity was over 97% and precision was more than 99.6% (Supplementary Table 5 and Methods ).

The joint call set included more than 1 billion genetic variants. We annotated the joint call dataset on the basis of functional annotation (for example, gene symbol and protein change) using Illumina Nirvana 15 . We defined coding variants as those inducing an amino acid change on a canonical ENSEMBL transcript and found 272,051,104 non-coding and 3,913,722 coding variants that have not been described previously in dbSNP 16 v153 (Extended Data Table 1 ). A total of 3,912,832 (99.98%) of the coding variants are rare (allelic frequency < 0.01) and the remaining 883 (0.02%) are common (allelic frequency > 0.01). Of the coding variants, 454 (0.01%) are common in one or more of the non-European computed ancestries in All of Us, rare among participants of European ancestry, and have an allelic number greater than 1,000 (Extended Data Table 2 and Extended Data Fig. 3 ). The distributions of pathogenic, or likely pathogenic, ClinVar variant counts per participant, stratified by computed ancestry, filtered to only those variants that are found in individuals with an allele count of <40 are shown in Extended Data Fig. 4 . The potential medical implications of these known and new variants with respect to variant pathogenicity by ancestry are highlighted in a companion paper 17 . In particular, we find that the European ancestry subset has the highest rate of pathogenic variation (2.1%), which was twice the rate of pathogenic variation in individuals of East Asian ancestry 17 .The lower frequency of variants in East Asian individuals may be partially explained by the fact the sample size in that group is small and there may be knowledge bias in the variant databases that is reducing the number of findings in some of the less-studied ancestry groups.

Genetic ancestry and relatedness

Genetic ancestry inference confirmed that 51.1% of the All of Us WGS dataset is derived from individuals of non-European ancestry. Briefly, the ancestry categories are based on the same labels used in gnomAD 18 . We trained a classifier on a 16-dimensional principal component analysis (PCA) space of a diverse reference based on 3,202 samples and 151,159 autosomal single-nucleotide polymorphisms. We projected the All of Us samples into the PCA space of the training data, based on the same single-nucleotide polymorphisms from the WGS data, and generated categorical ancestry predictions from the trained classifier ( Methods ). Continuous genetic ancestry fractions for All of Us samples were inferred using the same PCA data, and participants’ patterns of ancestry and admixture were compared to their self-identified race and ethnicity (Fig. 2 and Methods ). Continuous ancestry inference carried out using genome-wide genotypes yields highly concordant estimates.

figure 2

a , b , Uniform manifold approximation and projection (UMAP) representations of All of Us WGS PCA data with self-described race ( a ) and ethnicity ( b ) labels. c , Proportion of genetic ancestry per individual in six distinct and coherent ancestry groups defined by Human Genome Diversity Project and 1000 Genomes samples.

Kinship estimation confirmed that All of Us WGS data consist largely of unrelated individuals with about 85% (215,107) having no first- or second-degree relatives in the dataset (Supplementary Fig. 6 ). As many genomic analyses leverage unrelated individuals, we identified the smallest set of samples that are required to be removed from the remaining individuals that had first- or second-degree relatives and retained one individual from each kindred. This procedure yielded a maximal independent set of 231,442 individuals (about 94%) with genome sequence data in the current release ( Methods ).

Genetic determinants of LDL-C

As a measure of data quality and utility, we carried out a single-variant genome-wide association study (GWAS) for LDL-C, a trait with well-established genomic architecture ( Methods ). Of the 245,388 WGS participants, 91,749 had one or more LDL-C measurements. The All of Us LDL-C GWAS identified 20 well-established genome-wide significant loci, with minimal genomic inflation (Fig. 3 , Extended Data Table 3 and Supplementary Fig. 7 ). We compared the results to those of a recent multi-ethnic LDL-C GWAS in the National Heart, Lung, and Blood Institute (NHLBI) TOPMed study that included 66,329 ancestrally diverse (56% non-European ancestry) individuals 19 . We found a strong correlation between the effect estimates for NHLBI TOPMed genome-wide significant loci and those of All of Us ( R 2  = 0.98, P  < 1.61 × 10 −45 ; Fig. 3 , inset). Notably, the per-locus effect sizes observed in All of Us are decreased compared to those in TOPMed, which is in part due to differences in the underlying statistical model, differences in the ancestral composition of these datasets and differences in laboratory value ascertainment between EHR-derived data and epidemiology studies. A companion manuscript extended this work to identify common and rare genetic associations for three diseases (atrial fibrillation, coronary artery disease and type 2 diabetes) and two quantitative traits (height and LDL-C) in the All of Us dataset and identified very high concordance with previous efforts across all of these diseases and traits 20 .

figure 3

Manhattan plot demonstrating robust replication of 20 well-established LDL-C genetic loci among 91,749 individuals with 1 or more LDL-C measurements. The red horizontal line denotes the genome wide significance threshold of P = 5 × 10 –8 . Inset, effect estimate ( β ) comparison between NHLBI TOPMed LDL-C GWAS ( x  axis) and All of Us LDL-C GWAS ( y  axis) for the subset of 194 independent variants clumped (window 250 kb, r2 0.5) that reached genome-wide significance in NHLBI TOPMed.

Genotype-by-phenotype associations

As another measure of data quality and utility, we tested replication rates of previously reported phenotype–genotype associations in the five predicted genetic ancestry populations present in the Phenotype/Genotype Reference Map (PGRM): AFR, African ancestry; AMR, Latino/admixed American ancestry; EAS, East Asian ancestry; EUR, European ancestry; SAS, South Asian ancestry. The PGRM contains published associations in the GWAS catalogue in these ancestry populations that map to International Classification of Diseases-based phenotype codes 21 . This replication study specifically looked across 4,947 variants, calculating replication rates for powered associations in each ancestry population. The overall replication rates for associations powered at 80% were: 72.0% (18/25) in AFR, 100% (13/13) in AMR, 46.6% (7/15) in EAS, 74.9% (1,064/1,421) in EUR, and 100% (1/1) in SAS. With the exception of the EAS ancestry results, these powered replication rates are comparable to those of the published PGRM analysis where the replication rates of several single-site EHR-linked biobanks ranges from 76% to 85%. These results demonstrate the utility of the data and also highlight opportunities for further work understanding the specifics of the All of Us population and the potential contribution of gene–environment interactions to genotype–phenotype mapping and motivates the development of methods for multi-site EHR phenotype data extraction, harmonization and genetic association studies.

More broadly, the All of Us resource highlights the opportunities to identify genotype–phenotype associations that differ across diverse populations 22 . For example, the Duffy blood group locus ( ACKR1 ) is more prevalent in individuals of AFR ancestry and individuals of AMR ancestry than in individuals of EUR ancestry. Although the phenome-wide association study of this locus highlights the well-established association of the Duffy blood group with lower white blood cell counts both in individuals of AFR and AMR ancestry 23 , 24 , it also revealed genetic-ancestry-specific phenotype patterns, with minimal phenotypic associations in individuals of EAS ancestry and individuals of EUR ancestry (Fig. 4 and Extended Data Table 4 ). Conversely, rs9273363 in the HLA-DQB1 locus is associated with increased risk of type 1 diabetes 25 , 26 and diabetic complications across ancestries, but only associates with increased risk of coeliac disease in individuals of EUR ancestry (Extended Data Fig. 5 ). Similarly, the TCF7L2 locus 27 strongly associates with increased risk of type 2 diabetes and associated complications across several ancestries (Extended Data Fig. 6 ). Association testing results are available in Supplementary Dataset 1 .

figure 4

Results of genetic-ancestry-stratified phenome-wide association analysis among unrelated individuals highlighting ancestry-specific disease associations across the four most common genetic ancestries of participant. Bonferroni-adjusted phenome-wide significance threshold (<2.88 × 10 −5 ) is plotted as a red horizontal line. AFR ( n  = 34,037, minor allele fraction (MAF) 0.82); AMR ( n  = 28,901, MAF 0.10); EAS ( n  = 32,55, MAF 0.003); EUR ( n  = 101,613, MAF 0.007).

The cloud-based Researcher Workbench

All of Us genomic data are available in a secure, access-controlled cloud-based analysis environment: the All of Us Researcher Workbench. Unlike traditional data access models that require per-project approval, access in the Researcher Workbench is governed by a data passport model based on a researcher’s authenticated identity, institutional affiliation, and completion of self-service training and compliance attestation 28 . After gaining access, a researcher may create a new workspace at any time to conduct a study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is regularly audited and made accessible publicly on the All of Us Research Projects Directory. This streamlined access model is guided by the principles that: participants are research partners and maintaining their privacy and data security is paramount; their data should be made as accessible as possible for authorized researchers; and we should continually seek to remove unnecessary barriers to accessing and using All of Us data.

For researchers at institutions with an existing institutional data use agreement, access can be gained as soon as they complete the required verification and compliance steps. As of August 2023, 556 institutions have agreements in place, allowing more than 5,000 approved researchers to actively work on more than 4,400 projects. The median time for a researcher from initial registration to completion of these requirements is 28.6 h (10th percentile: 48 min, 90th percentile: 14.9 days), a fraction of the weeks to months it can take to assemble a project-specific application and have it reviewed by an access board with conventional access models.

Given that the size of the project’s phenotypic and genomic dataset is expected to reach 4.75 PB in 2023, the use of a central data store and cloud analysis tools will save funders an estimated US$16.5 million per year when compared to the typical approach of allowing researchers to download genomic data. Storing one copy per institution of this data at 556 registered institutions would cost about US$1.16 billion per year. By contrast, storing a central cloud copy costs about US$1.14 million per year, a 99.9% saving. Importantly, cloud infrastructure also democratizes data access particularly for researchers who do not have high-performance local compute resources.

Here we present the All of Us Research Program’s approach to generating diverse clinical-grade genomic data at an unprecedented scale. We present the data release of about 245,000 genome sequences as part of a scalable framework that will grow to include genetic information and health data for one million or more people living across the USA. Our observations permit several conclusions.

First, the All of Us programme is making a notable contribution to improving the study of human biology through purposeful inclusion of under-represented individuals at scale 29 , 30 . Of the participants with genomic data in All of Us, 45.92% self-identified as a non-European race or ethnicity. This diversity enabled identification of more than 275 million new genetic variants across the dataset not previously captured by other large-scale genome aggregation efforts with diverse participants that have submitted variation to dbSNP v153, such as NHLBI TOPMed 31 freeze 8 (Extended Data Table 1 ). In contrast to gnomAD, All of Us permits individual-level genotype access with detailed phenotype data for all participants. Furthermore, unlike many genomics resources, All of Us is uniformly consented for general research use and enables researchers to go from initial account creation to individual-level data access in as little as a few hours. The All of Us cohort is significantly more diverse than those of other large contemporary research studies generating WGS data 32 , 33 . This enables a more equitable future for precision medicine (for example, through constructing polygenic risk scores that are appropriately calibrated to diverse populations 34 , 35 as the eMERGE programme has done leveraging All of Us data 36 , 37 ). Developing new tools and regulatory frameworks to enable analyses across multiple biobanks in the cloud to harness the unique strengths of each is an active area of investigation addressed in a companion paper to this work 38 .

Second, the All of Us Researcher Workbench embodies the programme’s design philosophy of open science, reproducible research, equitable access and transparency to researchers and to research participants 26 . Importantly, for research studies, no group of data users should have privileged access to All of Us resources based on anything other than data protection criteria. Although the All of Us Researcher Workbench initially targeted onboarding US academic, health care and non-profit organizations, it has recently expanded to international researchers. We anticipate further genomic and phenotypic data releases at regular intervals with data available to all researcher communities. We also anticipate additional derived data and functionality to be made available, such as reference data, structural variants and a service for array imputation using the All of Us genomic data.

Third, All of Us enables studying human biology at an unprecedented scale. The programmatic goal of sequencing one million or more genomes has required harnessing the output of multiple sequencing centres. Previous work has focused on achieving functional equivalence in data processing and joint calling pipelines 39 . To achieve clinical-grade data equivalence, All of Us required protocol equivalence at both sequencing production level and data processing across the sequencing centres. Furthermore, previous work has demonstrated the value of joint calling at scale 10 , 18 . The new GVS framework developed by the All of Us programme enables joint calling at extreme scales (Code availability). Finally, the provision of data access through cloud-native tools enables scalable and secure access and analysis to researchers while simultaneously enabling the trust of research participants and transparency underlying the All of Us data passport access model.

The clinical-grade sequencing carried out by All of Us enables not only research, but also the return of value to participants through clinically relevant genetic results and health-related traits to those who opt-in to receiving this information. In the years ahead, we anticipate that this partnership with All of Us participants will enable researchers to move beyond large-scale genomic discovery to understanding the consequences of implementing genomic medicine at scale.

The All of Us cohort

All of Us aims to engage a longitudinal cohort of one million or more US participants, with a focus on including populations that have historically been under-represented in biomedical research. Details of the All of Us cohort have been described previously 5 . Briefly, the primary objective is to build a robust research resource that can facilitate the exploration of biological, clinical, social and environmental determinants of health and disease. The programme will collect and curate health-related data and biospecimens, and these data and biospecimens will be made broadly available for research uses. Health data are obtained through the electronic medical record and through participant surveys. Survey templates can be found on our public website: https://www.researchallofus.org/data-tools/survey-explorer/ . Adults 18 years and older who have the capacity to consent and reside in the USA or a US territory at present are eligible. Informed consent for all participants is conducted in person or through an eConsent platform that includes primary consent, HIPAA Authorization for Research use of EHRs and other external health data, and Consent for Return of Genomic Results. The protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program. The All of Us IRB follows the regulations and guidance of the NIH Office for Human Research Protections for all studies, ensuring that the rights and welfare of research participants are overseen and protected uniformly.

Data accessibility through a ‘data passport’

Authorization for access to participant-level data in All of Us is based on a ‘data passport’ model, through which authorized researchers do not need IRB review for each research project. The data passport is required for gaining data access to the Researcher Workbench and for creating workspaces to carry out research projects using All of Us data. At present, data passports are authorized through a six-step process that includes affiliation with an institution that has signed a Data Use and Registration Agreement, account creation, identity verification, completion of ethics training, and attestation to a data user code of conduct. Results reported follow the All of Us Data and Statistics Dissemination Policy disallowing disclosure of group counts under 20 to protect participant privacy without seeking prior approval 40 .

At present, All of Us gathers EHR data from about 50 health care organizations that are funded to recruit and enrol participants as well as transfer EHR data for those participants who have consented to provide them. Data stewards at each provider organization harmonize their local data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, and then submit it to the All of Us Data and Research Center (DRC) so that it can be linked with other participant data and further curated for research use. OMOP is a common data model standardizing health information from disparate EHRs to common vocabularies and organized into tables according to data domains. EHR data are updated from the recruitment sites and sent to the DRC quarterly. Updated data releases to the research community occur approximately once a year. Supplementary Table 6 outlines the OMOP concepts collected by the DRC quarterly from the recruitment sites.

Biospecimen collection and processing

Participants who consented to participate in All of Us donated fresh whole blood (4 ml EDTA and 10 ml EDTA) as a primary source of DNA. The All of Us Biobank managed by the Mayo Clinic extracted DNA from 4 ml EDTA whole blood, and DNA was stored at −80 °C at an average concentration of 150 ng µl −1 . The buffy coat isolated from 10 ml EDTA whole blood has been used for extracting DNA in the case of initial extraction failure or absence of 4 ml EDTA whole blood. The Biobank plated 2.4 µg DNA with a concentration of 60 ng µl −1 in duplicate for array and WGS samples. The samples are distributed to All of Us Genome Centers weekly, and a negative (empty well) control and National Institute of Standards and Technology controls are incorporated every two months for QC purposes.

Genome Center sample receipt, accession and QC

On receipt of DNA sample shipments, the All of Us Genome Centers carry out an inspection of the packaging and sample containers to ensure that sample integrity has not been compromised during transport and to verify that the sample containers correspond to the shipping manifest. QC of the submitted samples also includes DNA quantification, using routine procedures to confirm volume and concentration (Supplementary Table 7 ). Any issues or discrepancies are recorded, and affected samples are put on hold until resolved. Samples that meet quality thresholds are accessioned in the Laboratory Information Management System, and sample aliquots are prepared for library construction processing (for example, normalized with respect to concentration and volume).

WGS library construction, sequencing and primary data QC

The DNA sample is first sheared using a Covaris sonicator and is then size-selected using AMPure XP beads to restrict the range of library insert sizes. Using the PCR Free Kapa HyperPrep library construction kit, enzymatic steps are completed to repair the jagged ends of DNA fragments, add proper A-base segments, and ligate indexed adapter barcode sequences onto samples. Excess adaptors are removed using AMPure XP beads for a final clean-up. Libraries are quantified using quantitative PCR with the Illumina Kapa DNA Quantification Kit and then normalized and pooled for sequencing (Supplementary Table 7 ).

Pooled libraries are loaded on the Illumina NovaSeq 6000 instrument. The data from the initial sequencing run are used to QC individual libraries and to remove non-conforming samples from the pipeline. The data are also used to calibrate the pooling volume of each individual library and re-pool the libraries for additional NovaSeq sequencing to reach an average coverage of 30×.

After demultiplexing, WGS analysis occurs on the Illumina DRAGEN platform. The DRAGEN pipeline consists of highly optimized algorithms for mapping, aligning, sorting, duplicate marking and haplotype variant calling and makes use of platform features such as compression and BCL conversion. Alignment uses the GRCh38dh reference genome. QC data are collected at every stage of the analysis protocol, providing high-resolution metrics required to ensure data consistency for large-scale multiplexing. The DRAGEN pipeline produces a large number of metrics that cover lane, library, flow cell, barcode and sample-level metrics for all runs as well as assessing contamination and mapping quality. The All of Us Genome Centers use these metrics to determine pass or fail for each sample before submitting the CRAM files to the All of Us DRC. For mapping and variant calling, all Genome Centers have harmonized on a set of DRAGEN parameters, which ensures consistency in processing (Supplementary Table 2 ).

Every step through the WGS procedure is rigorously controlled by predefined QC measures. Various control mechanisms and acceptance criteria were established during WGS assay validation. Specific metrics for reviewing and releasing genome data are: mean coverage (threshold of ≥30×), genome coverage (threshold of ≥90% at 20×), coverage of hereditary disease risk genes (threshold of ≥95% at 20×), aligned Q30 bases (threshold of ≥8 × 10 10 ), contamination (threshold of ≤1%) and concordance to independently processed array data.

Array genotyping

Samples are processed for genotyping at three All of Us Genome Centers (Broad, Johns Hopkins University and University of Washington). DNA samples are received from the Biobank and the process is facilitated by the All of Us genomics workflow described above. All three centres used an identical array product, scanners, resource files and genotype calling software for array processing to reduce batch effects. Each centre has its own Laboratory Information Management System that manages workflow control, sample and reagent tracking, and centre-specific liquid handling robotics.

Samples are processed using the Illumina Global Diversity Array (GDA) with Illumina Infinium LCG chemistry using the automated protocol and scanned on Illumina iSCANs with Automated Array Loaders. Illumina IAAP software converts raw data (IDAT files; 2 per sample) into a single GTC file per sample using the BPM file (defines strand, probe sequences and illumicode address) and the EGT file (defines the relationship between intensities and genotype calls). Files used for this data release are: GDA-8v1-0_A5.bpm, GDA-8v1-0_A1_ClusterFile.egt, gentrain v3, reference hg19 and gencall cutoff 0.15. The GDA array assays a total of 1,914,935 variant positions including 1,790,654 single-nucleotide variants, 44,172 indels, 9,935 intensity-only probes for CNV calling, and 70,174 duplicates (same position, different probes). Picard GtcToVcf is used to convert the GTC files to VCF format. Resulting VCF and IDAT files are submitted to the DRC for ingestion and further processing. The VCF file contains assay name, chromosome, position, genotype calls, quality score, raw and normalized intensities, B allele frequency and log R ratio values. Each genome centre is running the GDA array under Clinical Laboratory Improvement Amendments-compliant protocols. The GTC files are parsed and metrics are uploaded to in-house Laboratory Information Management System systems for QC review.

At batch level (each set of 96-well plates run together in the laboratory at one time), each genome centre includes positive control samples that are required to have >98% call rate and >99% concordance to existing data to approve release of the batch of data. At the sample level, the call rate and sex are the key QC determinants 41 . Contamination is also measured using BAFRegress 42 and reported out as metadata. Any sample with a call rate below 98% is repeated one time in the laboratory. Genotyped sex is determined by plotting normalized x versus normalized y intensity values for a batch of samples. Any sample discordant with ‘sex at birth’ reported by the All of Us participant is flagged for further detailed review and repeated one time in the laboratory. If several sex-discordant samples are clustered on an array or on a 96-well plate, the entire array or plate will have data production repeated. Samples identified with sex chromosome aneuploidies are also reported back as metadata (XXX, XXY, XYY and so on). A final processing status of ‘pass’, ‘fail’ or ‘abandon’ is determined before release of data to the All of Us DRC. An array sample will pass if the call rate is >98% and the genotyped sex and sex at birth are concordant (or the sex at birth is not applicable). An array sample will fail if the genotyped sex and the sex at birth are discordant. An array sample will have the status of abandon if the call rate is <98% after at least two attempts at the genome centre.

Data from the arrays are used for participant return of genetic ancestry and non-health-related traits for those who consent, and they are also used to facilitate additional QC of the matched WGS data. Contamination is assessed in the array data to determine whether DNA re-extraction is required before WGS. Re-extraction is prompted by level of contamination combined with consent status for return of results. The arrays are also used to confirm sample identity between the WGS data and the matched array data by assessing concordance at 100 unique sites. To establish concordance, a fingerprint file of these 100 sites is provided to the Genome Centers to assess concordance with the same sites in the WGS data before CRAM submission.

Genomic data curation

As seen in Extended Data Fig. 2 , we generate a joint call set for all WGS samples and make these data available in their entirety and by sample subsets to researchers. A breakdown of the frequencies, stratified by computed ancestries for which we had more than 10,000 participants can be found in Extended Data Fig. 3 . The joint call set process allows us to leverage information across samples to improve QC and increase accuracy.

Single-sample QC

If a sample fails single-sample QC, it is excluded from the release and is not reported in this document. These tests detect sample swaps, cross-individual contamination and sample preparation errors. In some cases, we carry out these tests twice (at both the Genome Center and the DRC), for two reasons: to confirm internal consistency between sites; and to mark samples as passing (or failing) QC on the basis of the research pipeline criteria. The single-sample QC process accepts a higher contamination rate than the clinical pipeline (0.03 for the research pipeline versus 0.01 for the clinical pipeline), but otherwise uses identical thresholds. The list of specific QC processes, passing criteria, error modes addressed and an overview of the results can be found in Supplementary Table 3 .

Joint call set QC

During joint calling, we carry out additional QC steps using information that is available across samples including hard thresholds, population outliers, allele-specific filters, and sensitivity and precision evaluation. Supplementary Table 4 summarizes both the steps that we took and the results obtained for the WGS data. More detailed information about the methods and specific parameters can be found in the All of Us Genomic Research Data Quality Report 36 .

Batch effect analysis

We analysed cross-sequencing centre batch effects in the joint call set. To quantify the batch effect, we calculated Cohen’s d (ref.  43 ) for four metrics (insertion/deletion ratio, single-nucleotide polymorphism count, indel count and single-nucleotide polymorphism transition/transversion ratio) across the three genome sequencing centres (Baylor College of Medicine, Broad Institute and University of Washington), stratified by computed ancestry and seven regions of the genome (whole genome, high-confidence calling, repetitive, GC content of >0.85, GC content of <0.15, low mappability, the ACMG59 genes and regions of large duplications (>1 kb)). Using random batches as a control set, all comparisons had a Cohen’s d of <0.35. Here we report any Cohen’s d results >0.5, which we chose before this analysis and is conventionally the threshold of a medium effect size 44 .

We found that there was an effect size in indel counts (Cohen’s d of 0.53) in the entire genome, between Broad Institute and University of Washington, but this was being driven by repetitive and low-mappability regions. We found no batch effects with Cohen’s d of >0.5 in the ratio metrics or in any metrics in the high-confidence calling, low or high GC content, or ACMG59 regions. A complete list of the batch effects with Cohen’s d of >0.5 are found in Supplementary Table 8 .

Sensitivity and precision evaluation

To determine sensitivity and precision, we included four well-characterized control samples (four National Institute of Standards and Technology Genome in a Bottle samples (HG-001, HG-003, HG-004 and HG-005). The samples were sequenced with the same protocol as All of Us. Of note, these samples were not included in data released to researchers. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations. We use the high-confidence calling region, defined by Genome in a Bottle v4.2.1, as the source of ground truth. To be called a true positive, a variant must match the chromosome, position, reference allele, alternate allele and zygosity. In cases of sites with multiple alternative alleles, each alternative allele is considered separately. Sensitivity and precision results are reported in Supplementary Table 5 .

Genetic ancestry inference

We computed categorical ancestry for all WGS samples in All of Us and made these available to researchers. These predictions are also the basis for population allele frequency calculations in the Genomic Variants section of the public Data Browser. We used the high-quality set of sites to determine an ancestry label for each sample. The ancestry categories are based on the same labels used in gnomAD 18 , the Human Genome Diversity Project (HGDP) 45 and 1000 Genomes 1 : African (AFR); Latino/admixed American (AMR); East Asian (EAS); Middle Eastern (MID); European (EUR), composed of Finnish (FIN) and Non-Finnish European (NFE); Other (OTH), not belonging to one of the other ancestries or is an admixture; South Asian (SAS).

We trained a random forest classifier 46 on a training set of the HGDP and 1000 Genomes samples variants on the autosome, obtained from gnomAD 11 . We generated the first 16 principal components (PCs) of the training sample genotypes (using the hwe_normalized_pca in Hail) at the high-quality variant sites for use as the feature vector for each training sample. We used the truth labels from the sample metadata, which can be found alongside the VCFs. Note that we do not train the classifier on the samples labelled as Other. We use the label probabilities (‘confidence’) of the classifier on the other ancestries to determine ancestry of Other.

To determine the ancestry of All of Us samples, we project the All of Us samples into the PCA space of the training data and apply the classifier. As a proxy for the accuracy of our All of Us predictions, we look at the concordance between the survey results and the predicted ancestry. The concordance between self-reported ethnicity and the ancestry predictions was 87.7%.

PC data from All of Us samples and the HGDP and 1000 Genomes samples were used to compute individual participant genetic ancestry fractions for All of Us samples using the Rye program. Rye uses PC data to carry out rapid and accurate genetic ancestry inference on biobank-scale datasets 47 . HGDP and 1000 Genomes reference samples were used to define a set of six distinct and coherent ancestry groups—African, East Asian, European, Middle Eastern, Latino/admixed American and South Asian—corresponding to participant self-identified race and ethnicity groups. Rye was run on the first 16 PCs, using the defined reference ancestry groups to assign ancestry group fractions to individual All of Us participant samples.

Relatedness

We calculated the kinship score using the Hail pc_relate function and reported any pairs with a kinship score above 0.1. The kinship score is half of the fraction of the genetic material shared (ranges from 0.0 to 0.5). We determined the maximal independent set 41 for related samples. We identified a maximally unrelated set of 231,442 samples (94%) for kinship scored greater than 0.1.

LDL-C common variant GWAS

The phenotypic data were extracted from the Curated Data Repository (CDR, Control Tier Dataset v7) in the All of Us Researcher Workbench. The All of Us Cohort Builder and Dataset Builder were used to extract all LDL cholesterol measurements from the Lab and Measurements criteria in EHR data for all participants who have WGS data. The most recent measurements were selected as the phenotype and adjusted for statin use 19 , age and sex. A rank-based inverse normal transformation was applied for this continuous trait to increase power and deflate type I error. Analysis was carried out on the Hail MatrixTable representation of the All of Us WGS joint-called data including removing monomorphic variants, variants with a call rate of <95% and variants with extreme Hardy–Weinberg equilibrium values ( P  < 10 −15 ). A linear regression was carried out with REGENIE 48 on variants with a minor allele frequency >5%, further adjusting for relatedness to the first five ancestry PCs. The final analysis included 34,924 participants and 8,589,520 variants.

Genotype-by-phenotype replication

We tested replication rates of known phenotype–genotype associations in three of the four largest populations: EUR, AFR and EAS. The AMR population was not included because they have no registered GWAS. This method is a conceptual extension of the original GWAS × phenome-wide association study, which replicated 66% of powered associations in a single EHR-linked biobank 49 . The PGRM is an expansion of this work by Bastarache et al., based on associations in the GWAS catalogue 50 in June 2020 (ref.  51 ). After directly matching the Experimental Factor Ontology terms to phecodes, the authors identified 8,085 unique loci and 170 unique phecodes that compose the PGRM. They showed replication rates in several EHR-linked biobanks ranging from 76% to 85%. For this analysis, we used the EUR-, and AFR-based maps, considering only catalogue associations that were P  < 5 × 10 −8 significant.

The main tools used were the Python package Hail for data extraction, plink for genomic associations, and the R packages PheWAS and pgrm for further analysis and visualization. The phenotypes, participant-reported sex at birth, and year of birth were extracted from the All of Us CDR (Controlled Tier Dataset v7). These phenotypes were then loaded into a plink-compatible format using the PheWAS package, and related samples were removed by sub-setting to the maximally unrelated dataset ( n  = 231,442). Only samples with EHR data were kept, filtered by selected loci, annotated with demographic and phenotypic information extracted from the CDR and ancestry prediction information provided by All of Us, ultimately resulting in 181,345 participants for downstream analysis. The variants in the PGRM were filtered by a minimum population-specific allele frequency of >1% or population-specific allele count of >100, leaving 4,986 variants. Results for which there were at least 20 cases in the ancestry group were included. Then, a series of Firth logistic regression tests with phecodes as the outcome and variants as the predictor were carried out, adjusting for age, sex (for non-sex-specific phenotypes) and the first three genomic PC features as covariates. The PGRM was annotated with power calculations based on the case counts and reported allele frequencies. Power of 80% or greater was considered powered for this analysis.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The All of Us Research Hub has a tiered data access data passport model with three data access tiers. The Public Tier dataset contains only aggregate data with identifiers removed. These data are available to the public through Data Snapshots ( https://www.researchallofus.org/data-tools/data-snapshots/ ) and the public Data Browser ( https://databrowser.researchallofus.org/ ). The Registered Tier curated dataset contains individual-level data, available only to approved researchers on the Researcher Workbench. At present, the Registered Tier includes data from EHRs, wearables and surveys, as well as physical measurements taken at the time of participant enrolment. The Controlled Tier dataset contains all data in the Registered Tier and additionally genomic data in the form of WGS and genotyping arrays, previously suppressed demographic data fields from EHRs and surveys, and unshifted dates of events. At present, Registered Tier and Controlled Tier data are available to researchers at academic institutions, non-profit institutions, and both non-profit and for-profit health care institutions. Work is underway to begin extending access to additional audiences, including industry-affiliated researchers. Researchers have the option to register for Registered Tier and/or Controlled Tier access by completing the All of Us Researcher Workbench access process, which includes identity verification and All of Us-specific training in research involving human participants ( https://www.researchallofus.org/register/ ). Researchers may create a new workspace at any time to conduct any research study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is made accessible publicly on the All of Us Research Projects Directory at https://allofus.nih.gov/protecting-data-and-privacy/research-projects-all-us-data .

Code availability

The GVS code is available at https://github.com/broadinstitute/gatk/tree/ah_var_store/scripts/variantstore . The LDL GWAS pipeline is available as a demonstration project in the Featured Workspace Library on the Researcher Workbench ( https://workbench.researchallofus.org/workspaces/aou-rw-5981f9dc/aouldlgwasregeniedsubctv6duplicate/notebooks ).

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Acknowledgements

The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers (OT2 OD026549; OT2 OD026554; OT2 OD026557; OT2 OD026556; OT2 OD026550; OT2 OD 026552; OT2 OD026553; OT2 OD026548; OT2 OD026551; OT2 OD026555); Inter agency agreement AOD 16037; Federally Qualified Health Centers HHSN 263201600085U; Data and Research Center: U2C OD023196; Genome Centers (OT2 OD002748; OT2 OD002750; OT2 OD002751); Biobank: U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: U24 OD023163; Communications and Engagement: OT2 OD023205; OT2 OD023206; and Community Partners (OT2 OD025277; OT2 OD025315; OT2 OD025337; OT2 OD025276). In addition, the All of Us Research Program would not be possible without the partnership of its participants. All of Us and the All of Us logo are service marks of the US Department of Health and Human Services. E.E.E. is an investigator of the Howard Hughes Medical Institute. We acknowledge the foundational contributions of our friend and colleague, the late Deborah A. Nickerson. Debbie’s years of insightful contributions throughout the formation of the All of Us genomics programme are permanently imprinted, and she shares credit for all of the successes of this programme.

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Authors and affiliations.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Alexander G. Bick & Henry R. Condon

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

Ginger A. Metcalf, Eric Boerwinkle, Richard A. Gibbs, Donna M. Muzny, Eric Venner, Kimberly Walker, Jianhong Hu, Harsha Doddapaneni, Christie L. Kovar, Mullai Murugan, Shannon Dugan, Ziad Khan & Eric Boerwinkle

Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

Kelsey R. Mayo, Jodell E. Linder, Melissa Basford, Ashley Able, Ashley E. Green, Robert J. Carroll, Jennifer Zhang & Yuanyuan Wang

Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Lee Lichtenstein, Anthony Philippakis, Sophie Schwartz, M. Morgan T. Aster, Kristian Cibulskis, Andrea Haessly, Rebecca Asch, Aurora Cremer, Kylee Degatano, Akum Shergill, Laura D. Gauthier, Samuel K. Lee, Aaron Hatcher, George B. Grant, Genevieve R. Brandt, Miguel Covarrubias, Eric Banks & Wail Baalawi

Verily, South San Francisco, CA, USA

Shimon Rura, David Glazer, Moira K. Dillon & C. H. Albach

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Robert J. Carroll, Paul A. Harris & Dan M. Roden

All of Us Research Program, National Institutes of Health, Bethesda, MD, USA

Anjene Musick, Andrea H. Ramirez, Sokny Lim, Siddhartha Nambiar, Bradley Ozenberger, Anastasia L. Wise, Chris Lunt, Geoffrey S. Ginsburg & Joshua C. Denny

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

I. King Jordan, Shashwat Deepali Nagar & Shivam Sharma

Neuroscience Institute, Institute of Translational Genomic Medicine, Morehouse School of Medicine, Atlanta, GA, USA

Robert Meller

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Mine S. Cicek, Stephen N. Thibodeau & Mine S. Cicek

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Kimberly F. Doheny, Michelle Z. Mawhinney, Sean M. L. Griffith, Elvin Hsu, Hua Ling & Marcia K. Adams

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

Evan E. Eichler, Joshua D. Smith, Christian D. Frazar, Colleen P. Davis, Karynne E. Patterson, Marsha M. Wheeler, Sean McGee, Mitzi L. Murray, Valeria Vasta, Dru Leistritz, Matthew A. Richardson, Aparna Radhakrishnan & Brenna W. Ehmen

Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

Evan E. Eichler

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Stacey Gabriel, Heidi L. Rehm, Niall J. Lennon, Christina Austin-Tse, Eric Banks, Michael Gatzen, Namrata Gupta, Katie Larsson, Sheli McDonough, Steven M. Harrison, Christopher Kachulis, Matthew S. Lebo, Seung Hoan Choi & Xin Wang

Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA

Gail P. Jarvik & Elisabeth A. Rosenthal

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Dan M. Roden

Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA

Center for Individualized Medicine, Biorepository Program, Mayo Clinic, Rochester, MN, USA

Stephen N. Thibodeau, Ashley L. Blegen, Samantha J. Wirkus, Victoria A. Wagner, Jeffrey G. Meyer & Mine S. Cicek

Color Health, Burlingame, CA, USA

Scott Topper, Cynthia L. Neben, Marcie Steeves & Alicia Y. Zhou

School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Eric Boerwinkle

Laboratory for Molecular Medicine, Massachusetts General Brigham Personalized Medicine, Cambridge, MA, USA

Christina Austin-Tse, Emma Henricks & Matthew S. Lebo

Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA

Christina M. Lockwood, Brian H. Shirts, Colin C. Pritchard, Jillian G. Buchan & Niklas Krumm

Manuscript Writing Group

  • Alexander G. Bick
  • , Ginger A. Metcalf
  • , Kelsey R. Mayo
  • , Lee Lichtenstein
  • , Shimon Rura
  • , Robert J. Carroll
  • , Anjene Musick
  • , Jodell E. Linder
  • , I. King Jordan
  • , Shashwat Deepali Nagar
  • , Shivam Sharma
  •  & Robert Meller

All of Us Research Program Genomics Principal Investigators

  • Melissa Basford
  • , Eric Boerwinkle
  • , Mine S. Cicek
  • , Kimberly F. Doheny
  • , Evan E. Eichler
  • , Stacey Gabriel
  • , Richard A. Gibbs
  • , David Glazer
  • , Paul A. Harris
  • , Gail P. Jarvik
  • , Anthony Philippakis
  • , Heidi L. Rehm
  • , Dan M. Roden
  • , Stephen N. Thibodeau
  •  & Scott Topper

Biobank, Mayo

  • Ashley L. Blegen
  • , Samantha J. Wirkus
  • , Victoria A. Wagner
  • , Jeffrey G. Meyer
  •  & Stephen N. Thibodeau

Genome Center: Baylor-Hopkins Clinical Genome Center

  • Donna M. Muzny
  • , Eric Venner
  • , Michelle Z. Mawhinney
  • , Sean M. L. Griffith
  • , Elvin Hsu
  • , Marcia K. Adams
  • , Kimberly Walker
  • , Jianhong Hu
  • , Harsha Doddapaneni
  • , Christie L. Kovar
  • , Mullai Murugan
  • , Shannon Dugan
  • , Ziad Khan
  •  & Richard A. Gibbs

Genome Center: Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine

  • Niall J. Lennon
  • , Christina Austin-Tse
  • , Eric Banks
  • , Michael Gatzen
  • , Namrata Gupta
  • , Emma Henricks
  • , Katie Larsson
  • , Sheli McDonough
  • , Steven M. Harrison
  • , Christopher Kachulis
  • , Matthew S. Lebo
  • , Cynthia L. Neben
  • , Marcie Steeves
  • , Alicia Y. Zhou
  • , Scott Topper
  •  & Stacey Gabriel

Genome Center: University of Washington

  • Gail P. Jarvik
  • , Joshua D. Smith
  • , Christian D. Frazar
  • , Colleen P. Davis
  • , Karynne E. Patterson
  • , Marsha M. Wheeler
  • , Sean McGee
  • , Christina M. Lockwood
  • , Brian H. Shirts
  • , Colin C. Pritchard
  • , Mitzi L. Murray
  • , Valeria Vasta
  • , Dru Leistritz
  • , Matthew A. Richardson
  • , Jillian G. Buchan
  • , Aparna Radhakrishnan
  • , Niklas Krumm
  •  & Brenna W. Ehmen

Data and Research Center

  • Lee Lichtenstein
  • , Sophie Schwartz
  • , M. Morgan T. Aster
  • , Kristian Cibulskis
  • , Andrea Haessly
  • , Rebecca Asch
  • , Aurora Cremer
  • , Kylee Degatano
  • , Akum Shergill
  • , Laura D. Gauthier
  • , Samuel K. Lee
  • , Aaron Hatcher
  • , George B. Grant
  • , Genevieve R. Brandt
  • , Miguel Covarrubias
  • , Melissa Basford
  • , Alexander G. Bick
  • , Ashley Able
  • , Ashley E. Green
  • , Jennifer Zhang
  • , Henry R. Condon
  • , Yuanyuan Wang
  • , Moira K. Dillon
  • , C. H. Albach
  • , Wail Baalawi
  •  & Dan M. Roden

All of Us Research Demonstration Project Teams

  • Seung Hoan Choi
  • , Elisabeth A. Rosenthal

NIH All of Us Research Program Staff

  • Andrea H. Ramirez
  • , Sokny Lim
  • , Siddhartha Nambiar
  • , Bradley Ozenberger
  • , Anastasia L. Wise
  • , Chris Lunt
  • , Geoffrey S. Ginsburg
  •  & Joshua C. Denny

Contributions

The All of Us Biobank (Mayo Clinic) collected, stored and plated participant biospecimens. The All of Us Genome Centers (Baylor-Hopkins Clinical Genome Center; Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine; and University of Washington School of Medicine) generated and QCed the whole-genomic data. The All of Us Data and Research Center (Vanderbilt University Medical Center, Broad Institute of MIT and Harvard, and Verily) generated the WGS joint call set, carried out quality assurance and QC analyses and developed the Researcher Workbench. All of Us Research Demonstration Project Teams contributed analyses. The other All of Us Genomics Investigators and NIH All of Us Research Program Staff provided crucial programmatic support. Members of the manuscript writing group (A.G.B., G.A.M., K.R.M., L.L., S.R., R.J.C. and A.M.) wrote the first draft of this manuscript, which was revised with contributions and feedback from all authors.

Corresponding author

Correspondence to Alexander G. Bick .

Ethics declarations

Competing interests.

D.M.M., G.A.M., E.V., K.W., J.H., H.D., C.L.K., M.M., S.D., Z.K., E. Boerwinkle and R.A.G. declare that Baylor Genetics is a Baylor College of Medicine affiliate that derives revenue from genetic testing. Eric Venner is affiliated with Codified Genomics, a provider of genetic interpretation. E.E.E. is a scientific advisory board member of Variant Bio, Inc. A.G.B. is a scientific advisory board member of TenSixteen Bio. The remaining authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Timothy Frayling and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended data fig. 1 historic availability of ehr records in all of us v7 controlled tier curated data repository (n = 413,457)..

For better visibility, the plot shows growth starting in 2010.

Extended Data Fig. 2 Overview of the Genomic Data Curation Pipeline for WGS samples.

The Data and Research Center (DRC) performs additional single sample quality control (QC) on the data as it arrives from the Genome Centers. The variants from samples that pass this QC are loaded into the Genomic Variant Store (GVS), where we jointly call the variants and apply additional QC. We apply a joint call set QC process, which is stored with the call set. The entire joint call set is rendered as a Hail Variant Dataset (VDS), which can be accessed from the analysis notebooks in the Researcher Workbench. Subsections of the genome are extracted from the VDS and rendered in different formats with all participants. Auxiliary data can also be accessed through the Researcher Workbench. This includes variant functional annotations, joint call set QC results, predicted ancestry, and relatedness. Auxiliary data are derived from GVS (arrow not shown) and the VDS. The Cohort Builder directly queries GVS when researchers request genomic data for subsets of samples. Aligned reads, as cram files, are available in the Researcher Workbench (not shown). The graphics of the dish, gene and computer and the All of Us logo are reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Extended Data Fig. 3 Proportion of allelic frequencies (AF), stratified by computed ancestry with over 10,000 participants.

Bar counts are not cumulative (eg, “pop AF < 0.01” does not include “pop AF < 0.001”).

Extended Data Fig. 4 Distribution of pathogenic, and likely pathogenic ClinVar variants.

Stratified by ancestry filtered to only those variants that are found in allele count (AC) < 40 individuals for 245,388 short read WGS samples.

Extended Data Fig. 5 Ancestry specific HLA-DQB1 ( rs9273363 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight ancestry specific consequences across ancestries.

Extended Data Fig. 6 Ancestry specific TCF7L2 ( rs7903146 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight diabetic consequences across ancestries.

Supplementary information

Supplementary information.

Supplementary Figs. 1–7, Tables 1–8 and Note.

Reporting Summary

Supplementary dataset 1.

Associations of ACKR1, HLA-DQB1 and TCF7L2 loci with all Phecodes stratified by genetic ancestry.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

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The All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature (2024). https://doi.org/10.1038/s41586-023-06957-x

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Received : 22 July 2022

Accepted : 08 December 2023

Published : 19 February 2024

DOI : https://doi.org/10.1038/s41586-023-06957-x

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Job Market Candidate Aniruddha Ghosh’s paper accepted at Journal of Economic Theory

  • Posted on: February 27, 2024
  • Posted in: News

AniHeadShot

Aniruddha Ghosh, a graduate student in the department, recently published a paper at Journal of Economic Theory. The paper titled, “ On the Existence of Berk-Nash Equilibria in Misspecified Markov Decision Processes with Infinite Spaces, ” co-authored with Robert Anderson (UC Berkeley), Haosui Duanmu (HIT), and M. Ali Khan (JHU), addresses the critical issue of model misspecification in economics. It focuses on misspecified dynamic programming environments, which are routinely used to model decision-making problems in economics. The work substantially extends the applicability of a popular equilibrium framework for misspecified environments, known as Berk-Nash equilibrium, from finite environments to infinite environments of more general interest.

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Reddit Files to Go Public, in First Social Media I.P.O. in Years

The message board site, founded in 2005, detailed its financial performance in a filing. It is the last of an early generation of social media companies to aim for a public offering.

A hallway with the word “reddit” and the company’s symbol taking up a large wall.

By Mike Isaac

Mike Isaac has covered Reddit and social media companies since 2010 from San Francisco.

Reddit, the community-focused message board site, filed to go public on Thursday, paving the way for it to be the first major social media company to debut on the stock market in years and a test for private companies after a drought in initial public offerings.

In an offering prospectus , Reddit disclosed its financial performance in preparation for selling shares to investors. The San Francisco-based company reported that its revenue rose more than 20 percent as its losses narrowed last year. It added that it had 73 million daily users and more than 100,000 active communities.

The prospectus kicks off a process to the stock market, with the 18-year-old company set to meet potential investors to whet their appetites for buying its shares. Reddit could go public on the New York Stock Exchange in a matter of weeks under the stock symbol RDDT.

Reddit’s bankers are seeking a valuation of at least $5 billion in its I.P.O., according to two people familiar with the matter. That is roughly half of the $10 billion valuation the company fetched in a 2021 private financing round. The talks are continuing, and the price could still rise or fall in the weeks ahead.

Reddit is the last of an earlier generation of social media companies to aim for the stock market, after Facebook’s high-profile offering in 2012 , Twitter’s in 2013 and Snap’s in 2017 . In the years since, the social media industry has changed, facing scrutiny for misinformation, hate speech and other effects. Some of the companies have shifted directions; Facebook was renamed Meta, and Twitter was bought by Elon Musk , who took the company private in 2022 and renamed it X.

Reddit’s move is also highly anticipated after a lull in initial public offerings. Just 108 companies went public in the United States last year, roughly a quarter of the number that debuted in 2021, according to data compiled by Renaissance Capital. Some of the biggest tech offerings last year were Arm, a chip designer , and Instacart , a grocery delivery company.

“We are going public to advance our mission and become a stronger company,” Steve Huffman, Reddit’s chief executive, said in a founder’s letter included in the prospectus. “We hope going public will provide meaningful benefits to our community as well. Our users have a deep sense of ownership over the communities they create on Reddit.”

Mr. Huffman added that the company wanted “this sense of ownership to be reflected in real ownership — for our users to be our owners” and that “becoming a public company makes this possible.” Reddit said it would reserve a chunk of its shares at the I.P.O. price for 75,000 of the company’s most prolific users if they wished to purchase them.

In its prospectus, Reddit said revenue in 2023 was $804 million, up about 21 percent from $666 million a year earlier. The company lost $90 million in 2023, compared with a $158 million loss the year before, according to the prospectus.

Some of its largest shareholders include Advance Magazine Publishers, Tencent Cloud Europe, Vy Capital, Fidelity Management, and Sam Altman, a former Reddit board member and the chief executive of OpenAI.

Reddit’s path to the public markets has been long and rocky. Founded in a University of Virginia dorm room in 2005 by Mr. Huffman and Alexis Ohanian, the site began as a destination for anonymous users to come together and discuss anything from popular TV shows, to guitars, makeup and power washers.

The site was unique in that it largely focused on tightknit communities, mostly anonymous, all moderated by volunteers who self-governed their forums, or “subreddits,” based on rules of their own making. It became known for “A.M.A.s,” otherwise known as the “ask me anything” sessions, sometimes with public figures like former President Barack Obama, Microsoft’s Bill Gates and the actor Nicolas Cage.

The company raised hundreds of millions of dollars in funding over the years, including $250 million and more than $410 million in two financing rounds in 2021. Investors include Fidelity Investments, Andreessen Horowitz, Sequoia Capital and Tencent Holdings.

Like other early social networking efforts, Reddit initially eschewed offering advertising and making money. It instead focused on forms of revenue that came from community ideas, like a user-generated e-commerce system and awards that users could buy one another. Those ideas are still in play.

Reddit eventually embraced advertising based on its topic-focused communities. Brands like Laneige, for instance, targeted ads to a forum called Makeup Addiction, one of the most active subreddits, in which users discuss cosmetics and how to apply them.

The site has also built an emerging data licensing business based on its enormous corpus of conversation data, which has become increasingly important amid a frenzy over artificial intelligence. A.I. models are trained on gobs of such data so that they can become more powerful. On Thursday, Reddit announced a licensing deal with Google, which has used Reddit data to train and build its A.I. systems.

“We expect our data advantage and intellectual property to continue to be a key element in the training of” future A.I. models, Mr. Huffman said in the letter. The company has a number of undisclosed licensing agreements to use its data and expects to make upward of $203 million over the next three years from those contracts, according to the filing.

The site has had its share of struggles. It faced controversy after controversy over its refusal to moderate communities in its early years, including its role in spreading misinformation during the Boston Marathon bombing in 2013 , and hosting racist and misogynistic content in some of its smaller subreddits. Last year, Reddit faced a user revolt after changing some of its rules and restricting third-party developers from using the site’s content without paying for it.

Reddit has reversed its position on moderation and has updated and more strictly enforced its policies in recent years, making it more attractive for marketers to place advertising across the site.

The company also had a revolving door of leaders in its first decade, being helmed by four chief executives before Mr. Huffman returned to lead the site in 2015 .

Reddit cautioned potential investors that it faced challenges and potential risks as a public company, including the rise of large language models, the underlying A.I. systems that could potentially aggregate and synthesize the site’s content and let users view Reddit without visiting the site or seeing advertising.

The company may also face difficulty courting brands in a digital ad market dominated by Meta and Google.

“Reddit may face a daunting challenge in growing its advertising business, given the gap between its platform’s capabilities and those that are best in class,” said Eric Seufert, an independent mobile analyst who closely monitors social media companies and advertising.

The company also warned that it was heavily dependent on its community for moderating the platform, and that future revolts or departures could harm the site.

“We have many opportunities and much to do,” Mr. Huffman said.

Lauren Hirsch contributed reporting from New York.

Mike Isaac is a technology correspondent for The Times based in San Francisco. He regularly covers Facebook and Silicon Valley. More about Mike Isaac

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