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What makes an online review credible? A systematic review of the literature and future research directions

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• Published: 05 December 2022

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• K. Pooja   ORCID: orcid.org/0000-0001-7735-8308 1 &
• Pallavi Upadhyaya   ORCID: orcid.org/0000-0003-4523-2051 2  

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Online reviews of products and services are strategic tools for e-commerce platforms, as they aid in consumers’ pre-purchase decisions. Past research studies indicate online reviews impact brand image and consumer behaviour. With several instances of fake reviews and review manipulations, review credibility has become a concern for consumers and service providers. In recent years, due to growing webcare attitude among managers, the need for maintaining credible online reviews on the e-commerce platforms has gained attention. Though, there are several empirical studies on review credibility, the findings are diverse and contradicting. Therefore, in this paper, we systematically review the literature to provide a holistic view of antecedents of online review credibility. We examine variables, methods, and theoretical perspective of online review credibility research using 69 empirical research papers shortlisted through multi-stage selection process. We identify five broad groups of antecedents: source characteristics, review characteristics, consumer characteristics, interpersonal determinants in the social media platform and product type. Further, we identify research issues and propose directions for future research. This study contributes to existing knowledge in management research by providing the holistic understanding of the “online review credibility” construct and helps understand what factors lead to consumers’ belief in the credibility of online review. The insights gained would provide managers adequate cues to design effective online review systems.

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1 Introduction

Online reviews of products and services have become an integral component of product information on e-commerce platforms and are often used as strategic instrument to gain competitive advantage (Gutt et al. 2019 ). They are influential in marketing communications and help shoppers identify the products (Chen and Xie 2008 ) and make informed pre-purchase decisions (Hong and Pittman 2020 ; Eslami et al. 2018 ; Klaus and Changchit 2019 ; Reyes- Menendez et al. 2019 ). In the absence of physical interaction with the product, they aid consumers to take decisions based on experiences shared by previous users on the e-commerce platform (Klaus and Changchit 2019 ). Reviews facilitate the free flow of consumer-generated content that help managers promote their products or brand or company (Smith 2011 ). The products that get at least 5 reviews have a 270% higher conversion rate compared to the products with no reviews (Collinger et al. 2017 ).

With the growing popularity of online reviews, there is an overwhelming interest among researchers to understand the characteristics of reviews and reviewer that contribute to the credibility of online reviews (Cheung et al. 2009 ; Chih et al. 2020 ; Fang and Li 2016 ; Jimenez and Mendoza 2013 ; Liu and Ji 2018 ; Mumuni et al. 2019 ; Qiu et al. 2012 ; Tran and Can 2020 ; Yan et al. 2016 ). The credibility of online information and digital media is often contested, due to the lack of quality control standards and ambiguity concerning the ownership of the information with the convergence of information and media channels (Flanagin and Metzger 2007 ). As all online reviews cannot be trusted (Johnson and Kaye 2016 ) and when sources are uncertain (Lim and Van Der Heide 2015 ) consumers often use cues to assess review credibility. The credibility issue also arises due to review manipulation practices by asking the reviewers to write a positive review in favour of the brand and to write a negative review attacking the competitor's product, by incentivizing the reviewer (Wu et al. 2015 ).

Recent meta-analysis studies on electronic word of mouth (eWOM) communications have focused on factors impacting eWOM providing behaviour (Ismagilova et al. 2020a ), the effect of eWOM on intention to buy (Ismagilova et al. 2020b ), the effect of source credibility on consumer behaviour (Ismagilova et al. 2020c ), factors affecting adoption of eWOM message (Qahri-Saremi and Montazemi 2019 ) and eWOM elasticity (You et al. 2015 ). Moran and Muzellec ( 2017 ) and recently Verma and Dewani ( 2020 ) have proposed four-factor frameworks for eWOM Credibility. Zheng ( 2021 ) presented a systematic review of literature on the classification of online consumer reviews.

Even though there are literature reviews and meta-analysis on eWOM, they address different research questions or constructs in eWOM and no attempt to synthesise the antecedents of online review credibility, in the context of products and services has been made. Xia et al. ( 2009 ) posit that all eWOM are not formulated equally and classify eWOM as “many to one” (e.g., No of ratings, downloads calculated by computers), “many to many” (e.g., Discussion forums), “one to many” (e.g., Text-based product reviews), and “one to one” (instant messaging). Studies confirm that the effort to process and persuasiveness of different forms of eWOM vary (Weisfeld -Spolter et al. 2014 ). Senecal and Nantel ( 2004 ) argue that consumers spend significantly more time and effort to process online reviews than any other form of eWOM. Hence understanding credibility of the online reviews and the factors that influence credibility is important for managers of e-commerce platforms.

Our objective in this paper is three-fold: First, we revisit, review, and synthesize 69 empirical research on online review credibility that focuses on textual online reviews of products and services (“one to many” form of eWOM). Second, we identify the antecedents of review credibility. Finally, we identify gaps and propose future research directions in the area of online reviews and online review credibility. From theoretical perspective, this systematic review synthesises the antecedents of review credibility, in the context of online reviews of products and services. As in past literature, eWOM and online reviews are interchangeably used, we carefully analysed both the eWOM credibility and online review credibility and selected studies that focused on reviews of products and services. Studies on sponsored posts on social media, blogs, the brand initiated eWOM communication were excluded. From managerial perspective, this study would aid managers of e-commerce platforms, a holistic view of review credibility and aid in the design of online review systems.

1.1 Defining online review credibility

Mudambi and Schuff ( 2010 ) define online reviews as “peer-generated product evaluations, posted on company or third-party websites”. Person-to-person communication via the internet is eWOM. An online review is a form of eWOM. There are various channels of eWOM such as social media, opinion forums, review platforms, and blogs. Past literature posits that credible eWOM is one that is perceived as believable, true, or factual (Fogg et al. 2001 ; Tseng and Fogg. 1999 ).

The perception a consumer holds regarding the veracity of online review is considered as the review credibility (Erkan and Evans 2016 ). Several research studies (Cheung et al. 2009 ; Dong 2015 ) define credible online reviews as a review that the consumers perceive as truthful, logical, and believable. Past research defines credibility to be associated with consumers’ perception and evaluation and not as a direct measure of the reality of reviews (Chakraborty and Bhat 2018a ). The credibility of online reviews is described as consumers’ assessment of the accuracy (Zha et al. 2015 ) and validity of the reviews (Chakraborty and Bhat 2017 ).

2 Research methods

This paper uses the systematic literature review method (Linnenluecke et al. 2020 ; Moher et al. 2009 ; Neumann 2021 ; Okoli 2015 ; Snyder 2019 ) to synthesize the research findings. Liberati et al. ( 2009 ) explains systematic review as a process for identifying, critically appraising relevant research and analyzing data. Systematic reviews differ from meta-analysis with respect to methods of analysis used. While meta-analysis focuses primarily on quantitative and statistical analysis; systematic reviews use both quantitative and qualitative analysis and critical appraisal of the literature. In a systematic review, pre-specified protocols on inclusion and exclusion of the articles are used to identify the evidence that fits the criteria to answer the research question (Snyder 2019 ). In this paper, we follow the steps proposed by Okoli ( 2015 ) for conducting the systematic review process and the recommendations given by Fisch and Block ( 2018 ) to improve the quality of the review. The purpose of our systematic literature review is to identify and synthesize the antecedents of online review credibility.

The study uses journal articles from two popular research databases (Scopus and Web of Science) to conduct a systematic search of articles on review credibility/eWOM credibility. As online reviews are interchangeably used with other related concepts such as eWOM, user-generated content, and online recommendations in the literature, we used a diverse pool of sixteen keywords (refer Fig.  1 ) for the initial search. The keywords were identified through an initial review of literature and articles having these terms in the title, abstract, and keywords were chosen. Initial search and document retrieval were done in January 2022. Studies published till October 2022 were later updated in the paper. A set of filters using inclusion and exclusion criteria were applied to arrive at a focused set of relevant papers. The full-length empirical articles in English language, related to business management and allied areas were included for systematic review. Using multiple phases of filtering and reviewing (refer Fig.  1 ), we shortlisted the final list of 69 empirical papers that used either review credibility or eWOM credibility as a construct with a focus on reviews of products and services. In line with previous systematic reviews (Kuckertz and Brändle 2022 ; Nadkarni and Prügl 2021 ; Walter 2020) we excluded work in progress papers, conference papers, dissertations or books from the analysis.

Systematic review process

2.1 Descriptive analysis of empirical research on online review credibility

The 69 empirical research articles included 36 experimental design studies and 33 cross-sectional survey-based studies. Figure  2 summarises the review credibility publication trends in the last decade with their research design choices.

Research designs of Review credibility articles

Research on review credibility has used samples from diverse geographical regions, the highest number of studies being in the USA, China, and Taiwan (refer to Table 1 ). Table 2 and Table 3 summarizes the sample and analysis methods used in these studies. Even though online review is commonly used in tourism and hospitality, there are only six studies examining review credibility.

3 Theoretical perspectives in review credibility literature

Most of the empirical research (88 percent) on review credibility has used theories to explain the antecedents of review credibility. A total of 48 different theories have been invoked in explaining various dimensions of review credibility antecedents.

We observed five broad groups of theories from the underlying 48 theories that contribute to understanding the different aspects of online review credibility assessment by consumers. We discuss them in the following sections.

3.1 Information processing in online review

Several theories provide a lens to understand ways in which individual consumes or processes the information available in the online reviews. The popular theories discussed in the review credibility literature such as the elaboration likelihood model, heuristic—systematic model, accessibility—diagnosticity theory, and attribution theory describe how an individual processes information.

Building on the elaboration likelihood model (ELM) several studies have examined characteristics of online review content such as argument quality (Cheung et al. 2009 ; Hussain et al. 2018 ; Thomas et al. 2019 ), review sidedness (Cheung et al. 2012 ; Brand and Reith 2022 ), review consistency (Brand et al. 2022 ; Brand and Reith 2022 ; Cheung et al. 2012 ; Thomas et al. 2019 ), and source credibility (Cheung et al. 2012 ; Hussain et al. 2018 ; Reyes- Menendez et al. 2019 ). These dimensions are also examined using the heuristics-systematic model (HSM). These two theories are similar in their function as both ELM and HSM posit two routes (the central vs. peripheral route and the systematic vs. heuristic route) for judging the persuasiveness of messages (Chang and Wu 2014 ). In literature, the elaboration likelihood model has received more empirical support compared to the heuristics systematic model. The yale persuasive communication theory covers a wider array of factors that can affect the acceptance of the message (Chang and Wu 2014 ). This theory has been adopted by studies to evaluate the relationship between these factors with review credibility.

The psychological choice model posits that the effectiveness of online reviews gets influenced by environmental factors like product characteristics and consumer’s past experience. These factors influences the credibility assessment by the consumer and purchase decision based on their interaction with the online reviews.

Consumers’ use of information for judgment also depends upon the accessibility and diagnosticity of the input as proposed in accessibility-diagnosticity theory. This theory helps in understanding the utilization of information by individuals and posits that the information in hand has more value than information stored as a form of memory (Tsao and Hseih 2015 ; Chiou et al. 2018 ). The attribution theory helps in understanding the nature of the causal conclusion drawn by the consumers in the presence of negative and positive information (Chiou et al. 2018 ).

Overall, the theories related to information processing have contributed well to understanding the influence of strength of the message, argument, valence, source reputation, consistency, persuasiveness, and diagnosability.

Theories such as media richness theory (Tran and Can 2020 ) and language expectancy theory (Seghers et al. 2021 ) provided insights into the relevance of the quality of the information shared in online reviews. Several other theories focus on the information adoption process (ex. Information adoption mode, informational influence theory, dual-process theory). For example, cognitive cost theory has been used to explain review adoption due to the effect of different levels of cognitive involvement of the consumer when they are exposed to reviews from different platforms simultaneously (Yan et al. 2016 ).

The contribution of technology acceptance model (TAM) to the review credibility literature is operationalized in the study by Liu and Ji ( 2018 ). Hussain et al. ( 2018 ) uses TAM to complement ELM in the computer-mediated communication adoption process.

We observe that the theories in information processing in the online review have provided a theoretical lens to understand the role of the quality of the information in the online review credibility assessment.

3.2 Trust in online reviews

Studies have examined the trust formation and perception of the trustworthiness of the source of the information in online reviews using the theoretical lens of trust transfer theory and source credibility theory. Virtual communities do not support the face-to-face interaction between sender and receiver of the message. Therefore, the receiver has to rely on cues such as the reputation of the source, credibility of the source, and the reviewer profile. These cues are observed as some of the antecedents of review credibility. Trust transfer theory contributes to our understanding of how online reviews shared on a trusted e-commerce website makes the consumer consider that review is credible compared to the review shared on a website that is not trustworthy (Park and Lee 2011 ). Source credibility theory suggests trustworthiness and expertise of the source of the review have a positive relationship with review credibility (Mumuni et al. 2019 ; Shamhuyenhanzva et al. 2016 ). These theories note that when a person perceives the origin of online review as trustworthy, he would be more likely to consume the information.

3.3 Socio-cultural influence in online reviews

Individuals’ innate values or beliefs help shape their behaviour. As online reviews are more complex social conversations (Kozinets 2016 ) there is a need to gain perspectives on how these conversations differ in terms of country and culture (Bughin et al. 2010 ). The theories such as culture theory, and Hall’s categorization provide a lens to examine the influence of culture on online review consumption and assessment of review credibility (Brand and Reith 2022 ; Chiou et al. 2014 ; Luo et al. 2014 ).

In general, attention paid to understanding the influence of cultural factors on online reviews is very limited (Mariani et al. 2019 ; Gao et al. 2017 ). However, much attention has been given to understanding the role of social influence through the use of theories like social influence theory, role theory, social identity theory, social information processing theory, socio-cognitive systems theory, and value theory. The most prominent theory related to this theme is the social influence theory. Social influence theory emphasizes the social pressure faced by consumers to form a decision based on online reviews (Jha and Shah 2021 ). Social identity theory posits that an individual may reduce uncertainty by choosing to communicate with other people who share similar values and social identities (Kusumasondjaja et al. 2012 ).

Social information processing theory posits the importance of the closeness between review writer and reader on social networking as an alternative cue, in the absence of physical interaction (Lim and Van Der Heide 2015 ). The social standings of an individual in terms of the number of friends on social networks (Lim and Van Der Heide 2015 ), nonverbal cues such as profile photos (Xu 2014 ), and their impact on review credibility have been studied using this theory. In a nutshell, these theories explain individuals’ belief that gets shaped due to the influence of the social groups and how it impacts the credibility of the review.

3.4 Consumer attitude and behaviour towards online reviews

Consumers attitude towards computer-mediated communications and online reviews have been examined in past studies (Chakraborty and bhat 2017 ; Chih et al. 2020 ; Hussain et al. 2018 ; Isci and Kitapci 2020 ; Jha and Shah 2021 ) using several theoretical frameworks. Theories such as attitude—behaviour linkage, cognition-affection-behaviour (CAB) model, expectancy-disconfirmation theory (EDT), needs theory, regulatory focus theory, search and alignment theory, stimulus- organism-response model, theory of planned behaviour, yale attitude change model, associative learning theory were used in literature to examine the factors that influence the formation of the attitude and behaviour towards online reviews. These factors and their relationship with credibility evaluation have been studied by the yale attitude change model (Chakraborty and Bhat 2017 , 2018b ), and the stimulus-organism-response model (Chakraborty 2019 ). Jha and Shah ( 2021 ) adapted attitude-behavior linkage theory to study how the exposure to past reviews acts as an influence to write credible reviews.

The consumer’s expectation about product experience and credibility assessment is studied using theories like expectancy-disconfirmation theory (Jha and Shah 2021 ), needs theory (Anastasiei et al. 2021 ), and regulatory focus theory (Isci and Kitapci, 2020 ; Lee and Koo, 2012 ). Overall, these theories have contributed to the advancement of the understanding of the holistic process involved in consumer attitude formation and behaviour in online reviews.

3.5 Risk aversion

The theories such as category diagnosticity theory, prospect theory, uncertainty management theory, and uncertainty reduction theory provide a theoretical lens to examine how consumers rely on credible information to avoid uncertain outcomes. Hong and Pittman ( 2020 ) use category diagnosticity theory and prospect theory to hypothesize negative online reviews as more credible than positive reviews. An individual who focuses on reducing loss perceives negative online reviews as more diagnostic and credible. Kusumasondjaja et al. ( 2012 ) also argue that consumers try to avoid future losses by spending effort to find credible information before making a decision. With the help of these underlying assumptions, studies have used perspectives drawn from theories to understand the loss-aversion behaviour and higher perceived diagnostic value of negative information. Prospect theory suggests consumers attempt to avoid risks or loss and expect gain. Consumers avoid choosing the experience which has more negative online reviews because of the risk and loss associated with the negativity of the reviews (Floh et al. 2013 ). The risk aversion-related theories have contributed to understanding the consumers’ quest for credible information in negative reviews.

4 Antecedents of online review credibility

Literature on review credibility reveals varied nomenclature and operationalisation of antecedents of review credibility. However, we can broadly categorize review credibility antecedents into five broad groups: source characteristics, message characteristics, consumer characteristics, social/interpersonal influence, and product type (Refer to Fig.  3 ).

Anteeedents of review credibility

We discuss these antecedent themes along with the major constructs in each theme in the following sections. In the final section, we also summarise the theoretical perspectives in each antecedent themes.

4.1 Source characteristics

Literature reveals that several characteristics of the source influence the credibility perception and evaluation of review by consumers. Chakraborty and Bhat ( 2017 ) define a source as the person who writes online reviews. Researchers have operationalized the source characteristics primarily through reviewers’ knowledge and reliability (Chakraborty and Bhat 2017 ); reviewer characteristics such as identity disclosure, level of expertise, review experience, and total useful votes (Liu and Ji 2018 ). In several studies (Cheung et al. 2012 ; Chih et al. 2013 ; Mumuni et al. 2019 ; Newell and Goldsmith 2001 ; Reyes- Menendez et al. 2019 ; Yan et al. 2016 ), expertise and trustworthiness of the reviewer is one of the most common conceptualizations of source credibility. Cheung and Thadani ( 2012 ) define source credibility as the “message source’s perceived ability (expertise) or motivation to provide accurate and truthful (trustworthiness) information”.

Source credibility is used as a single construct in several studies (Abedin et al. 2021 ; Chih et al. 2013 ; Cheung et al. 2009 , 2012 ; Mumuni et al. 2019 ; Reyes-Menendez et al. 2019 ; Yan et al. 2016 ; Luo et al. 2014 ). Studies have also conceptualized its sub-dimensions such as source trustworthiness (Chih et al. 2020 ; Lo and Yao 2018 ; Shamhuyenhanzva et al. 2016 ; Siddiqui et al. 2021 ; Thomas et al. 2019 ; Tien et al. 2018 ); reviewer expertise (Anastasiei et al. 2021 ; Fang 2014 ; Fang and Li 2016 ; Jha and Shah 2021 ) and reviewers’ authority (Shamhuyenhanzva et al. 2016 ), as separate antecedents to review credibility. Mumuni et al. ( 2019 ) posited that reviewer expertise and reviewer trustworthiness as two distinct constructs. Chih et al. ( 2020 ) define source trustworthiness as the credibility of the information presented by the message sender. Thomas et al. ( 2019 ) operationalize reviewer expertise as a peripheral cue and found that the amount of knowledge that a reviewer has about a product or service is influential in consumer’s perception of review credibility. Information presented by professional commentators who are perceived as experts in the specific field was found to have a positive influence on credibility (Chiou et al. 2014 ).

Source cues help in assessing the credibility and usefulness of the information shared in product reviews (Liu and Ji 2018 ). Reviews written by the source whose identity is disclosed have higher credibility compared to the reviews written by unidentified sources (Kusumasondjaja et al. 2012 ). However, in case of positive reviews with disclosed identity of the sponsor the review, credibility is negatively affected (Wang et al. 2022 ). Zhang et al. ( 2020 ) found that suspicion about the identity of the message sender influences negatively on the message’s credibility. Past studies found that when the number of friends of a reviewer (Lim and Van Der Heide 2015 ) and a number of trusted members of the reviewer (Xu 2014 ) are high in the online review community, reviews of such reviewers are considered as more credible. If a reviewer involves very actively in writing the review, the number of reviews posted by the reviewer provides evidence to the reader that the reviews written by such reviewers are credible (Lim and Van Der Heide 2015 ). The consumer also believes online reviews to be credible when they perceive the reviewer as honest (Yan et al. 2021) and caring (Yan et al. 2021). The source characteristics as antecedents of review credibility are summarized in Table 4 .

Several studies also define the source with the characteristics of the platform where the review is published. Consumers’ trust on the website (Lee et al. 2011 ) and the reputation of the website (Chih et al. 2013 ) were found as antecedents of the review credibility. If a consumer perceives an online shopping mall as trustworthy, he would believe that reviews posted in shopping mall as credible (Lee et al. 2011 ). Chih et al. ( 2013 ) posit that in addition to the source credibility (reviewer expertise), consumers evaluate the quality of contents of a website based on website reputation, which in turn leads to higher trust on the website and higher perceived credibility of the review. Website reputation is defined as the extent to which consumers perceive the platform where the review is published to be believable and trustworthy (Chih et al. 2013 ; Thomas et al. 2019 ; Tran and Can 2020 ; Guzzo et al. 2022 ; Majali et al. 2022 ). Bae and Lee ( 2011 ) found that consumer-developed sites were perceived as more credible than marketer-developed sites. Similarly, Tsao and Hsieh ( 2015 ) found that review quality as perceived by consumers had a higher impact on review credibility on independent platforms than on corporate-run platforms. Ha and Lee ( 2018 ) found that for credence service (eg. Hospital), the provider-driven platform and reviews were more credible and for experience goods (eg. Restaurant), consumer-driven platforms were perceived as more credible.

4.2 Review characteristics

Several characteristics of the message or the review are found to influence the review credibility on online review platforms (presented in Table 5 ). A product with a large number of reviews provides evidence of higher sales and popularity of the product (Flanagin and Metzger 2013 ; Hong and Pittman 2020 ; Reyes- Menendez et al. 2019 ). When online review for a product or service is higher, it directly influences the review credibility (Hong and Pittman 2020 ; Reyes- Menendez et al. 2019 ; Thomas et al. 2019 ; Tran and Can 2020 ).

If the reviewer agrees with most of online reviews or recommendations of others those reviews are considered as consistent reviews (Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ). The consistent online reviews were found to have higher credibility (Abedin et al. 2021 ; Baharuddin and Yaacob 2020 ; Brand and Reith 2022 ; Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ; Cheung et al. 2009 , 2012 ; Luo et al. 2014 ; Tran and Can 2020 ). Fang and Li ( 2016 ) found out that receiver of the information actively monitors the consistency of the information while perceiving the credibility of review. The degree of agreement in aggregated review ratings on the review platform creates consensus among the reviewers (Qiu et al. 2012 ). Information evolved from such consensus is perceived as highly credible (Lo and Yao 2018 ; Qiu et al. 2012 ). However, a few studies (Cheung et al. 2012 ; Luo et al. 2015 ; Thomas et al. 2019 ) have reported contradicting findings and argue that when the involvement of consumers is low and consumers are knowledgeable, review consistency has an insignificant impact on the review credibility.

Past studies have found strong evidence on the impact of review argument quality (Anastasiei et al. 2021 ; Baharuddin and Yaacob 2020 ; Cheung et al. 2012 ; Thomas et al. 2019 ; Tran and Can 2020 ; Tsao and Hsieh 2015 ) and review quality (Bambauer-Sachse and Mangold 2010 ; Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ; Liu and Ji 2018 ) and argument strength (Cheung et al. 2009 ; Fang 2014 ; Fang and Li 2016 ; Luo et al. 2015 ) on review credibility. Concreteness in the argument also positively impacts the review credibility (Shukla and Mishra 2021 ).

According to Petty et al. ( 1983 ), the strength of the argument provided in the message represents the quality of the message. Cheung et al. ( 2009 ) define argument strength as the quality of the information in the online review. Chakraborty and Bhat ( 2017 ) present review quality as the logical and reliable argument in the online review. Recent studies (Thomas et al. 2019 ; Tran and Can 2020 ) considered accuracy and completeness as dimensions of argument quality.

Review attribute helps in classifying the review as an objective review or subjective review based on the information captured (Lee and Koo 2012 ). Jimenez and Mendoza (2013); Gvili and Levy ( 2016 ) operationalize the level of detail as the amount of information present in the review about a product or service. Past studies have found evidence for the positive relationship between different attributes of reviews such as review objectivity (Luo et al. 2015 ; Abedin et al. 2021 ), level of detail (Jimenez and Mendoza 2013 ), review attribute (Lee and Koo 2012 ), message readability (Guzzo et al. 2022 ), persuasiveness of eWOM messages (Tien et al. 2018 ), interestingness (Shamuyenhanzva et al. 2016 ), graphics (Fang and Li 2016 ) and suspicion of truthfulness (Zhang et al. 2020 ) with review credibility. Vendemia ( 2017 ) found that the emotional content of information in the review also influences the review credibility. While assessing the review credibility, the utilitarian function of the review (Ran et al. 2021 ) and message content (Siddiqui et al. 2021 ) play an important role.

Several studies confirm that review valence influences review credibility (Lee and Koo 2012 ; Hong and Pittman 2020 ; Lo and Yao 2018 ; Manganari and Dimara 2017 ; Pentina et al. 2018 ; Pentina et al. 2017 ; vanLohuizen and Trujillo-Barrera 2019 ; Kusumasondjaja et al. 2012 ; Lim and Van Der Heide 2015 ; Chiou et al. 2018 ). Chiou et al. ( 2018 ) explain review valence is negative or positive evaluation of the product or service in online reviews. Review valence is often operationalized in experimental research at two levels: positive reviews vs negative reviews. Several studies report that negative reviews are perceived to be more credible than positive reviews (Chiou et al. 2018 ; Kusumasondjaja et al. 2012 ; Lee and Koo 2012 ; Lo and Yao 2018 ; Manganari and Dimara 2017 ). Negative reviews present a consumer’s bad experience, service failure or low quality and they create a loss-framed argument. Tversky and Kahneman ( 1991 ) explain that loss-framed arguments have a greater impact on the behaviour of consumer than gain-framed arguments. Contradictory to these findings, a few studies found that positive reviews are more credible than negative reviews (Hong and Pittman 2020 ; Pentina et al. 2017 , 2018 ). Lim and Van Der Heide ( 2015 ) found that though negative reviews impact greatly on consumer behavior it is perceived to be less credible.

Several studies (Chakraborty 2019 ; Cheung et al. 2012 ; Luo et al. 2015 ) have observed the impact of review sidedness (positive, negative or two-sided reviews) on review credibility and found that two-sided reviews are perceived as more credible. Further, Cheung et al. ( 2012 ) found that when consumers’ expertise level was high and involvement level was low, review sidedness had a stronger impact on review credibility.

Star ratings are numerical evidence of product performance (Hong and Pittman 2020 ). Star rating represents the average rating of all the review ratings therefore it helps to assess the conclusions in general (Tran and Can 2020 ). Rating evaluation needs a low amount of cognitive effort while processing the review information (Thomas et al. 2019 ). Past studies have found star ratings (Hong and Pittman 2020 ), aggregated review scores (Camilleri 2017 ), product or service ratings (Thomas et al. 2019 ; Tran and Can 2020 ), review ratings (Luo et al. 2015 ), and recommendation or information rating (Cheung et al. 2009 ) act as peripheral cues influencing the review credibility.

4.3 Consumer characteristics

Receiver is the consumer of the review and consumer needs, traits, motivation, knowledge, and involvement have been found to influence the review credibility. Chih et al. ( 2013 ) posit that online community members have two types of needs: functional need (need to find useful product information) and social need (need to build social relationships with others). These needs motivate consumers to use online reviews and form perceptions of review credibility. Consumers refer to online reviews to understand the product's pros, cons, and costs (Hussain et al. 2018 ); reduce purchase risk, and information search time (Schiffman and Kanuk 2000 ).

Past research studies indicate consumer’s motivation to obtain more information on purchase context (Chih et al. 2013 ), self-worth reinforcement (Hussain et al. 2018 ), opinion seeking from other consumers (Hussain et al. 2018 ), and prior knowledge of the receiver on the product (Cheung and Thadani 2012 ; Wang et al. 2013 ), influences review credibility. When the online reviews are congruous to the consumer’s knowledge and experiences, the message is perceived to be credible (Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ; Cheung et al. 2009 ). Chiou et al. ( 2018 ) found that high-knowledge consumers find reviews less credible. Studies in the past have also used prior knowledge of consumers as a control variable (Bae and Lee 2011 ) and moderating variable (Doh and Hwang 2009 ) when studying other factors. Bambauer-Sachse and Mangold ( 2010 ) found that knowledge on manipulations on product reviews influenced consumers' product evaluations, negative reviews, in particular, and when they come from a highly credible source.

Lim and Van Der Heide ( 2015 ) observed differences in the perceived credibility of users and non-users of the review platform and found an interaction effect between users’ familiarity with the review platform and reviewer profile (number of friends and number of reviews) characteristics of review credibility. Consumer experience with online reviews affects their perception of review credibility (Guzzo et al 2022 ). Izogo et al ( 2022 ) posit that consumer experiences such as sensory, cognitive and behavioral experience also influences review credibility. Consumer motivation, beliefs, and knowledge, as antecedents in literature, are summarised in Table 6 .

Cheung et. al ( 2012 ) posited that the influence of source and message characteristics on review credibility depends on two characteristics of the consumer: involvement and expertise. The authors found that level of involvement and knowledge of consumers moderate the relationships between review characteristics (review consistency and review sidedness) source credibility, and review credibility. Consumers process the information through central route, when making high involvement decisions and carefully read the content (Lin et al. 2013 ; Park and Lee 2008 ). When consumers have low involvement decisions, they are more likely to use peripheral cues and pay lesser attention to the review content, resulting in low eWOM credibility. Xue and Zhou ( 2010 ) found that consumers with high involvement decisions trusted negative reviews. In a recent study, Zhang et al. ( 2020 ) found that personality traits such as dispositional trust can trigger suspicion about the truthfulness of the message and may in turn, impact review credibility.

4.4 Interpersonal influence in the social media

Earlier research shows that interpersonal influence (Chu and Kim 2011 ) and tie strength (Bansal and Voyer 2000 ) positively influences online reviews. Consumers perceive online reviews as more credible when social status and cognitive dissonance reduction can be achieved through online forums (Chih et al. 2013 ). The previous studies have considered these factors under the theme related to source or communicator of the message (Verma and Dewani 2020 )). However, the constructs tie strength and homophily represent an interpersonal relationship between the communicator and the reader. Therefore, we discuss them separately. Tie strength is considered to be higher in an online community when the members have close relationships with other members and frequently communicate with each other. Consumers who have similar tastes and preferences share information in brand communities and enjoy meeting other members in a meaningful way (Xiang et al. 2017 ). Reviews are found to be more credible when review writers get exposed to past reviews written by others (Jha and Shah 2021 ). The exposure to past reviews moderates the relationship between disconfirmation and perception of online review credibility (Jha and Shah 2021 ). The recommendations of the members on social networking sites have also been found to be influencing the credibility of online reviews (Siddiqui et al. 2021 ).

Consumers’ perceptions of their similarity to the source of message are believed to impact their credibility assessment (Gilly et al. 1998 ; Wangenheim and Bayon 2004). Brown and Reingen ( 1987 ) define similarity or homophily as the “degree to which individuals are similar to sources in terms of certain attributes”. Herrero and Martin ( 2015 ) found that hotel consumers would perceive reviews more credible when there is a similarity between users and content creators. Source homophily is found to have an impact on review credibility in the e-commerce context as well (Abedin et al. 2021 ). Similarity of the source is often described in terms of interests of consumers and content generators. Xu ( 2014 ) posits that when a greater number of trusted members for reviewers are present on the website, it increases trust, thereby impacting the perceived credibility of the review. (Table 7 ).

4.5 Product type

The type of the product (search or experience product) is found to impact user’s evaluation of review credibility (Bae and Lee 2011 ; Jimenez and Mendoza 2013 ) and review helpfulness (Mudambi and Schuff 2010 ). Experience products differ from search products. They require more effort in retrieving product’s attribute-related information online and often require direct experience to assess the product features accurately. Bae and Lee ( 2011 ) found that when review originates from the consumer-owned online community, consumers find review credible for experience products. Tsao and Hsieh ( 2015 ) found that the credibility of eWOM is stronger for credence products than search products. Credence goods are those whose qualities cannot be confirmed even after purchase, such as antivirus software and sellers often cheat consumers due to information asymmetry and charge higher prices for inferior goods.

Jimenez and Mendoza ( 2013 ) found differences in consumers’ evaluation of review credibility for search and experience products. The study found that for search products detailed reviews were considered more credible and for experience products, reviewer agreement impacted review credibility (Jimenez and Mendoza 2013 ). Chiou et al. ( 2014 ) found that the review credibility was perceived differently for elite (eg: Classical musical concerts) and mass (eg: movies) cultural offerings. The study posited that when consumers read reviews of elite cultural offerings, and it originates from professionals, it is perceived as more credible. (Table 8 ).

4.6 Summary of antecedent themes and theoretical perspectives

Review characteristics, followed by source characteristics, are the most researched themes in terms of the number of studies and theories used (refer to Fig.  4 ). It indicates the wide coverage of different theoretical perspectives examined in these two areas. Consumer characteristics, interpersonal determinants in social media, and product type were less researched antecedent themes and lesser examined through a theoretical lens.

Anteeedent themewise articles and theories

The most popular theories in review credibility literature are the elaboration likelihood model, social influence theory, accessibility- diagnosticity theory, attribution theory, and theory of reasoned action. Contribution from these theories was noted in at least four antecedent themes identified in our study. Table 9 summarizes the theories used in each antecedent theme identified in the current review.

5 Review credibility: future research directions

Though there is ample research on online review credibility, there are several gaps in understanding the aspects of consumer behavior in online review evaluation and mitigation of issues with credibility. We identify six research issues that need further investigation and empirical evidence.

5.1 Research issue 1: review credibility in a high-involvement decision-making context

Several studies have examined credibility of reviews in experience products such as movies (Chiou et al. 2014 ; Flanagin and Metzer 2013 ), restaurants (Ha and Lee 2018 ; Pentina et al. 2017 ; vanLohuizen and Trujillo-Barrera 2019 ), hotels (Lo and Yao 2018 ; Manganari and Dimara 2017 ), and search goods such as audiobooks (Camilleri 2017 ), consumer electronics (Bambauer-Sachse and Mangold 2010 ; Chiou et al. 2018 ; Lee et al. 2011 ; Lee and Koo 2012 ; Tsao and Hsieh 2015 ; Xu 2014 ), few studies (Jimenez and Mendoza 2013 ; Doh and Hwang 2009 ; Xue and Zhou 2010 ; Bae and Lee 2011 ) have examined both experience and search products.

However, most of the products involve low to medium involvement of consumers and there is a gap in understanding online review usage, credibility, and impact in the context of high involvement decisions. There are several online review platforms on high involvement goods and services such as cars (eg: carwale, auto-drive), and destination holiday planning (TripAdvisor). Consumers often use online reviews to reduce purchase risk. As purchase risks are higher in high involvement decisions, consumers would spend more time searching online to evaluate the product. It is also necessary to understand to what extent consumers trust online reviews in a high involvement decision context, which often combines online information, reviews, and offline experiences (eg: visit to a car dealership for a test drive). Previous studies on consumer involvement (Hussain et al. 2018 ; Lin et al. 2013 ; Park and Lee 2008 ; Reyes-Menendez et al. 2019 ; Xue and Zhou, 2010 ) have operationalized involvement as a multi-item construct that captures the level of involvement of consumers, using consumers’ response. Experimental design studies, using high involvement goods and their reviews would help to establish causal relationships, in high involvement goods context. As an exception, one of the recent studies by Isci and Kitapci ( 2020 ) uses experimental design using automobile products as the stimuli for the experiment. However, as observed in our analysis, there are scarce studies in high involvement decision making context.

5.2 Research issue 2: mitigation of low credibility of the online review

While extant literature is available on factors affecting review credibility and its impact on brand and consumer behavior, there is limited literature and discussion on how companies can mitigate the impact of low credibility of reviews and improve trust. More evidence and empirical research is required to demonstrate effectiveness of measures that firms can take to build credibility and improve trust. As reviews are an important component of product information in e-commerce websites and reviews are used to form pre-purchase decisions, research on mitigation of poor credibility would be useful. For example, while past research shows that reviews on marketer-developed sites are perceived less credible for experience products than consumer-developed sites (Bae and Lee 2011 ). There is a need to study strategies that marketers can use to gain the trust of consumers.

5.3 Research issue 3: mitigating impact of negative online reviews

Past studies have indicated that consumers pay more attention to negative reviews (Kusumasondjaja et al. 2012 ; Lee and Koo 2012 ; vanLohuizen and Barrera 2019 ; Yang and Mai 2010 ), and trust (Xue and Zhou 2010 ; Banerjee and Chua 2019 ) more than positive reviews. Negative reviews are found to be persuasive and have a higher impact on brand interest and purchase intention (Xue and Zhou 2010 ). There are also limited studies discussing the ways to mitigate the impact of negative reviews and strategies to deal with them in a wide variety of contexts. While extant literature is available on review characteristics such as review sidedness, review valence, and its impact on review credibility (Refer to Table 5 ), there is little empirical evidence on strategies to deal with negative reviews. An exception is a study by Pee ( 2016 ), that addressed this issue by focusing on marketing mix and suggested that managing the marketing mix can mitigate the impact of negative reviews. However, more research is needed to equip marketers with mitigation techniques and fair strategies to deal with negative reviews.

5.4 Research issue 4: credibility of brand initiated online reviews

Brand-initiated eWOM often incentivizes consumers to share the content with their friends and it is unclear whether such initiatives are perceived as less credible. Brands use a variety of strategies to promote products on social media and facilitate person-to-person communications of brand content such as referral rewards, coupons, and bonus points (Abu-El-Rub et al. 2017 ). Incentivized reviews can easily manipulate consumers as their motive is not to provide unbiased information to make an informed decision (Mayzlin et al. 2014 ).

These practices followed by the service providers, or the vendors could jeopardize the trust consumers have towards them. More research in this area would provide insights into the best social media marketing practices that are considered credible. Future research must focus on guiding marketers on ethical and credible practices in social media marketing and managing online reviews.

5.5 Research issue 5: presence of fake online reviews

Unlike incentivized reviews, deceptive opinion spams are written to sound real and to deceive the review readers (Ott, Cardie and Hancock 2013 ; Hernández Fusilier et al. 2015 ). Spammers use extreme language when it comes to praising or criticizing (Gao et al. 2021 ). These spammers are active on several social media and review platforms. As technology is continuously evolving deceptive opinion spam has found a way through the use of artificial intelligence. The social media platforms like Twitter and Facebook have experienced the rise of bot or automated accounts. This trend is even entering into online review systems and is a threat to the online review system Tousignant ( 2017 ). A study conducted by Yao et al. ( 2017 ) argues that the reviews generated by bots are not only undetectable but also scored as useful reviews. This is a serious issue as the whole purpose of online review platforms is to provide information that would lead an individual to make an informed decision, but these fake reviews severely damage the credibility of review site (Munzel 2016 ). In recent years, researchers started contributing to this area and have proposed models to detect fake reviews in different platforms such as app stores (Martens and Maalej 2019 ), online review platforms (Singh and Kumar 2017 ), and filtering fake reviews on TripAdvisor (Cardoso et al. 2018 ). However, presence of fake reviews can make the review users skeptical towards using the reviews. Future research must focus on the role of artificial intelligence in online review systems and its impact on consumers’ assessment of online review credibility. Research into tools to detect and curb the spread of fake reviews is needed to improve credibility of reviews.

5.6 Research issue 6: new forms of online reviews

Rapid technological developments have resulted in new digital formats of online reviews such as video and images. Past experimental design studies have primarily used stimuli in the form of textual reviews. As consumers use more and more multimedia data and engage in platforms such as Youtube.com or Instagram.com, research is required to examine the online review credibility and practices using new forms of reviews.

6 Theoretical contribution and managerial implications and conclusions

This paper makes three important theoretical contributions. First, it provides a consolidated account of antecedents, mediators and moderators of the construct online review credibility identifies five broad groups of antecedents. Second, this paper also makes a maiden attempt to map the antecedent themes to the theoretical frameworks in the literature. This mapping provides a holistic understanding of theories that examine various facets of online review credibility. In the process, we also identify theoretical lenses that are less investigated. Third we identify research gaps and issues that needs further investigation in the area of online review credibility. Some of the areas of future research include mitigation strategies for negative reviews and credibility of reviews in purchase of high-involvement product or service. Emergence of new forms of multimedia reviews, fake reviews and sponsored reviews have also triggered the need to push research beyond simple text reviews. Future research could use theoretical lens that have been less explored to investigate research issues in review credibility. There is a need to advance online review credibility research beyond the popular theoretical frameworks such as elaboration likelihood model, social influence theory, accessibility- diagnosticity theory, attribution theory, and theory of reasoned action.

The paper has several managerial implications. The lower credibility of reviews poses threat to its relevance in digital marketing and electronic commerce. Therefore, managers of electronic commerce must strive to adopt practices to preserve the trust and integrity of online reviews. Our review indicated five groups of antecedents of online review credibility: source characteristics, review characteristics, consumer characteristics, interpersonal characteristics in social media, and product type. Managers cannot control completely all the factors on the social media. However, by appropriately designing the e-commerce platform with the elements that influence credibility, managers will be able to improve their marketing communications. Awareness of review characteristics that impact review credibility would help managers to choose more appropriate measures to deal with negative and positive reviews. Managers must adopt a social media marketing strategy that is suitable to the context of the review and type of product.

Data availability

The dataset was generated by two licensed databases and thus cannot be made accessible.

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Pooja, K., Upadhyaya, P. What makes an online review credible? A systematic review of the literature and future research directions. Manag Rev Q (2022). https://doi.org/10.1007/s11301-022-00312-6

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A systematic review of online examinations: A pedagogical innovation for scalable authentication and integrity

Kerryn butler-henderson.

a College of Health and Medicine, University of Tasmania, Locked Bag 1322, Launceston, Tasmania, 7250, Australia

Joseph Crawford

b Academic Division, University of Tasmania, Locked Bag 1322, Launceston, Tasmania, 7250, Australia

Digitization and automation across all industries has resulted in improvements in efficiencies and effectiveness to systems and process, and the higher education sector is not immune. Online learning, e-learning, electronic teaching tools, and digital assessments are not innovations. However, there has been limited implementation of online invigilated examinations in many countries. This paper provides a brief background on online examinations, followed by the results of a systematic review on the topic to explore the challenges and opportunities. We follow on with an explication of results from thirty-six papers, exploring nine key themes: student perceptions, student performance, anxiety, cheating, staff perceptions, authentication and security, interface design, and technology issues. While the literature on online examinations is growing, there is still a dearth of discussion at the pedagogical and governance levels.

• • There is a lack of score variation between examination modalities.
• • Online exams offer various methods for mitigating cheating.
• • There is a favorable ratings for online examinations by students.
• • Staff preferred online examinations for their ease of completion and logistics.
• • The interface of a system continues to be an enabler or barrier of online exams.

1. Introduction

Learning and teaching is transforming away from the conventional lecture theatre designed to seat 100 to 10,000 passive students towards more active learning environments. In our current climate, this is exacerbated by COVID-19 responses ( Crawford et al., 2020 ), where thousands of students are involved in online adaptions of face-to-face examinations (e.g. online Zoom rooms with all microphones and videos locked on). This evolution has grown from the need to recognize that students now rarely study exclusively and have commitments that conflict with their University life (e.g. work, family, social obligations). Students have more diverse digitally capability ( Margaryan et al., 2011 ) and higher age and gender diversity ( Eagly & Sczesny, 2009 ; Schwalb & Sedlacek, 1990 ). Continual change of the demographic and profile of students creates a challenge for scholars seeking to develop a student experience that demonstrates quality and maintains financial and academic viability ( Gross et al., 2013 ; Hainline et al., 2010 ).

Universities are developing extensive online offerings to grow their international loads and facilitate the massification of higher learning. These protocols, informed by growing policy targets to educate a larger quantity of graduates (e.g. Kemp, 1999 ; Reiko, 2001 ), have challenged traditional university models of fully on-campus student attendance. The development of online examination software has offered a systematic and technological alternative to the end-of-course summative examination designed for final authentication and testing of student knowledge retention, application, and extension. As a result of the COVID-19 pandemic, the initial response in higher education across many countries was to postpone examinations ( Crawford et al., 2020 ). However, as the pandemic continued, the need to move to either an online examination format or alternative assessment became more urgent.

This paper is a timely exploration of the contemporary literature related to online examinations in the university setting, with the hopes to consolidate information on this relatively new pedagogy in higher education. This paper begins with a brief background of traditional examinations, as the assumptions applied in many online examination environments build on the techniques and assumptions of the traditional face-to-face gymnasium-housed invigilated examinations. This is followed by a summary of the systematic review method, including search strategy, procedure, quality review, analysis, and summary of the sample.

Print-based educational examinations designed to test knowledge have existed for hundreds of years. The New York State Education Department has “the oldest educational testing service in the United States” and has been delivering entrance examinations since 1865 ( Johnson, 2009 , p. 1; NYSED, 2012 ). In pre-Revolution Russia, it was not possible to obtain a diploma to enter university without passing a high-stakes graduation examinations ( Karp, 2007 ). These high school examinations assessed and assured learning of students in rigid and high-security conditions. Under traditional classroom conditions, these were likely a reasonable practice to validate knowledge. The discussion of authenticating learning was not a consideration at this stage, as students were face to face only. For many high school jurisdictions, these are designed to strengthen the accountability of teachers and assess student performance ( Mueller & Colley, 2015 ).

In tertiary education, the use of an end-of-course summative examination as a form of validating knowledge has been informed significantly by accreditation bodies and streamlined financially viable assessment options. The American Bar Association has required a final course examination to remain accredited ( Sheppard, 1996 ). Law examinations typically contained brief didactic questions focused on assessing rote memory through to problem-based assessment to evaluate students’ ability to apply knowledge ( Sheppard, 1996 ). In accredited courses, there are significant parallels. Alternatives to traditional gymnasium-sized classroom paper-and-pencil invigilated examinations have been developed with educators recognizing the limitations associated with single-point summative examinations ( Butt, 2018 ).

The objective structured clinical examinations (OSCE) incorporate multiple workstations with students performing specific practical tasks from physical examinations on mannequins to short-answer written responses to scenarios ( Turner & Dankoski, 2008 ). The OSCE has parallels with the patient simulation examination used in some medical schools ( Botezatu et al., 2010 ). Portfolios assess and demonstrate learning over a whole course and for extracurricular learning ( Wasley, 2008 ).

The inclusion of online examinations, e-examinations, and bring-your-own-device models have offered alternatives to the large-scale examination rooms with paper-and-pencil invigilated examinations. Each of these offer new opportunities for the inclusion of innovative pedagogies and assessment where examinations are considered necessary. Further, some research indicates online examinations are able to discern a true pass from a true fail with a high level of accuracy ( Ardid et al., 2015 ), yet there is no systematic consolidation of the literature. We believe this timely review is critical for the progression of the field in first stepping back and consolidating the existing practices to support dissemination and further innovation. The pursuit of such systems may be to provide formative feedback and to assess learning outcomes, but a dominant rationale for final examinations is to authenticate learning. That is, to ensure the student whose name is on the student register, is the student who is completing the assessed work. The development of digitalized examination pilot studies and case studies are becoming an expected norm with universities developing responses to a growing online curriculum offering (e.g. Al-Hakeem & Abdulrahman, 2017 ; Alzu'bi, 2015 ; Anderson et al., 2005 ; Fluck et al., 2009 ; Fluck et al., 2017 ; Fluck, 2019 ; Seow & Soong, 2014 ; Sindre & Vegendla, 2015 ; Steel et al., 2019 ; Wibowo et al., 2016 ).

As many scholars highlight, cheating is a common component of the contemporary student experience ( Jordan, 2001 ; Rettinger & Kramer, 2009 ) despite that it should not be. Some are theorizing responses to the inevitability of cheating from developing student capacity for integrity ( Crawford, 2015 ; Wright, 2011 ) to enhancing detection of cheating ( Dawson & Sutherland-Smith, 2018 , 2019 ) and legislation to ban contract cheating ( Amigud & Dawson, 2020 ). We see value in the pursuit of methods that can support integrity in student assessment, including during rapid changes to the curriculum. The objective of this paper is to summarize the current evidence on online examination methods, and scholarly responses to authentication of learning and the mitigation of cheating, within the confines of assessment that enables learning and student wellbeing. We scope out preparation for examinations (e.g. Nguyen & Henderson, 2020 ) to enable focus on the online exam setting specifically.

2. Material and methods

2.1. search strategy.

To address the objective of this paper, a systematic literature review was undertaken, following the PRISMA approach for article selection ( Moher et al., 2009 ). The keyword string was developed incorporating the U.S. National Library of Medicine (2019) MeSH (Medical Subject Headings) terms: [(“online” OR “electronic” OR “digital”) AND (“exam*” OR “test”) AND (“university” OR “educat*” OR “teach” OR “school” OR “college”)]. The following databases were queried: A + Education (Informit), ERIC (EBSCO), Education Database (ProQuest), Education Research Complete (EBSCO), Educational Research Abstracts Online (Taylor & Francis), Informit, and Scopus. These search phrases will enable the collection of a broad range of literature on online examinations as well as terms often used synonymously, such as e-examination/eExams and BYOD (bring-your-own-device) examinations. The eligibility criteria included peer-reviewed journal articles or full conference papers on online examinations in the university sector, published between 2009 and 2018, available in English. As other sources (e.g. dissertations) are not peer-reviewed, and we aimed to identify rigorous best practice literature, we excluded these. We subsequently conducted a general search in Google Scholar and found no additional results. All records returned from the search were extracted and imported into the Covidence® online software by the first author.

2.2. Selection procedure and quality assessment

The online Covidence® software facilitated article selection following the PRISMA approach. Each of the 1906 titles and abstracts were double-screened by the authors based on the eligibility criteria. We also excluded non-higher education examinations, given the context around student demographics is often considerably different than vocational education, primary and high schools. Where there was discordance between the authors on a title or abstract inclusion or exclusion, consensus discussions were undertaken. The screening reduced the volume of papers significantly because numerous papers related to a different education context or involved online or digital forms of medical examinations. Next, the full-text for selected abstracts were double-reviewed, with discordance managed through a consensus discussion. The papers selected following the double full-text review were accepted for this review. Each accepted paper was reviewed for quality using the MMAT system ( Hong et al., 2018 ) and the scores were calculated as high, medium, or low quality based on the matrix ( Hong et al., 2018 ). A summary of this assessment is presented in Table 1 .

Summary of article characteristics.

QAS, quality assessment score.

2.3. Thematic analysis

Following the process described by Braun and Clarke (2006) , an inductive thematic approach was undertaken to identify common themes identified in each article. This process involves six stages: data familiarization, data coding, theme searching, theme review, defining themes, and naming themes. Familiarization with the literature was achieved during the screening, full-text, and quality review process by triple exposure to works. The named authors then inductively coded half the manuscripts each. The research team consolidated the data together to identify themes. Upon final agreement of themes and their definitions, the write-up was split among the team with subsequent review and revision of ideas in themes through independent and collaborative writing and reviewing ( Creswell & Miller, 2000 ; Lincoln & Guba, 1985 ). This resulted in nine final themes, each discussed in-depth during the discussion.

There were thirty-six (36) articles identified that met the eligibility criteria and were selected following the PRISMA approach, as shown in Fig. 1 .

PRISMA results.

3.1. Characteristics of selected articles

The selected articles are from a wide range of discipline areas and countries. Table 1 summarizes the characteristics of the selected articles. The United States of America held a vast majority (14, 38.9%) of the publications on online examinations, followed by Saudi Arabia (4, 11.1%), China (2, 5.6%), and Australia (2, 5.6%). When aggregated at the region-level, there was an equality of papers from North America and Asia (14, 38.9% each), with Europe (6, 16.7%) and Oceania (2, 5.6%) least represented in the selection of articles. There has been considerable growth in publications in the past five years, concerning online examinations. Publications between the years 2009 and 2015 represented a third (12, 33.3%) of the total number of selected papers. The majority (24, 66.7%) of papers were published in the last three years. Papers that described a system but did not include empirical evidence scored a low-quality rank as they did not meet many of the criteria that relate to the evaluation of a system.

When examining the types of papers, the majority (30, 83.3%) were empirical research, with the remainder commentary papers (6, 16.7%). Of the empirical research papers, three-quarters of the paper reported a quantitative study design (32, 88.9%) compared to two (5.6%) qualitative study designs and two (5.6%) that used a mixed method. For quantitative studies, there was a range between nine and 1800 student participants ( x ̄  = 291.62) across 26 studies, and a range between two and 85 staff participants ( x ̄  = 30.67) in one study. The most common quantitative methods were self-administered surveys and analysis of numerical examination student grades (38% each). Qualitative and mixed methods studies only adopted interviews (6%). Only one qualitative study reported a sample of students ( n  = 4), with two qualitative studies reporting a sample of staff ( n  = 2, n  = 5).

3.2. Student perceptions

Today's students prefer online examinations compared to paper exams ([68.75% preference of online over paper-based examinations: Attia, 2014 ; 56–62.5%: Böhmer et al., 2018 ; no percentage: ( Schmidt, Ralph & Buskirk, 2009 ); 92%: Matthíasdóttir & Arnalds, 2016 ; no percentage: Pagram et al., 2018 ; 51%: Park, 2017 ; 84%: Schmidt, Ralph & Williams & Wong, 2009 ). Two reasons provided for the preference is the increased speed and ease of editing responses ( Pagram et al., 2018 ), with one study finding two-thirds (67%) of students reported a positive experience in online examination environment ( Matthíasdóttir & Arnalds, 2016 ). Students believe online examinations allows a more authentic assessment experience ( Williams & Wong, 2009 ), with 78 percent of students reporting consistencies between the online environment and their future real-world environment ( Matthíasdóttir & Arnalds, 2016 ).

Students perceive the online examinations saves time (75.0% of students surveyed) and is more economical (87.5%) than paper examinations ( Attia, 2014 ). It provides greater flexibility for completing examinations ( Schmidt et al., 2009 ) with faster access to remote student papers (87.5%) and students trust the result of online over paper-based examinations (78.1%: Attia, 2014 ). The majority of students (59.4%: Attia, 2014 ; 55.5%: Pagram et al., 2018 ) perceive that the online examination environment makes it easier to cheat. More than half (56.25%) of students believe that a lack of information communication and technology (ICT) skill do not adversely affect performance in online examinations ( Attia, 2014 ). Nearly a quarter (23%) of students reported ( Abdel Karim & Shukur, 2016 ) the most preferred font face (type) was Arial, a font also recommended by Vision Australia (2014) in their guidelines for online and print inclusive design and legibility considerations. Nearly all (87%) students preferred black text color on a white background color (87%). With regards to onscreen time counters, a countdown counter was the most preferred option (42%) compared to a traditional analogue clock (30%) or an ascending counter (22%). Many systems allow students to set their preferred remaining time reminder or alert, including 15 min remaining (35% students preferred), 5 min remaining (26%), mid-examination (15%) or 30 min remaining (13%).

3.3. Student performance

Several studies in the sample referred to a lack of score variation between the results of examination across different administration methods. For example, student performance did not have significant difference in final examination scores across online and traditional examination modalities ( Gold & Mozes-Carmel, 2017 ). This is reinforced by a test of validity and reliability of computer-based and paper-based assessment that demonstrated no significant difference ( Oz & Ozturan, 2018 ), and equality of grades identified across the two modalities ( Stowell & Bennett, 2010 ).

When considering student perceptions, of the studies documented in our sample, there tended to be favorable ratings of online examinations. In a small sample of 34 postgraduate students, the respondents had positive perceptions towards online learning assessments (67.4%). The students also believed it contributed to improved learning and feedback (67.4%), and 77 percent had favorable attitudes towards online assessment ( Attia, 2014 ). In a pre-examination survey, students indicated they preferred to type than to write, felt more confident about the examination, and had limited issues with software and hardware ( Pagram, 2018 ). With the same sample in a post-examination survey, within the design and technology examination, students felt the software and hardware were simple to use, yet many students did not feel at ease from their use of an e-examination.

Rios and Liu (2017) compared proctored and non-proctored online examinations across several aspects, including test-taking behavior. Their study did not identify any difference in the test-taking behavior of students between the two environments. There was no significant difference between omitted items and not-reached items. Furthermore, with regards to rapid guessing, there was no significant difference. A negligible difference existed for students aged older than thirty-five years, yet gender was a nonsignificant factor.

3.4. Anxiety

Scholars have an increasing awareness of the role that test anxiety has in reducing student success in online learning environments ( Kolski & Weible, 2018 ). The manuscripts identified by the literature scan, identified inconsistencies of results for the effect that examination modalities have on student test anxiety. A study of 69 psychology undergraduates identified that students who typically experienced high anxiety in traditional test environments had lower anxiety levels when completing an online examination ( Stowell & Bennett, 2010 ). In a quasi-experimental study ( n  = 38 nursing students), when baseline anxiety is controlled, students in computer-based examinations had higher degrees of test anxiety.

In 34 postgraduate student interviews, only three opposed online assessment based on perceived lack of technical skill (e.g. typing; Attia, 2014 ). Around two-thirds of participants identified some form of fear-based on internet disconnection, electricity, slow typing, or family disturbances at home. A 37 participant Community College study used proximal indicators (e.g. lip licking and biting, furrowed eyebrows, and seat squirming) to assess the rate of test anxiety in webcam-based examination proctoring ( Kolski & Weible, 2018 ). Teacher strategies to reduce anxiety in their students include enabling students to consider, review, and acknowledge their anxieties ( Kolski & Weible, 2018 ). Responses such as students writing of their anxiety, or responding to multiple-choice questionnaire on test anxiety, reduced anxiety. Students in the test group and provided anxiety items or expressive writing exercises, performed better ( Kumar, 2014 ).

3.5. Cheating

Cheating was the most prevalent area among all the themes identified. Cheating in asynchronous, objective, and online assessments is argued by some to be at unconscionable levels ( Sullivan, 2016 ). In one survey, 73.6 percent of students felt it was easier to cheat on online examinations than regular examinations ( Aisyah et al., 2018 ). This is perhaps because students are monitored in paper and pencil examinations, compared to online examinations where greater control of variables is required to mitigate cheating. Some instructors have used randomized examination batteries to minimize cheating potential through peer-to-peer sharing ( Schmidt et al., 2009 ).

Scholars identify various methods for mitigating cheating. Identifying the test taker, preventing examination theft, unauthorized use of textbook/notes, preparing a set-up for online examination, unauthorized student access to a test bank, preventing the use of devices (e.g. phone, Bluetooth, and calculators), limiting access to other people during the examination, equitable access to equipment, identifying computer crashes, inconsistency of method for proctoring ( Hearn Moore et al., 2017 ). In another, the issue for solving cheating is social as well as technological. While technology is considered the current norm for reducing cheating, these tools have been mostly ineffective ( Sullivan, 2016 ). Access to multiple question banks through effective quiz design and delivery is a mechanism to reduce the propensity to cheat, by reducing the stakes through multiple delivery attempts ( Sullivan, 2016 ). Question and answer randomization, continuous question development, multiple examination versions, open book options, time stamps, and diversity in question formats, sequences, types, and frequency are used to manage the perception and potential for cheating. In the study with MBA students, perception of the ability to cheat seemed to be critical for the development of a safe online examination environment ( Sullivan, 2016 ).

Dawson (2016) in a review of bring-your-own-device examinations including:

• • Copying contents of USB to a hard drive to make a copy of the digital examination available to others,
• • Use of a virtual machine to maintain access to standard applications on their device,
• • USB keyboard hacks to allow easy access to other documents (e.g. personal notes),
• • Modifying software to maintain complete control of their own device, and
• • A cold boot attack to maintain a copy of the examination.

The research on cheating has focused mainly on technical challenges (e.g. hardware to support cheating), rather than ethical and social issues (e.g. behavioral development to curb future cheating behaviors). The latter has been researched in more depth in traditional assessment methods (e.g. Wright, 2015 ). In a study on Massive Open Online Courses (MOOCs), motivations for students to engage in optional learning stemmed from knowledge, work, convenience, and personal interest ( Shapiro et al., 2017 ). This provides possible opportunities for future research to consider behavioral elements for responding to cheating, rather than institutional punitive arrangements.

3.6. Staff perception

Schmidt et al. (2009) also examined the perceptions of academics with regards to online examination. Academics reported that their biggest concern with using online examinations is the potential for cheating. There was a perception that students may get assistance during an examination. The reliability of the technology is the second more critical concern of academic staff. This includes concerns about internet connectivity as well as computer or software issues. The third concern is related to ease of use, both for the academic and for students. Academics want a system that is easy and quick to create, manage and mark examinations, and students can use with proficient ICT skills ( Schmidt et al., 2009 ). Furthermore, staff reported in a different study that marking digital work was easier and preferred it over paper examinations because of the reduction in paper ( Pagram et al., 2018 ). They believe preference should be given to using university machines instead of the student using their computer, mainly due to issues around operating system compatibility and data loss.

3.7. Authentication and security

Authentication was recognized as a significant issue for examination. Some scholars indicate that the primary reason for requiring physical attendance to proctored examinations is to validate and authenticate the student taking the assessment ( Chao et al., 2012 ). Importantly, the validity of online proctored examination administration procedures is argued as lower than proctored on-campus examinations ( Rios & Liu, 2017 ). Most responses to online examinations use bring-your-own-device models where laptops are brought to traditional lecture theatres, use of software on personal devices in any location desired, or use of prescribed devices in a classroom setting. The primary goal of each is to balance the authentication of students and maintain the integrity and value of achieving learning outcomes.

In a review of current authentication options ( AbuMansoor, 2017 ), the use of fingerprint reading, streaming media, and follow-up identifications were used to authenticate small cohorts of students. Some learning management systems (LMS) have developed subsidiary products (e.g. Weaver within Moodle) to support authentication processes. Some biometric software uses different levels to authenticate keystrokes for motor controls, stylometry for linguistics, application behavior for semantics, capture to physical or behavioral samples, extraction of unique data, comparison of distance measures, and recording decision-making. Development of online examinations should be oriented towards the same theory of open book examinations.

A series of models are proposed in our literature sample. AbuMansoor (2017) propose to use a series of processes into place to develop examinations that minimize cheating (e.g. question batteries), deploying authentication techniques (e.g. keystrokes and fingerprints), and conduct posthoc assessments to search for cheating. The Aisyah et al. (2018) model identifies two perspectives to conceptualize authentication systems: examinee and admin. From the examinee perspective, points of authentication at the pre-, intra-, and post-examination periods. From the administrative perspective, accessing photographic authentication from pre- and intra-examination periods can be used to validate the examinee. The open book open web (OBOW: Mohanna & Patel, 2016 ) model uses the application of authentic assessment to place the learner in the role of a decision-maker and expert witness, with validation by avoiding any question that could have a generic answer.

The Smart Authenticated Fast Exams (SAFE: Chebrolu et al., 2017 ) model uses application focus (e.g. continuously tracking focus of examinee), logging (phone state, phone identification, and Wi-Fi status), visual password (a password that is visually presented but not easily communicated without photograph), Bluetooth neighborhood logging (to check for nearby devices), ID checks, digitally signed application, random device swap, and the avoidance of ‘bring your own device’ models. The online comprehensive examination (OCE) was used in a National Board Dental Examination to test knowledge in a home environment with 200 multiple choice questions, and the ability to take the test multiple times for formative knowledge development.

Some scholars recommend online synchronous assessments as an alternative to traditional proctored examinations while maintaining the ability to manually authenticate ( Chao et al., 2012 ). In these assessments: quizzes are designed to test factual knowledge, practice for procedural, essay for conceptual, and oral for metacognitive knowledge. A ‘cyber face-to-face’ element is required to enable the validation of students.

3.8. Interface design

The interface of a system will impact on whether a student perceives the environment to be an enabler or barrier for online examinations. Abdel Karim and Shukur (2016) summarized the potential interface design features that emerged from a systematic review of the literature on this topic, as shown in Table 2 . The incorporation of navigation tools has also been identified by students and staff as an essential design feature ( Rios & Liu, 2017 ), as is an auto-save functionality ( Pagram et al., 2018 ).

Potential interface design features ( Abdel Karim & Shukur, 2016 ).

3.9. Technology issues

None of the studies that included technological problems in its design reported any issues ( Böhmer et al., 2018 ; Matthíasdóttir & Arnalds, 2016 ; Schmidt et al., 2009 ). One study stated that 5 percent of students reported some problem ranging from a slow system through to the system not working well with the computer operating system, however, the authors stated no technical problems that resulted in the inability to complete the examination were reported ( Matthíasdóttir & Arnalds, 2016 ). In a separate study, students reported that they would prefer to use university technology to complete the examination due to distrust of the system working with their home computer or laptop operating system or the fear of losing data during the examination ( Pagram et al., 2018 ). While the study did not report any problems loading on desktop machines, some student laptops from their workplace had firewalls, and as such had to load the system from a USB.

4. Discussion

This systematic literature review sought to assess the current state of literature concerning online examinations and its equivalents. For most students, online learning environments created a system more supportive of their wellbeing, personal lives, and learning performance. Staff preferred online examinations for their workload implications and ease of completion, and basic evaluation of print-based examination logistics could identify some substantial ongoing cost savings. Not all staff and students preferred the idea of online test environments, yet studies that considered age and gender identified only negligible differences ( Rios & Liu, 2017 ).

While the literature on online examinations is growing, there is still a dearth of discussion at the pedagogical and governance levels. Our review and new familiarity with papers led us to point researchers in two principal directions: accreditation and authenticity. We acknowledge that there are many possible pathways to consider, with reference to the consistency of application, the validity and reliability of online examinations, and whether online examinations enable better measurement and greater student success. There are also opportunities to synthesize online examination literature with other innovative digital pedagogical devices. For example, immersive learning environments ( Herrington et al., 2007 ), mobile technologies ( Jahnke & Liebscher, 2020 ); social media ( Giannikas, 2020 ), and web 2.0 technologies ( Bennett et al., 2012 ). The literature examined acknowledges key elements of the underlying needs for online examinations from student, academic, and technical perspectives. This has included the need for online examinations need to accessible, need to be able to distinguish a true pass from a true fail, secure, minimize opportunities for cheating, accurately authenticates the student, reduce marking time, and designed to be agile in software or technological failure.

We turn attention now to areas of need in future research, and focus on accreditation and authenticity over these alternates given there is a real need for more research prior to synthesis of knowledge on the latter pathways.

4.1. The accreditation question

The influence of external accreditation bodies was named frequently and ominously among the sample group, but lacked clarity surrounding exact parameters and expectations. Rios (2017, p. 231) identified a specific measure was used “for accreditation purposes”. Hylton et al. (2016 , p. 54) specified that the US Department of Education requires “appropriate procedures or technology are implemented” to authentic distance students. Gehringer and Peddycord (2013) empirically found that online/open-web examinations provided more significant data for accreditation. Underlying university decisions to use face-to-face invigilated examination settings is to enable authentication of learning – a requirement of many governing bodies globally. The continual refinement of rules has enabled a degree of assurance that students are who they say they are.

Nevertheless, sophisticated networks have been established globally to support direct student cheating from completing quick assessments and calculators with secret search engine capability through to full completion of a course inclusive of attending on-campus invigilated examinations. The authentication process in invigilated examinations does not typically account for distance students who have a forged student identification card to enable a contract service to complete their examinations. Under the requirement assure authentication of learning, invigilated examinations will require revision to meet contemporary environments. The inclusion of a broader range of big data from keystroke patterns, linguistics analysis, and whole-of-student analytics over a student lifecycle is necessary to identify areas of risk from the institutional perspective. Where a student has a significantly different method of typing or sentence structure, it is necessary to review.

An experimental study on the detection of cheating in a psychology unit found teachers could detect cheating 62 percent of the time ( Dawson & Sutherland-Smith, 2017 ). Automated algorithms could be used to support the pre-identification of this process, given lecturers and professors are unlikely to be explicitly coding for cheating propensity when grading multiple hundreds of papers on the same topic. Future scholars should be considering the innate differences that exist among test-taking behaviors that could be codified to create pattern recognition software. Even in traditional invigilated examinations, the use of linguistics and handwriting evaluations could be used for cheating identification.

4.2. Authentic assessments and examinations

The literature identified in the sample discussed with limited depth the role of authentic assessment in examinations. The evolution of pedagogy and teaching principles (e.g. constructive alignment; Biggs, 1996 ) have paved the way for revised approaches to assessment and student learning. In the case of invigilated examinations, universities have been far slower to progress innovative solutions despite growing evidence that students prefer the flexibility and opportunities afforded by digitalizing exams. University commitments to the development of authentic assessment environments will require a radical revision of current examination practice to incorporate real-life learning processes and unstructured problem-solving ( Williams & Wong, 2009 ). While traditional examinations may be influenced by financial efficacy, accreditation, and authentication pressures, there are upward pressures from student demand, student success, and student wellbeing to create more authentic learning opportunities.

The online examination setting offers greater connectivity to the kinds of environments graduates will be expected to engage in on a regular basis. The development of time management skills to plan times to complete a fixed time examination is reflected in the business student's need to pitch and present at certain times of the day to corporate stakeholders, or a dentist maintaining a specific time allotment for the extraction of a tooth. The completion of a self-regulated task online with tangible performance outcomes is reflected in many roles from lawyer briefs on time-sensitive court cases to high school teacher completions of student reports at the end of a calendar year. Future practitioner implementation and evaluation should be focused on embedding authenticity into the examination setting, and future researchers should seek to understand better the parameters by which online examinations can create authentic learning experiences for students. In some cases, the inclusion of examinations may not be appropriate; and in these cases, they should be progressively extracted from the curriculum.

4.3. Where to next?

As institutions begin to provide higher learning flexibility to students with digital and blended offerings, there is scholarly need to consider the efficacy of the examination environment associated with these settings. Home computers and high-speed internet are becoming commonplace ( Rainie & Horrigan, 2005 ), recognizing that such an assumption has implications for student equity. As Warschauer (2007 , p. 41) puts it, “the future of learning is digital”. Our ability as educators will be in seeking to understand how we can create high impact learning opportunities while responding to an era of digitalization. Research considering digital fluency in students will be pivotal ( Crawford & Butler-Henderson, 2020 ). Important too, is the scholarly imperative to examine the implementation barriers and successes associated with online examinations in higher education institutions given the lack of clear cross-institutional case studies. There is also a symbiotic question that requires addressing by scholars in our field, beginning with understanding how online examinations can enable higher education, and likewise how higher education can shape and inform the implementation and delivery of online examinations.

4.4. Limitations

This study adopted a rigorous PRISMA method for preliminary identification of papers for inclusion, the MMAT protocol for identifying the quality of papers, and an inductive thematic analysis for analyzing papers included. These processes respond directly to limitations of subjectivity and assurance of breadth and depth of literature. However, the systematic literature review method limits the papers included by the search criteria used. While we opted for a broad set of terms, it is possible we missed papers that would typically have been identified in other manual and critical identification processes. The lack of research published provided a substantial opportunity to develop a systematic literature review to summarize the state of the evidence, but the availability of data limits each comment. A meta-analysis on quantitative research in this area of study would be complicated because of the lack of replication. Indeed, our ability to unpack which institutions currently use online examinations (and variants thereof) relied on scholars publishing on such implementations; many of which have not. The findings of this systematic literature review are also limited by the lack of replication in this infant field. The systematic literature review was, in our opinions, the most appropriate method to summarize the current state of literature despite the above limitations and provides a strong foundation for an evidence-based future of online examinations. We also acknowledge the deep connection that this research may have in relation to the contemporary COVID-19 climate in higher education, with many universities opting for online forms of examinations to support physically distanced education and emergency remote teaching. There were 138 publications on broad learning and teaching topics during the first half of 2020 ( Butler-Henderson et al., 2020 ). Future research may consider how this has changed or influenced the nature of rapid innovation for online examinations.

5. Conclusion

This systematic literature review considered the contemporary literature on online examinations and their equivalents. We discussed student, staff, and technological research as it was identified in our sample. The dominant focus of the literature is still oriented on preliminary evaluations of implementation. These include what processes changed at a technological level, and how students and staff rated their preferences. There were some early attempts to explore the effect of online examinations on student wellbeing and student performance, along with how the changes affect the ability for staff to achieve.

Higher education needs this succinct summary of the literature on online examinations to understand the barriers and how they can be overcome, encouraging greater uptake of online examinations in tertiary education. One of the largest barriers is perceptions of using online examinations. Once students have experienced online examinations, there is a preference for this format due to its ease of use. The literature reported student performance did not have significant difference in final examination scores across online and traditional examination modalities. Student anxiety decreased once they had used the online examination software. This information needs to be provided to students to change students’ perceptions and decrease anxiety when implementing an online examination system. Similarly, the information summarized in this paper needs to be provided to staff, such as the data related to cheating, reliability of the technology, ease of use, and reduction in time for establishing and marking examinations. When selecting a system, institutions should seek one that includes biometrics with a high level of precision, such as user authentication, and movement, sound, and keystroke monitoring (reporting deviations so the recording can be reviewed). These features reduce the need for online examinations to be invigilated. Other system features should include locking the system or browser, cloud-based technology so local updates are not required, and an interface design that makes using the online examination intuitive. Institutions should also consider how it will address technological failures and digital disparities, such as literacy and access to technology.

We recognize the need for substantially more evidence surrounding the post-implementation stages of online examinations. The current use of online examinations across disciplines, institutions, and countries needs to be examined to understand the successes and gaps. Beyond questions of ‘do students prefer online or on-campus exams’, serious questions of how student mental wellbeing, employability, and achievement of learning outcomes can be improved as a result of an online examination pedagogy is critical. In conjunction is the need to break down the facets and types of digitally enhanced examinations (e.g. online, e-examination, BYOD examinations, and similar) and compare each of these for their respective efficacy in enabling student success against institutional implications. While this paper was only able to capture the literature that does exist, we believe the next stage of literature needs to consider broader implications than immediate student perceptions toward the achievement of institutional strategic imperatives that may include student wellbeing, student success, student retention, financial viability, staff enrichment, and student employability.

Author statement

Both authors Kerryn Butler-Henderson and Joseph Crawford contributed to the design of this study, literature searches, data abstraction and cleaning, data analysis, and development of this manuscript. All contributions were equal.

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

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• Published: 27 March 2024

The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease

• Jordi Manuello   ORCID: orcid.org/0000-0002-9928-0924 1 , 2 ,
• Joosung Min   ORCID: orcid.org/0000-0002-5541-5014 3 ,
• Paul McCarthy 1 ,
• Fidel Alfaro-Almagro 1 ,
• Soojin Lee 1 , 4 ,
• Stephen Smith 1 ,
• Lloyd T. Elliott 3   na1 ,
• Anderson M. Winkler 5 , 6   na1 &
• Gwenaëlle Douaud   ORCID: orcid.org/0000-0003-1981-391X 1  

Nature Communications volume  15 , Article number:  2576 ( 2024 ) Cite this article

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• Risk factors

We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer’s disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide – a proxy for traffic-related air pollution – and alcohol intake frequency. The extent of these associations was uncovered by examining these modifiable risk factors in a single model to assess the unique contribution of each on the vulnerable brain network, above and beyond the dominating effects of age and sex. These results provide a comprehensive picture of the role played by genetic and modifiable risk factors on these fragile parts of the brain.

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Introduction

The development of preventative strategies based on modifying risk factors might prove to be a successful approach in ensuring healthy ageing. Factors particularly scrutinised in dementia and unhealthy ageing have included cerebrovascular factors such as high blood pressure, diabetes and obesity, but also lifestyle ones such as alcohol consumption, and protective factors such as exercise 1 . Assessing these modifiable risk factors together makes it possible to identify the unique contribution of each of these factors on the brain or on cognitive decline. A Lancet commission, updated in 2020 to include, e.g., pollution for its possible role in the incidence of dementia 2 , examined the relative impact of 12 modifiable risk factors for dementia, and showed that these 12 factors may account for 40% of the cases worldwide 3 . Conversely, genetic factors are non-modifiable in nature, but can inform us about the mechanisms underlying the phenotypes of interest. These mechanisms sometimes can be shared across these phenotypes. For instance, genetic overlap has been found for Alzheimer’s and Parkinson’s diseases at a locus in the MAPT region 4 . Likewise, one of the most pleiotropic variants, in the SLC39A8 / ZIP8 gene, shows genome-wide associations with both schizophrenia and fluid intelligence, amongst many other phenotypes 5 , 6 .

One way to objectively and robustly assess susceptibility for unhealthy ageing is to look non-invasively at brain imaging markers 7 . Using a data-driven approach on a lifespan cohort, we previously identified an ensemble of higher-order, ‘transmodal’ brain regions that degenerates earlier and faster than the rest of the brain 8 . The very same areas also develop relatively late during adolescence, thus supporting the ‘last in, first out’ (LIFO) hypothesis, which posits that the process of age-related brain decline mirrors developmental maturation. Importantly, this network of brain regions further demonstrated heightened vulnerability to schizophrenia and Alzheimer’s disease, two disorders that impact on brain structure during adolescence and ageing respectively. Accordingly, this LIFO network was strongly associated with cognitive traits whose impairment is specifically related to these two disorders, namely fluid intelligence and long-term memory 8 .

Here, our main objective was to assess both the genetic and modifiable risk factors’ contributions to the vulnerability of these most fragile parts of the brain. We conducted a genome-wide association study on a prospective cohort of nearly 40,000 participants of the UK Biobank study who had received brain imaging, and in total evaluated the association between the LIFO brain network and 161 modifiable risk factors, classified according to 15 broad categories: blood pressure, cholesterol, diabetes, weight, alcohol consumption, smoking, depressive mood, inflammation, pollution, hearing, sleep, socialisation, diet, physical activity and education.

The vulnerable LIFO brain network in UK Biobank

Similar to our previously observed results 8 , the loadings of the LIFO brain network, i.e., the normalised grey matter volume in the network after regressing out the effects of all the other brain maps (see Methods), demonstrated a strong quadratic association with age in the UK Biobank cohort of 39,676 participants ( R 2  = 0.30, P  < 2.23 × 10 −308 , Fig.  1 ). These higher-order regions thus show an accelerated decrease of grey matter volume compared with the rest of the brain. Furthermore, these areas define a network mainly involved in behavioural tasks related to execution, working memory, and attention (Fig.  1 , Supplementary Information ).

Top left, spatial map of the LIFO network (in red-yellow, thresholded at Z  > 4 for visualisation) used to extract the loadings from every scanned participant from UK Biobank ( n  = 39,676). Top right, these LIFO loadings (in arbitrary units) show a strong quadratic association with age in the UK Biobank cohort, i.e. grey matter volume decreases quadratically with older age in these specific regions ( R 2  = 0.30, P  < 2.23 × 10 −308 ; inset: residual scatterplot). Bottom, the vulnerable network appears to encompass areas mainly involved in execution, working memory, and attention (using the BrainMap taxonomy 60 , and with the LIFO brain network thresholded at both Z  = 4 and Z  = 10, see  Supplementary Information ).

Genetic influences over the vulnerable LIFO brain network

Using a minor allele frequency filter of 1% and a –log 10 (P) threshold of 7.5, we found, in the 39,676 participants, genome-wide associations between the LIFO brain network and seven genetic clusters whose top variants were all replicated (Table  1 /Supplementary Data  1 , Fig.  2 ).

Top row, Manhattan plot showing the 7 significant genetic clusters associated with the LIFO brain network (–log 10 ( P ) > 7.5). Second and third rows, regional association plots of the top variants for each of the 5 autosomal genetic clusters: rs6540873 on chromosome (Chr) 1 ( KCNK2 ), rs13107325 on Chr4 ( SLC39A8 ), rs2677109 on Chr6 ( RUNX2 ) (as a proxy in high LD R 2  = 0.86 with indel 6:45442860_TA_T), rs12146713 on Chr12 ( NUAK1 ), and rs2532395 on Chr17 ( MAPT , KANSL1 )(highest variant after tri-allelic rs2693333; see Supplementary Data  4 for a complete list of significant variants in this 5th MAPT genetic cluster). Bottom row, regional association plots of the top variants for the two genetic clusters in the pseudo-autosomal region PAR1 of the X chromosome: rs312238 ( XG , CD99 ) and rs2857316 ( XG )(UK Biobank has no genotyped variants on the 3’ side). Based on Human Genome build hg19. P -values are derived from a two-sided linear association test.

The first autosomal genetic cluster, on chromosome 1, included two variants (lead variant: rs6540873, β  = 0.06, P  = 1.71 × 10 −8 , and rs1452628, with posterior probabilities of inclusion in the causal variant set of 0.56 and 0.45, respectively) close to, and eQTL of, KCNK2 ( TREK1 ). This gene regulates immune-cell trafficking into the central nervous system, controls inflammation, and plays a major role in the neuroprotection against ischemia. Of relevance, these two loci are in particular related in UK Biobank participants with the amount of alcohol consumed, insulin levels, inflammation with interleukin-8 levels, as well as, crucially, with late-onset Alzheimer’s disease (Table  1 /Supplementary Data  1 ).

The second autosomal genetic cluster on chromosome 4 was made of 7 loci, with the lead variant rs13107325 in an exon of SLC39A8/ZIP8 ( β  = 0.14, P  = 2.82 × 10 −13 , posterior probability: 0.99). This locus is one of the most pleiotropic SNPs identified in GWAS, and is, amongst many other associations, related in UK Biobank with cholesterol, blood pressure, weight, inflammation with C-reactive proteins levels, diabetes with insuline-like growth factor 1 levels, alcohol intake, sleep duration, and cognitive performance/impairment, including prospective memory (Table 1 /Supplementary Data  1 ).

The third locus was an indel in chromosome 6 in an intron, and eQTL, of RUNX2 (rs35187443, β  = 0.06, P  = 9.03 × 10 −9 ), which plays a key role in differentiating osteoblasts, and has been very recently shown to limit neurogenesis and oligodendrogenesis in a cellular model of Alzheimer’s disease 9 .

The fourth locus was a SNP in chromosome 12, in an intron of NUAK1 (rs12146713, β  = −0.10, P  = 1.26 × 10 −9 ), and remarkably its top association in UK Biobank was with the contrast between schizophrenia and major depressive disorder 10 , and it was also associated with insulin-like growth factor 1 levels (Table 1 /Supplementary Data  1 ).

The final genetic autosomal genetic cluster was made of 3,906 variants in the MAPT region. Its lead non-triallelic variant, rs2532395 ( β  = −0.09, P  = 3.56 × 10 −15 ) was more specifically <10 kb from KANSL1 and an eQTL of KANSL1 , MAPT and other genes in brain tissues (Table 1 /Supplementary Data  1 , Supplementary Data 4 ). This locus was also associated in UK Biobank with tiredness and alcohol intake. MAPT is in 17q21.31, a chromosomal band involved with a common chromosome 17 inversion 11 . Adding chromosome 17 inversion status as a confounder reduced the significance of the association ( β  = −0.15, P  = 8.45 × 10 −3 ). Since the genotype for rs2532395 was also strongly correlated with chromosome 17 inversion in our dataset (Pearson correlation r  = 0.98, P  < 2 × 10 −16 ), this would suggest that the association between MAPT and the LIFO network is not independent from chromosome 17 inversion. As this extended genetic region is known for its pathological association with many neurodegenerative disorders including Alzheimer’s disease, we investigated whether the LIFO brain regions mediated the effect of the MAPT genetic cluster (using the lead bi-allelic variant rs2532395) on Alzheimer’s disease (see Methods). Despite small average causal mediated effect (ACME) sizes, we found a significant effect for both the dominant model (ACME β  = 1.16 × 10 −4 ; 95% CI = [5.19 × 10 −5 , 1.99 × 10 −4 ]; P  = 4 × 10 −5 ) and the recessive model (ACME β  = 1.55 × 10 −4 ; 95% CI = [3.96 × 10 −5 , 3.74 × 10 −4 ]; P  = 4 × 10 −5 ; full output of the mediation package on the dominant and recessive models in  Supplementary Information ).

The two last genetic clusters of 8 and 9 variants respectively were found on the X chromosome, notably in a pseudo-autosomal region (PAR1), which is interestingly hit at a higher rate than the rest of the genome ( P  = 1.56 × 10 −5 , see  Supplementary Information ). The top variants for these clusters were related to two homologous genes coding for the two antigens of the XG blood group: rs312238 ( β  = −0.05, P  = 1.77 × 10 −10 ) ~ 10 kb from, and an eQTL of, CD99/MIC2 , and rs2857316 ( β  = −0.08, P  = 2.27 × 10 −29 ) in an intron and eQTL of XG  (Table 1 /Supplementary Data  1 ). Since chromosome X has hardly been explored, we carried out our own association analyses between these two top variants and non-imaging variables in UK Biobank. Intriguingly, the first of these two PAR1 loci, rs312238, was found to be significantly associated in the genotyped participants who had not been scanned (out-of-sample analysis in n  = 374,230 UK Biobank participants) with nitrogen dioxide air pollution, our ‘best’ MRF for pollution (see below), and many other environmental, socioeconomic, and early life factors (such as urban or rural setting, distance from the coast, place of birth, number of siblings, breastfed as a baby, maternal smoking around birth), as well as health outcomes (Supplementary Data  2 ). In particular, amongst the more easily interpretable findings of the most associated variables with rs312238, the T allele of this locus was associated with two increased measures of deprivation and/or disability (worse socioeconomic status), the ‘Townsend deprivation index’ and the ‘Health score’, but also with ‘Nitrogen dioxide air pollution’, ‘Maternal smoking around birth’, as well as ‘Number of full brothers’ and ‘Number of full sisters’, thus showing consistent signs of association between this variant and these phenotypes.

We found that the heritability of the LIFO network was significant, with h 2  = 0.15 (se = 0.01). The genetic co-heritability between the LIFO network and Alzheimer’s disease or schizophrenia was not statistically significant (coefficient of co-heritability = −0.12, se = 0.10; P  = 0.23; coefficient of co-heritability = −0.16, se = 0.04, P  = 0.07, respectively).

Modifiable risk factors’ associations with the vulnerable LIFO brain network

Including the modifiable risk factors (MRFs) in a single general linear model allows us to assess the unique contribution of each factor on the LIFO brain network. Not all UK Biobank participants have data available for all of the MRF variables however. An analysis limited to those with complete data for all MRFs would be biased, and based on a relatively small, low-powered sample. We addressed this issue via a two-stage analysis in which: (i) we first identified which variable within each of the 15 MRF categories best represented associations of that category with the LIFO brain network loadings (based on two criteria: significance and <5% missing values), (ii) we investigated the unique contribution of that MRF category, over and above all other categories and the dominating effects of age and sex, to the LIFO loadings.

From the first stage of our analysis, 12 of the 15 categories of MRFs had at least one ‘best’ MRF, i.e., with a significant effect on the LIFO brain network and enough non-missing values across all scanned participants to be investigated further (Table  2 /Supplementary Data  3 ). The contribution of the MRFs on the vulnerable brain network differed vastly depending on whether confounding effects of age, sex and head size were taken into account. The effect size and significance of some MRFs diminished because of some clear collinearity with the confounders. For instance, for the category of blood pressure, the most significant MRF was first “systolic blood pressure, automatic (second) reading” ( r  = −0.20, P  < 2.23 × 10 −308 ), but after regressing out the confounders, the ‘best’ MRF for this category was “medication for blood pressure” ( r  = −0.05, P  = 7.55 × 10 −22 ). Conversely, regressing out the effects of age served to unmask the significant deleterious effects of pollution on the vulnerable brain regions, such as nitrogen dioxide air pollution or particulate matter air pollution (Table  2 /Supplementary Data  3 ).

When considered together in a single model in the second stage of the analysis, 3 best MRFs had an effect on the LIFO brain network that remained significant beyond the dominating effects of age and sex, and of the 9 other best MRFs: diabetes (“diabetes diagnosed by doctor”, r  = −0.05, P  = 1.13 × 10 −24 ), pollution (“nitrogen dioxide air pollution in 2005”, r  = −0.05, P  = 5.39 × 10 −20 ) and alcohol (“alcohol intake frequency”, r  = −0.04, P  = 3.81 × 10 −17 ) (Table  3 ). No MRFs showed any bias in their sub-sampling distribution, i.e., any significant difference between the original sample and the reduced sample of 35,527 participants who had values for all 18 variables considered (the 12 best MRFs and 6 confounders: age, sex, age 2 , age × sex, age 2  × sex, head size; Supplementary Information ). In total, the 12 best MRFs explained 1.5% of the effect on the vulnerable brain network ( F 12;35509  = 43.5).

While 6 out of the 7 genetic clusters associated with the LIFO network were correlated with many variables related to each of the 15 MRF categories, including diabetes, alcohol consumption and traffic pollution (Supplementary Data  1 ), we also found some genetic overlap between the very specific best MRF of “alcohol intake frequency” and the LIFO network in the pleiotropic rs13107325 variant (cluster 2), as well as rs17690703, part of the large genetic cluster 5 in MAPT (Supplementary Data  4 ). No genetic overlap was found for the precise “nitrogen dioxide air pollution in 2005” or “diabetes diagnosed by doctor”, nor for approximate variables.

This study reveals, in a cohort of nearly 40,000 UK Biobank participants, the genetic and modifiable risk factors’ associations with brain regions in a ‘last in, first out’ (LIFO) network that show earlier and accelerated ageing and are particularly vulnerable to disease processes such as that of Alzheimer’s disease 8 . Seven genetic clusters, two of which in the pseudo-autosomal region of the sex chromosomes coding for two antigens of the XG blood system, were found significantly associated and replicated genome-wide. In addition, after accounting for age and sex effects, diabetes, traffic-related pollution and alcohol were the most deleterious modifiable risk factors (MRFs) on these particularly vulnerable brain regions.

Three lead variants for our significant genetic clusters have been previously associated with ageing-related brain imaging measures in recent studies: one, in cluster 1, an eQTL of KCNK2 ( TREK1 ) 12 , 13 , whose increase in expression mediates neuroprotection during ischemia 14 , the ubiquitous rs13107325 (cluster 2), and one, in cluster 4, in an intron of NUAK1 ( ARK5 ) 15 , 16 , 17 , which has been associated with tau pathology 18 (Table  1 /Supplementary Data  1 ). On the other hand, of the seven genetic clusters, three were entirely novel (clusters 3, 6 and 7), and not found in other brain imaging studies, including our most recent work that expanded on our previous GWAS of all of the brain IDPs available in UK Biobank 19 by including more participants—in fact, the same number of participants as analysed in this present work—and, crucially, by also including the X chromosome 20 (Table  1 /Supplementary Data  1 ). This suggests that, beyond the genetic hits that were meaningfully associated with the LIFO brain network and an array of relevant risk factors, lifestyle variables and brain disorders, and found in a few other imaging GWAS, some of the genetic underpinnings of the LIFO network are intrinsically specific to it and to no other pre-existing imaging phenotype.

All five autosomal genetic clusters identified through the GWAS of the LIFO phenotype had relevant associations with risk factors for dementia (Results; Supplementary Data  1 ), including precisely two of the best MRFs (for clusters 2 and 5), and three of them directly related in UK Biobank to the two diseases showing a pattern of brain abnormalities following the LIFO network: schizophrenia (clusters 2 and 4) and Alzheimer’s disease (cluster 1) (Supplementary Data  1 ). In particular, cluster 2 has its lead variant rs13107325 in an exon of one of the most pleiotropic genes ZIP8 , which codes for a zinc and metal transporter. Considering the vulnerability of the LIFO brain network to adolescent-onset schizophrenia and its significant association with fluid intelligence that we previously demonstrated 8 , it is notable that this variant has been associated genome-wide with schizophrenia 6 , as well as intelligence, educational attainment and mathematics ability 5 , 21 . In line with the LIFO brain network being both prone to accelerated ageing and susceptible to Alzheimer’s disease, this genetic locus has also been associated genome-wide with well-known risk factors for dementia. These comprise alcohol—including the exact same variable of “alcohol intake frequency” as identified as one of the best MRFs—cholesterol, weight, sleep—including “sleep duration”—and blood pressure 22 , 23 , 24 , 25 , 26 , all of which significantly contribute to modulating the LIFO brain network when considered separately (Table  2 /Supplementary Data  3 ). Of relevance, this genetic locus is also associated to an increased risk of cardiovascular death 27 . Cluster 5, a large genetic cluster in the MAPT region (Microtubule-Associated Protein Tau), comprised in total 3906 significant variants (Supplementary Data  4 ). This genetic region plays a role in various neurodegenerative disorders related to mutations of the protein tau, such as frontotemporal dementia 28 and progressive supranuclear palsy 29 , but also, of particular pertinence to the LIFO brain network, Alzheimer’s and Parkinson’s disease, with a genetic overlap between these two diseases in a locus included in our significant cluster 5 (rs393152, β  = −0.09, P  = 6.35 × 10 −14 ) 4 . Despite the relatively low number of people with diagnosed Alzheimer’s disease in the genetic discovery cohort, we were able to establish—albeit with small effect sizes—a significant mediation role for the LIFO brain regions between the lead bi-allelic variant for cluster 5 and this Alzheimer’s diagnosis, suggesting once more the importance played by these vulnerable brain areas in unhealthy ageing.

Finally, of the seven clusters, two were located in the pseudo-autosomal region (PAR1) of the sex chromosomes corresponding to the genes XG and CD99 , coding for the two antigens of the XG blood group. This blood group system has been largely neglected, its main contribution related to the mapping of the X chromosome itself, and its clinical role remains elusive 30 . In order to investigate further the possible role of these two variants of the XG blood group, we examined out-of-sample their associations with thousands of non-imaging phenotypes. This analysis revealed that the first of these two loci was significantly and consistently associated with early life factors, environmental factors and health outcomes, including particulate matter and nitrogen dioxide air pollution, the second most deleterious MRF to the LIFO brain network (Supplementary Data  2 ). Whether these associations are due to stratification or genotyping artefacts, or to the fact that this specific variant, which is inherited from a parent, has a parental impact that modulates the effect of early life environment of the UK Biobank participants, the so-called “nature of nurture”, will need further investigation 31 .

Intriguingly, an analysis revealed that the genes involved in the loci associated with the LIFO network (Table  1 /Supplementary Data  1 ) are enriched for the gene ontology terms of leucocyte extravasation, namely “positive regulation of neutrophil extravasation” ( P  = 4.75 × 10 −6 ) and “T cell extravasation” ( P  = 4.75 × 10 −6 ). This result held when removing the genes included in the MAPT extended region (with P  = 2.54 × 10 −6 and P  = 2.54 × 10 −6 , respectively). Leucocyte extravasation facilitates the immune and inflammatory response, and there has been renewed focus on the fact that a breakdown of the blood-brain barrier together with leukocyte extravasation might contribute to both Alzheimer’s disease and schizophrenia 32 , 33 . In line with the enrichment findings, 4 out of the 7 genetic clusters associated with the LIFO network are correlated in UK Biobank blood assays with percentage or count of immune cells (neutrophil, lymphocyte, platelet, monocyte, etc.; Supplementary Data  1 ).

Regarding MRFs’ effects on the LIFO brain network, diabetes and alcohol consumption have been consistently shown to be associated with both cerebral and cognitive decline 34 , 35 . On the other hand, pollution—and notably that of nitrogen oxides—has emerged more recently as a potential MRF for dementia 2 , 36 . In particular, the increase of dementia risk due to nitrogen oxide pollution, a proxy for traffic-related air pollution, seems to be enhanced by cardiovascular disease 37 . In this study, we found that nitrogen dioxide pollution has one of the most deleterious effects onto the fragile LIFO brain regions. This effect could only be unmasked by regressing out the effects of age and sex, as traffic-related air pollution is modestly inversely-correlated with age (Supplementary Data  5 ). It is also worth noting that including age and sex as confounding variables in the first stage of our analysis reduced considerably the contribution of what had appeared at first—before regression—as the most harmful risk factors: blood pressure, cholesterol and weight (Table  2 /Supplementary Data  3 ). Furthermore, the benefit of examining these MRFs in a single model in the second stage of our analysis is that we can assess the unique contribution of each of these factors on the LIFO brain network; in doing so, blood pressure, cholesterol and weight were no longer significant (Table  3 ).

One defining characteristic of the LIFO brain network is how much age explains its variance. Indeed, in the dataset covering most of the lifespan that was initially used to identify the LIFO and spatially define it 8 , age explained 50%. In the UK Biobank imaging project, where imaged participants are over 45 years old, age explained 30% (Fig.  1 ). It is thus perhaps unsurprising that, while the explained variance by each of the MRFs varies widely (Table  2 /Supplementary Data  3 ), it reduces notably once the effect of age and other confounders has been regressed out (without confounders included in the model: maximum 8.4%; with confounders: maximum 0.5%). Combined, the 12 best MRFs explained a significant 1.5% of the effect on the vulnerable brain network after regressing out age, head size and sex effects. Regarding the genetic hits, we found a significant heritability with h 2  = 0.15, in keeping with our results for structural brain phenotypes (except for subcortical and global brain volumes, which demonstrate higher heritability 19 ).

The uniqueness of this study relies on the fact that we combined the strengths of two different cohorts: the first, which revealed the LIFO grey matter network, is lifespan, demonstrating the mirroring of developmental and ageing processes in the LIFO brain areas, something that could never be achieved with UK Biobank because of its limited age range. Of note, for this initial work with the lifespan cohort 8 , we not only included grey matter partial volume images, as done in this current study, but also Freesurfer information of cortical thickness and surface area. The LIFO network showed no contribution from Freesurfer cortical thickness or area. This might hint at processes that only partial volume maps are able to detect due to the LIFO network’s specific localisation, including in the cerebellum and subcortical structures, which are not included in the area and thickness surface methods from Freesurfer.

Limitations of our study pertain to the nature of the data itself and the way each variable is encoded in the UK Biobank (binary, ordinal, categorical, continuous), the number of missing values, what is offered as variables for each modifiable risk factor category (e.g. we chose not to create any compound variables, such as the ratio of cholesterol levels or systolic and diastolic blood pressures), and the curation of each of these variables. Some of the factors might be proxies for another category, but including the ‘best’ ones in a single model alleviate these issues to some extent. Another limitation is the assumption in our models that each risk factor has a linear, additive effect on the vulnerable LIFO brain network. It is also important to note that cross-sectional and longitudinal patterns of brain ageing can differ, as has been shown for instance for adult span trajectories of episodic and semantic memory, especially in younger adults 38 . A recent study has also demonstrated a specific ‘brain age’ imaging measure to be more related to early life influences on brain structure than within-person rates of change in the ageing brain 39 . Further work will be needed to establish how the LIFO network data changes in terms of within-person trends, for instance by investigating the growing UK Biobank longitudinal imaging database. While we took care of assessing the replicability of our genetic results by randomly assigning a third of our dataset for such purposes (all our significant genetic hits were replicated), this was performed within the UK Biobank cohort that exhibits well-documented biases, being well-educated, less deprived, and healthier than the general population, especially for its imaging arm 40 . Independent replications will be needed to confirm the existence of the LIFO-associated genetic loci.

In conclusion, our study reveals the modifiable and non-modifiable factors associated with some of the most fragile parts of the brain particularly vulnerable to ageing and disease process. It shows that, above and beyond the effect of age and sex, the most deleterious modifiable risk factors to this brain network of higher-order regions are diabetes, pollution and alcohol intake. Genetic factors are related to immune and inflammatory response, tau pathology, metal transport and vascular dysfunction, as well as to the XG blood group system from the pseudo-autosomal region of the sex chromosomes, and meaningfully associated with relevant modifiable risk factors for dementia. The unprecedented genome-wide discovery of the two variants on the sex chromosomes in this relatively unexplored blood group opens the way for further investigation into its possible role in underlying unhealthy ageing.

Supplementary Information is available for this paper.

For the present work the imaging cohort of UK Biobank was used and we included 39,676 subjects who had been scanned and for whom the brain scans had been preprocessed at the time of the final set of analyses (M/F 47–53%; 44–82 years, mean age 64 ± 7 years; as of October 2020) 41 , 42 . Structural T1-weighted scans for each participant were processed using the FSL-VBM automated tool to extract their grey matter map 43 , 44 . The ‘last in, first out’ (LIFO) network of mainly higher-order brain regions was initially identified by performing a linked independent component analysis on the grey matter images of another, lifespan observational cohort of 484 subjects 8 , 45 , 46 . This map of interest, along with the other 69 generated by the analysis, was first realigned to the UK Biobank ‘standard’ space defined by the grey matter average across the first 15,000 participants, then regressed into the UK Biobank participants’ grey matter data, to extract weighted average values of grey matter normalised volume inside each of the z-maps, using the z-score as weighting factor. This made it possible to assess the unique contribution of this specific LIFO map, above and beyond all the rest of the brain represented in the other 69 maps. At the end of this process, we obtained a single imaging measure for each of the 39,676 participants, i.e. a ‘loading’ corresponding to their amount of grey matter normalised volume in the LIFO brain network.

Human participants: UK Biobank has approval from the North West Multi-Centre Research Ethics Committee (MREC) to obtain and disseminate data and samples from the participants ( http://www.ukbiobank.ac.uk/ethics/ ), and these ethical regulations cover the work in this study. Written informed consent was obtained from all of the participants.

Modifiable risk factors selection

The following 15 categories of modifiable risk factors (MRFs) for dementia were investigated based on previous literature: blood pressure, diabetes, cholesterol, weight, alcohol, smoking, depression, hearing, inflammation, pollution, sleep, exercise, diet/supplementation, socialisation, and education. These included well-documented cerebrovascular risk factors, and in particular included all of the 12 modifiable risk factors considered in the updated Lancet commission on dementia, with the sole exception of traumatic brain injury 3 . For each category, several MRF variables from UK Biobank were very minimally pre-processed ( Supplementary Information ). In total, 161 MRF variables were obtained. To optimise the interpretability of the results, and to be able to relate them to previous findings, we did not carry out any data reduction, which would have prevented us from identifying exactly which variable—and subsequently, which genetic component for this specific variable—contribute to the effect. For these same reasons, we did not create any compound variable.

Statistical analyses

Genome-wide association study.

We followed the same protocol we had developed for the first genome-wide association study (GWAS) with imaging carried out on UK Biobank 19 . Briefly, we examined imputed UK Biobank genotype data 47 , and restricted the analysis to samples that were unrelated (thereby setting aside only ~450 participants), without aneuploidy and with recent UK ancestry. To account for population stratification, 40 genetic principal components were used in the genetic association tests as is recommended for UK Biobank genetic studies 19 , 20 , 47 . We excluded genetic variants with minor allele frequency <0.01 or INFO score <0.03 or Hardy-Weinberg equilibrium –log 10 ( P ) > 7. We then randomly split the samples into a discovery set with 2/3 of the samples ( n  = 22,128) and a replication set with 1/3 of the samples ( n  = 11,083). We also examined the X chromosome with the same filters, additionally excluding participants with sex chromosome aneuploidy: 12 in non-pseudoautosomal region (PAR) and 9 in PAR for the discovery set, 3 in non-PAR and 6 in PAR for the replication set. Variants were considered significant at –log 10 ( P ) > 7.5, and replicated at P  < 0.05.

Modifiable risk factor study

In the first stage, the general linear model was used to investigate, separately, the association between each of these 161 MRFs and the LIFO network loadings in all the scanned UK Biobank participants ( n  = 39,676). We ran each model twice: once as is, and once adding 6 confounders: age, age 2 , sex, age × sex, age 2 × sex, and head size, to estimate the contribution of these MRFs on the LIFO network above and beyond the dominating effects of age and sex. Sex was based on the population characteristics entry of UK Biobank. This is a mixture of the sex the NHS had recorded for the participant at recruitment, and updated self-reported sex. For the GWAS, both sex and genetic sex were used (the sample was excluded in case of a mismatch). In total, 32 variables tailored to structural imaging had been considered as possible confounders, and we retained those with the strongest association ( R 2  ≥ 0.01; see  Supplementary Information ). Socioeconomic status via the Townsend deprivation index was also considered as a possible confounding variable but explained little variance ( R 2  < 0.001) and thus was not included as a confounder.

MRFs were not considered further if they were not significant—not surviving Bonferroni-correction, i.e., P  > 1.55 × 10 −4 —and if more than 5% of the subjects had their MRF values missing. For each category, a single ‘best’ MRF was then selected as the variable with the highest R 2 among those remaining, after regressing out the confounding effects of age and sex.

In the second stage, all these best MRFs were then included in a single general linear model, together with the same 6 confounders used in the first stage, to assess the unique contribution of each factor on the LIFO brain network loadings. A prerequisite to carry out this single general linear model analysis was to only include participants who would have values for all best MRFs and confounders. This explains the additional criterion of only including MRFs that had no more than 5% of values missing, to ensure that the final sample of participants who had values for all these best and confounding factors would not be biased compared with the original sample—something we formally tested (see  Supplementary Information )—especially as data are not missing at random in UK Biobank, and exhibit some genetic structure 48 . The sample was therefore reduced to a total of 35,527 participants for this second stage analysis (M/F 17,290–18,237; 45–82 years, mean 64 ± 7 years). The effect of these best MRFs taken altogether was considered significant with a very conservative Bonferroni correction for multiple comparisons across all combinations of every possible MRF from each of the initial 15 MRF categories ( P  < 4.62 × 10 −17 , see  Supplementary Information for more details). In addition, both full and partial correlations were computed for the same set of best MRFs and confounders, in order to assess possible relationships between variables.

Post hoc genetic analyses

Chromosome 17 inversion.

We investigated chromosome 17 inversion status of the participants in the discovery cohort by considering their genotype on 32 variants that tag chromosome 17 inversion according to Steinberg et al. 11 . Of these 32 variants, 24 were present in our genetic data. We labelled the participants homozygous inverted, heterozygous, or homozygous direct (not inverted) when all 24 of these alleles indicated the same zygosity. This yielded an unambiguous inversion status for 21,969 participants (99% of the discovery cohort). To examine if the association between the non-triallelic lead variant of the MAPT genetic cluster (rs2532395, Table  1 /Supplementary Data  1 ) and the LIFO network was independent from this common inversion, we determined inversion/direct status of the discovery cohort and: 1. repeated the association test between rs2532395 and the LIFO phenotype, with chromosome 17 inversion status added as a confounder; and 2. correlated the genotype for rs2532395 with chromosome 17 inversion.

Causality within each genetic cluster

We used CAVIAR (Causal Variants Identification in Associated Regions 49 ) to assess causality of variants that passed the genome-wide significance threshold in each of the genetic clusters we report. CAVIAR uses a Bayesian model and the local linkage disequilibrium structure to assign posterior probabilities of causality to each variant in a region, given summary statistics for an association. We did not perform CAVIAR analysis on the genetic cluster on chromosome 17, as its non-triallelic lead variant (rs2532395) was strongly correlated with chromosome 17 inversion, and the LD matrix was large and low rank. We excluded the X chromosome loci from this analysis due to the difficulty in assessing LD in this chromosome.

Enrichment analysis

Based on the genes listed in the ‘Genes’ column of Table  1 /Supplementary Data  1 , we performed an enrichment analysis for the genes associated with the LIFO brain network using PANTHER 50 . PANTHER determines whether a gene function is overrepresented in a set of genes, according to the gene ontology consortium 51 , 52 .

Mediation analysis between MAPT top variant and Alzheimer’s disease, via the LIFO brain network

As the gene MAPT is associated with Alzheimer’s disease, and as we found a significant association between MAPT and the LIFO brain network, we examined to what extent the effect of MAPT is mediated by the LIFO brain regions. We conducted a mediation analysis using the counterfactual framework in which the average indirect effect of the treatment on the outcome through the mediator is nonparametrically identified (version 4.5.0 of the R package ‘mediation' 53 ). This is a general approach that encompasses the classical linear structural equation modelling framework for causal mediation, allowing both linear and non-linear relationships. In this analysis, the genotype for the lead bi-allelic variant of the MAPT association was used as the treatment, the LIFO loadings as the mediator, and Alzheimer’s disease diagnosis as the outcome.

From the ~43 K UK Biobank participants who had been scanned, we searched for those who had been diagnosed with Alzheimer’s disease specifically, regardless of whether this diagnosis occurred before, or after their brain scans. Based on hospital inpatient records (ICD10: F000, F001, F002, F009, G300, G301, G308, and G309 and ICD9: 3310) and primary care (GP) data (Eu00., Eu000, Eu001, Eu002, Eu00z, F110., F1100, F1101, Fyu30, X002x, X002y, X002z, X0030, X0031, X0032, X0033, XaIKB, XaIKC, and XE17j), we identified 65 such cases— UK Biobank being healthier than the general population, and those scanned showing an even stronger healthy bias—of which 34 were included in the discovery set after QC.

We considered two conditions for the effect of the treatment on the outcome. First, a dominant condition in which the minor allele is assumed to be dominant and for which at least one copy of the minor allele is considered treated. Second, a recessive condition in which the minor allele is assumed to be recessive. We considered that either condition was nominally significant if the confidence interval of the average causal mediated effect did not intersect zero, and had an associated P  < 0.05 ÷ 2 (correcting for the two conditions). We assessed confidence intervals and P -values using 50,000 bootstrapped samples.

Associations between the LIFO brain network’s genetic hits and the MRFs

First, we reported in Table  1 / Supplementary Data  1 the significant associations between the LIFO genetic hits and UK Biobank variables related to the 15 categories listed for the MRFs. For this, we used the Open Targets Genetics website, which reports the GWAS carried out in UK Biobank ( https://genetics .opentargets.org/ ). Second, we assessed whether there was any genetic overlap between the known genetic components of the 3 best MRFs and the LIFO phenotype. Again, we used the Open Targets Genetics website outputs for these 3 very specific UK Biobank variables, and compared the significant hits for these 3 best MRFs within ±250 kbp of, or in high LD (>0.8) with, our own LIFO variants. If reported hits were limited, we also searched online for GWAS done on similar variables. Finally, we also included the list of significant hits for diabetes 54 , which focused on a potential genetic overlap between diabetes and Alzheimer’s disease.

Post hoc association for the sex chromosomes variants

The allele counts of each participant for two specific significant variants of the sex chromosomes not—or hardly—available in open databases such as https://genetics.opentargets.org/ 55 were further associated out-of-sample with all non-imaging phenotypes of UK Biobank ( n  = 16,924). This analysis was carried out in the entire genotyped, quality-controlled sample where participants who had been scanned were removed (final sample: 374,230 participants), taking into account the population structure (40 genetic principal components), as well as the confounding effects of age, sex, age x sex, age 2 and age 2 x sex. Results were corrected for multiple comparisons across all non-imaging phenotypes and the two variants.

Heritability

We examined the heritability of the LIFO phenotype, and the coheritability between the LIFO network and Alzheimer’s disease or schizophrenia using LDSC 56 . This method uses regression on summary statistics to determine narrow sense heritability h 2 of a trait, or the shared genetic architecture between two traits. LDSC corrects for bias LD structure using LD calculated from a reference panel (we used LD from the Thousand Genomes Project Phase 1 57 ). We obtained summary statistics for a meta-analysis of Alzheimer’s disease involving 71,880 cases and 383,378 controls 58 . The number of genetic variants in the intersection between the summary statistics was 1,122,435. For schizophrenia, the summary statistics were obtained from a meta-analysis involving 53,386 cases and 77,258 controls 59 . A total of 1,171,319 genetic variants were in the intersection with the summary statistics for LIFO. For both Alzheimer’s and schizophrenia, the X chromosome was not included in the heritability calculation, as it was excluded from the meta-analysis that we sourced the summary statistics from.

Reproducibility

No data was excluded for the MRF analyses. For the genetic analyses, these were restricted to samples that were unrelated, without aneuploidy and with recent UK ancestry (see above).

No statistical method was used to predetermine sample size. The experiments were not randomised. The Investigators were not blinded to allocation during experiments and outcome assessment.

Reporting summary

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

Data availability

All the FLICA decomposition maps − including the LIFO grey matter network − in UK Biobank standard space, the UK Biobank grey matter template, scripts, and the LIFO loadings for all of the participants are freely available on a dedicated webpage: open.win.ox.ac.uk/pages/douaud/ukb-lifo-flica/ .

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Acknowledgements

We are grateful to Profs Christian K. Tamnes, Lars T. Westlye, Kristine B. Walhovd and Anders M. Fjell, and Dr Andreas Engvig for providing the lifespan cohort which was used to initially derive the original ‘last in, first out’ brain network map, and to Prof Augustine Kong for helpful discussion on the associations between the PAR hit and early life and environmental factors. G.D. was supported by a UK MRC Career Development Fellowship (MR/K006673/1) and a Wellcome Collaborative Award (215573/Z/19/Z). S.S. was supported by Wellcome (203139/Z/16/Z; 215573/Z/19/Z). L.E. was funded by NSERC grants (RGPIN/05484-2019; DGECR/00118-2019) and a Michael Smith Health Research BC Scholar Award. A.M.W. received support through the NIH Intramural Research Program (ZIA-MH002781; ZIA-MH002782). This research was funded in whole, or in part, by the Wellcome Trust (215573/Z/19/Z; 203139/Z/16/Z; 203139/A/16/Z). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This research was also supported by the NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z and 203139/A/16/Z).

Author information

These authors contributed equally: Lloyd T. Elliott, Anderson M. Winkler.

Authors and Affiliations

FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK

Jordi Manuello, Paul McCarthy, Fidel Alfaro-Almagro, Soojin Lee, Stephen Smith & Gwenaëlle Douaud

FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy

Jordi Manuello

Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada

Joosung Min & Lloyd T. Elliott

Pacific Parkinson’s Research Centre, The University of British Columbia, Vancouver, BC, Canada

National Institutes of Mental Health, National Institutes of Health, Bethesda, MD, USA

Anderson M. Winkler

Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, TX, USA

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G.D. conceived and supervised the work, and carried out some of the genetic and modifiable risk factors analyses. J.Ma. carried out most of the genetic and modifiable risk factors analyses. J.Mi., S.L., A.M.W., and L.T.E. carried out additional genetics analyses. G.D., P. McC., F.A.-A., S.S., and L.T.E. created/extracted the imaging and genetics data, and organised the non-imaging data and confound variables. L.T.E. co-supervised the genetic analyses. A.M.W. co-supervised the modifiable risk factor analyses. G.D. interpreted the results and wrote the paper. J.Ma., S.S., L.T.E., and A.M.W. revised the paper.

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Correspondence to Gwenaëlle Douaud .

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Manuello, J., Min, J., McCarthy, P. et al. The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease. Nat Commun 15 , 2576 (2024). https://doi.org/10.1038/s41467-024-46344-2

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DOI : https://doi.org/10.1038/s41467-024-46344-2

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