Introduction

Mobile advertising interacts with its audience via a mobile device, either through a short message, a mobile web interface or an in-app display (Park and Park 2020). It is expected that the revenue from these types of advertising will continue to rise in the future, particularly in the in-app one (Dogtiev 2018). According to Ethan Cramer-Flood 2023, the total mobile in-app ad spending was nearly $77 billion, nearly ten times the amount spent on mobile web ads and 89% of smartphone users’ media time is spent on apps and just 11% on mobile websites. It accordingly accounts for 57% of all online advertisements worldwide (IAB 2022). Mobile in-app advertising appears to already become the most popular marketing channel for businesses.

Advertisers now have several options to improve the effectiveness of their advertising campaigns by using interactive and personalized targeting techniques with the help of ad networks such as Google Ads, Facebook Audience Network and Twitter MoPub, in addition to the traditional use of ad designs controlled by the advertiser (Kumar 2016; Ullah and Binbusayyis 2022). The Mobile Advertising Effectiveness Framework (MAEF) proposed by Grewal et al. (2016) groups factors that can impact the outcome of mobile advertising into three components of context, consumer and ad elements, highlighting the importance of contextual, personalized and visual aspects (Grewal et al. 2016).

However, one thing to be noted is that advertisers are on the demand side of the ad-serving process. In practice, they demand ad space to show their ads. On the supply side, app publishers create apps and provide those advertising space. Therefore, publishers retain control over supplying ad spaces and delivering ad impressions on those ad spaces (Brakenhoff and Spruit 2017). An ad space, also known as an ad slot, is the designated area on a website or app on which an advertisement can be displayed. In reality, publishers normally receive 20–30% of global mobile in-app advertising revenue (Johnson 2022). The publishers’ goal is to maximize their revenue, which sometimes contradicts the advertiser’s (Boerman et al. 2017; Choi et al. 2020; Maehara et al. 2018).

Due to their role in supplying ad space, the publisher is, indeed, an important player in the money flow (Cao et al. 2023; Valaei et al. 2022). Surprisingly, studies on app publishers are scarce, and subsequently, there are not many optimization options available to help them achieve their goals. On the one hand, instructional materials for mobile devices are shockingly limited (Boerman et al. 2017; Choi et al. 2020). On the other hand, there are ongoing challenges in measuring the effectiveness of advertisements (IAB 2022; Li and Tsai 2022). While effective factors are currently raised more frequently in mobile research, there is no focus on mobile ads as a subject of their own (Hao et al. 2017). Instead, they research mobile ads using a theoretical framework for other platforms, such as the Internet or television (Choi et al. 2020). This lack of academic interest in mobile in-app advertising is unsurprising given the inherent technological and organizational difficulty of conducting a realistic field experiment with mobile ads, as well as the need for close collaboration with practitioners/app publishers who can provide greater access to relevant data, such as traffic acquired via apps (Rutz et al. 2019; Ullah and Binbusayyis 2022).

An empirical study is thus required to understand better the role of app publishers in enhancing the performance of mobile in-app advertising by evaluating the direct and indirect effects in relation to other participants to achieve the common goal of advertising effectiveness.

Literature review

This study began with a review of the literature on advertising effectiveness to identify the common goal of all participants. Based on MAEF, it then looked at the effective factors over which advertisers, users and ad networks control. Lastly, factors controlled by app publishers will be thoroughly reviewed. Literature gaps in the current literature are identified, and the hypotheses are formulated accordingly.

Advertising effectiveness

An ad-serving process involves four parties: users, advertisers, ad networks and app publishers (Choi et al. 2020; Wang et al. 2018). Each perceives the effectiveness of advertising differently.

Online users typically want to receive advertisements that are personalized and relevant to their interests (Balseiro and Candogan 2017). Several early studies (e.g. Bidmon and Röttl 2018; Effendi and Ali 2017) demonstrated that consumers clicked on advertisements they perceived to be trustworthy, personalized and appropriate. Le and Wang (2022) reported a higher click-through rate for users who were active on the platform, seeing ads for products and services similar to those on the web, and were more likely to click on those more relevant to their needs. According to Viclisika and Valdi Arie (2019), consumer preferences for appropriate messaging are changing; they want customized messages which meet their specific needs. As long as the advertisement matches the consumer’s usage goals, the advertisement has no negative impact on its effectiveness. According to some advertising research, the effectiveness of online advertising is determined by the benefits it provides to individual consumers (Hojjat et al. 2017). Not only do consumer preferences influence effectiveness but also shape the advertisements (Rafieian and Yoganarasimhan 2021). Through their empirical study, Cantrell et al. (2022) claimed that, from the consumer’s perspective, the only thing that matters is the relevance of the advertisement. Prior research has confirmed that relevance is the goal of users when it comes to advertising, which is reflected in the ratio of times they click on the advertisement and the number of times they saw them (Appel et al. 2020). As a result, users strive to increase the click-to-impression ratio.

On the other hand, when campaigning for their products or services, advertisers aim to achieve two informational and behavioural goals (Ciçek et al. 2018). They use mobile in-app ads to raise awareness, promote positive attitudes, enhance engagement, increase conversion rates, encourage repurchases and promote advocacy (Meyer et al. 2019). Advertisers willing to spend money on advertisements to increase brand awareness, attitude and intention will strive to meet informational goals measured by the number of impressions (Boerman et al. 2017). On the other hand, those looking for engagement, online conversion or advocacy, will pay for the performance of their displayed ads, which is measured by the number of clicks (Effendi and Ali 2017). Between the two, advertisers typically prioritize the informational goal over the behavioural one (Murillo-Zegarra et al. 2020). Brand recognition is the foundation of any advertiser–customer relationship (Dogtiev 2018). The more customers learn about a brand—the more information they have—the more likely they will trust, buy and remain loyal to that company’s products and services (Le and Wang 2022). Brand advertising is a type of advertising that helps to link and develop long-term relationships. As a result, companies that use brand advertisements strive for long-term positive awareness (Ciçek et al. 2018). Large and mid-sized publicly traded companies in the United States always prioritize long-term branding (Maehara et al. 2018). That means, regarding mobile in-app advertising, advertisers want to increase the number of impressions first, then the number of clicks.

In the meantime, ad networks/exchanges normally seek the best match for their ad inventories (Balseiro and Candogan 2017). The best match encompasses not only the “relevance” in the traditional sense of information-based retrieval analysis but also the best economic revenue (Ahmed and Palusa 2023). Their goal is to maximize revenue based on the likelihood of a user clicking on the ads and the value of advertising brought to consumers (Dogtiev 2018). Revenue optimization is one of the most important aspects of running an ad network business. It applies to large corporations, where Google and Facebook account for 70% of advertising revenue (Johnson 2022). They are all aiming to maximize the match between supply and demand. For that reason, the primary function of an ad network is to optimally aggregate publisher ad space supply and advertiser demand (Ceci 2022a). Indirectly, advertisers can buy advertising space through publishers’ websites and applications via ad networks. Advertising networks there enable advertisers to efficiently organize marketing campaigns across dozens, hundreds, or even thousands of websites and apps (Ceci 2022b). That means the primary function of an ad network is to collect ad space and match it to the needs of the advertiser. The greater the matching rate, the greater their revenue (Ji et al. 2019). As a result, all ad networks strive to increase the ratio between the number of clicks and impressions.

Finally, when it comes to advertising, publishers are concerned about revenue (Choi et al. 2020). In the past, publishers’ allocation and inventory control must be efficient to maximize revenue from guaranteed contracts (Hao et al. 2017). Nowadays, app publishers can earn money from programmatic ads with the help of ad networks based on the number of views and clicks on their ad spaces (Cao et al. 2023). Initially, they provide free information (such as news, comments and responses) and resources (such as email, maps and various online tools) to attract users. Later, they use the users’ past navigation activities to generate impressions and clicks and get revenue from there (Appel et al. 2020). Impressions are provided either directly or through ad networks. Publishers, in turn, utilize ad revenue to cover operating costs. As a result, publisher optimization is critical to their business and the entire advertising ecosystem. Between the two impressions and clicks, clicks always outperform impressions in terms of revenue generation as advertisers prefer potential customers to click through to their website (Appel et al. 2020). The average revenue per impression for Google AdSense varies significantly based on the niche, website quality, traffic source and amount of advertisers on the AdWords platform. It can range from $5 to $10 per thousand impressions (Johnson 2022). Google, as a broker, charges advertisers based on the number of clicks they receive too. Publishers can receive 68% of all clicks’ revenue (or 51% regarding AdSense for search). The publishers’ commission mainly depends on specialty competitiveness and cost per click. In actuality, commissions per click can reach $15 (Ceci 2022a). Clicks definitely bring higher revenue to publishers compared with impressions. As a result, app publishers are more concerned with the number of clicks than advertisers. They want to raise the number of clicks first, followed by the number of impressions.

On the surface, the goals of the four participants are all related to the number of clicks and the number of impressions in one way or another. In fact, all goals can be divided into informational goals or behavioural ones that do not contradict each other (Effendi and Ali 2017). According to Tian et al. (2022), both paradigms, namely informational and behavioural advertising, are not incompatible but complementary. The applicability of either model is determined not just by the advertiser’s aim but also by the customer’s decision journey. As a result, it can be claimed that these informational and behavioural objectives are not mutually exclusive. According to Viclisika and Valdi Arie (2019), a short-term objective should already have long-term ones and vice versa. When advertising is stuck to an app, for example, it impairs the consumer’s experience for a long time before. Therefore a long-term metric should include a penalty term for employing unclicked ads and precisely quantify repeated visits and abandonment already in the past (Rafieian and Yoganarasimhan 2022). Similarly, on the consumer side, delayed or sometimes called latent conversions by users should be already calculated also (Hao et al. 2017). If a short-term indicator is used to measure performance for an extended period, both goals should already have been reflected in it (Dogtiev 2018).

Furthermore, the primary goal of programmatic advertising is to locate the “optimal match” between a specific user and a relevant advertisement in a given context (Choi et al. 2020), which necessitates leveraging information associated with consumers, advertisers and publishers collectively (Hojjat et al. 2017). As a result, publishers do not need to advocate information in the same way that advertisers do, but they do have the same behavioural aim, namely click-throughs. The click-through rate is also used to determine the long-term relevance of advertisements to customers (Effendi and Ali 2017) and the best match for ad networks/exchanges (Kumar 2016). As a result, boosting the ratio of clicks to impressions in an interactive context is where the aims of the publisher, advertisers, ad networks/exchanges and consumers could intersect. As a result, the click-through rate (CTR), the ratio of the number of clicks to the number of impressions, is the metric used to assess the common goals of all participants. A greater click-through rate means higher ad relevancy (users’ aim), higher engagement (advertisers’ goal) and higher revenue (ad networks and publishers’ goal). Therefore, in practice, the click-through rate is the most commonly used indicator to assess the efficacy of online and web advertising (Kumar 2016).

Mobile in-app advertising, however, has its own set of characteristics. Advertisers, for example, have clear rules on television and blogs regarding what makes an ad impression (Andrews 2017). It is not as clear on smartphones and tablets. The current literature does not clearly define what a mobile ad impression is (Sun et al. 2017). Is it where half of an ad view is an impression for a few seconds, or does it have to be the entire ad? Most existing monetization strategies do not consider duration and size as an option to maximize profits (Truong 2016a, b). It was important to standardize mobile advertising measurement as soon as possible rather than wait until advertisers and media complained that they could no longer trust the statistics (Rutz et al. 2019). As a result, the click-through rate formula must be reviewed to accurately measure ad impressions taking into account the view duration and view size for it to be used as a metric to measure mobile in-app adverting effectiveness properly.

It started with a discussion about advertising exposure, which has been going on among advertising practitioners (Sahni 2015). If efficacy is defined as the proportion of clicks over advertising exposure, then what is the advertising exposure? Currently, advertising exposure is commonly defined as the number of views and assumes that every ad view will trigger an impression automatically (Kumar 2016). However, because the ad view is a physical object, it has spacial and temporal dimensions (Cantrell et al. 2022). The spatial dimensions of a view on a two-dimensional screen are its width and height. The duration of the view is its temporal dimension accordingly (Sahni 2015). In mobile devices, the duration is measured in seconds, while the spatial dimensions are measured by pixels (Valaei et al. 2022). Advertising exposure should not be computed solely on the number of views, while the dimensions of those views are omitted (Cantrell et al. 2022).

As a result, the current CTR statistic, as a ratio of the number of clicks and the number of views, cannot fully determine how effective clicks are given their limited duration and size on mobile devices (Tian et al. 2022). In the context of mobile advertising, even though consumers spend much time on their devices, the time available for each piece of information is highly limited (Ullah and Binbusayyis 2022). The existing CTR formula does not account for that. Regardless of how lengthy each view is, calculating the number of views alone does not adequately reflect the impact of their impressions (Truong 2016a, b). Because an advertisement is a physical entity, it should be measured in both temporal and spatial dimensions, just like any other physical object (Tian et al. 2022). Advertising exposure measures the total amount of time an ad was exposed to its target audience, usually measured in milliseconds (Opree et al. 2014). The rate of click-throughs in the context of mobile in-app advertising should be measured as follows:

$${\text{Click{-}through \,rate}}\left( {{\text{CTR}}} \right) = \frac{{\text{Number\, of \,clicks }}}{{\text{Advertising\, exposure}}} = \frac{{\text{Number \,of\, clicks}}}{{{\text{Number\, of\, views}} \times {\text{ad \,space\, duration }}\left( {{\text{seconds}}} \right) \times {\text{ad\, space\, size }}\left( {{\text{pixels}}} \right)}}$$
(1)

That is the average number of clicks over the total area and time of advertising exposure. A higher CTR shows a better result. With this updated formula of CTR, the duration and the length of an ad view are now taken into account, and its effectiveness can be measured more accurately in the context of mobile advertising.

Factors controlled by advertisers, consumers and ad networks

In the current literature, factors influencing mobile in-app advertising and increasing the click-through rate of mobile in-app advertising can be classified into three categories: the ad characteristics (e.g. brand, price, content, entertainment), the user behaviour (e.g. age, gender, interest, preferences, history) and the context (e.g. time, place, environment, technology) (Rafieian and Yoganarasimhan 2022), or three-factor components: stimuli characteristics, personal characteristics and advertising context (Effendi and Ali 2017). In their Mobile Advertising Effectiveness Framework (MAEF), Grewal et al. 2016 classified those components as ad elements, consumer factors and context ones. The three participants control those three groups of factors, respectively: advertisers, users and ad networks. This study examined the effects of some of those factors on the click-through rate to confirm the roles of advertisers, users and ad networks in enhancing mobile in-app advertising effectiveness as highlighted in the current literature.

Location

Firstly, ad networks control the contextual component of Location (Grewal et al. 2016). Ahmed and Palusa (2023) classified location further into logical location e.g. distance, event and geographical one, e.g. area, city and country. According to Andrews (2017), mobile advertising that matches users’ logical location is more effective than those that do not. Logical location data are still a significant tool for marketers—nearly 9 out of 10 advertisers reported that logically location-based ads and marketing resulted in better revenue, driven by client base growth and higher consumer interaction (Sahni 2015). New data-driven tools and methods enable advertisers to better understand, test and analyse logically location-based messages. Simultaneously, contemporary distribution networks provide customer-specific, relevant information wherever consumers consume media. Logical location was an important aspect of marketing campaigns. These days, logical location data continue in performance improvement, revenue generation and consumer engagement (Ciçek et al. 2018).

Previous research has established that logical location is a contextual aspect that has a substantial impact on the effectiveness of online advertising. However, it is not clear if geographical one will have a similar effect, especially in the context of mobile in-app advertising. In another type of advertising, the geographical location of an ad has an impact on users’ attitudes. Furthermore, the geographical location of the ad also has an intentional exhibition of the cultural dimension of an individualistic and collectivistic country (the United States and Japan) and consequently has an impact on the advertising outcome (Moriuchi and Chung 2018). There are also some studies on the effect of geographical location in the context of mobile devices. For example, Livas and Skotis (2022) showed how the pre/postpaid mobile service program and activities with last-digit promotional short message service campaigns can optimize customer awareness about a product or service.

However, even though those studies were in the context of mobile advertising, it was about push type one. Generally speaking, mobile ads in the form of SMS messages and display ads on mobile websites and apps are different. Push promotional includes pushing advertising messages to customers, usually by warnings or short messages. Pull ads involve placing advertisements on websites. On mobile web and phones, messages are passed to the people’s free will, which is known to be a pull-out smartphone display of ads (Livas and Skotis 2022). Between the two forms of mobile advertising, research patterns have been distorted to move to the pull type. Empirical field research in mobile advertising mainly investigates the effectiveness of mobile coupons delivered via SMS, which are push type leaving the pull-type mobile display advertising largely unexplored including mobile in-app advertising and their effective factors like Location (Choi et al. 2020; Grewal et al. 2016).

Not only that the informational study of geographical location in the context of mobile in-app advertising was scarce and limited, but the behavioural performance of each different type was not also fully examined. Ahmed and Palusa (2023) classified geographical location into area, city and country. In terms of country, there were few studies on the relationship between countries and click-through rates. There was one marketing report about the click-through rates among countries, and that was conducted more than 10 years ago when mobile in-app advertising did not even exists (Chaffey 2023). On web advertisements, that study showed that the click-through rates differ significantly across East Asia, North America, Europe, Australia and New Zealand (Region 1) and Latin America, Africa, the Middle East and South Asia (Region 2) (Chaffey 2023).

The lack of academic and analytical reports about click-through rates among different locations in the context of mobile in-app advertising has shown a big gap in our current literature on the relationship between Location as conceptualized in Grewal et al. (2016) and our CTR. This study, therefore, hypothesized that:

Hypothesis 1

There is a significant relationship between Location and CTR.

Time

Similarly, in prior studies, time was seen as an important component that could influence advertising efficacy (Grewal et al. 2016). For example, Huang et al. (2020) discovered that the majority of Twitter’s tweets were written at a specific time of day. Similarly, Sahni et al. (2019) discovered that advertising efficiency varied with the absolute timing. While evenings are often regarded best for video ads due to the volume of video views, early morning viewing has a higher degree of advertising receptivity. According to a national poll, customers who see an advertisement in the early morning are more likely to purchase or respond favourably to offered items or services than those who watch it in the evening (Weingarten and Berger 2017). That is the most aggressive purchase intention window. Except for the early morning time slot, late night/early morning is the second most likely time to buy, a few per cent more likely than any other time of day (Weingarten and Berger 2017).

Similarly, according to a study by Vilaro et al. (2017), weekdays and weekends have various effects too. That was again reconfirmed in the case of marketing emails (LotRiet 2018). According to the Chitika Insights research, advertisers can better target users on Saturdays and Sundays when the rate at which people click advertisements is significantly higher than on weekdays. Weekday CTRs are typically 7–12% lower than weekend CTRs as in the case of web advertising (Chaffey 2020).

Even though there was evidence in both academic and marketing reports about the relationship between Time and the click-through rate, they were conducted on web and email advertising formats. Mobile in-app advertising has its own set of characteristics relating to time. Desktop visits last three times longer than smartphone visits on average, and desktop visitors see more pages and bounce rates are comparatively lower there (Rafieian and Yoganarasimhan 2022). For that reason, mobile users usually expect a seamless, smooth experience when visiting and navigating an app. Readability and proper location of relevant content and calls for action are critical when mobile users browse the pages quickly (Kurtz et al. 2021). Layout clarity and interactive element visibility are needed provided smaller screen sizes. Different from desktops or even laptops, mobile users normally can reach out to their devices at almost any time of the day and any day of the week. For that reason, the advertising effectiveness in the context of mobile in-app advertising could be very much different from other types of advertising.

On the other hand, according to Chaffey (2023), 80% of Internet users own a smartphone and spend 51% of their time on mobile digital media every day (about 3 h) more than they spend on a personal computer (42%). That means, even though mobile users spend shorter durations for each mobile session but because they have more sessions per day, the total time they spent on their mobile devices is actually longer. As the total time they spend on their devices is much longer than any other devices before, does that affect the effectiveness of advertisements displayed on them? That is something that has not been fully studied in our current literature.

Previous research has established time as a contextual factor that has a substantial impact on the effectiveness of online advertising. However, at the same time, the current literature still has not taken into account both characteristics of shorter duration and longer total time per day of mobile users in considering the effectiveness of mobile in-app advertising. This study, therefore, hypothesizes that:

Hypothesis 2

There is a significant relationship between Time and CTR.

Ad type

Ads could also be text-based, image-based, or rich media-based (Ciçek et al. 2018). The level of creativity of an advertisement may be related to how the ad is intended to be interacted with and how online advertisements appear (Le and Wang 2022). Previous models, e.g. Interactive Advertising Model, discussed several standard interactive ad types: banners, interstitials (pop-ups), sponsorships, hyperlinks and websites (Boerman et al. 2017). The model offers a detailed list of subjective advertising features such as consumer-based constructs (e.g. “website mood” and “interest”) and objective advertising features (e.g. colour, size or typeface) across print, broadcast and online (Boerman et al. 2017).

According to Edizel et al. (2017), some advertisers have begun to use animated banners to give gradual and sequential visual effects. Television is often recognized as one of the most disruptive media forms due to its ability to embed moving pictures. When banners incorporate animation, they also deal with the concept of television advertisements, which may mean that animated banner ads draw greater attention and, as a result, more clicks (Moriuchi and Chung 2018). Analyses of TV advertisements in many corporations show that animation improves the rate of clicks (Chaffey 2023). Valaei et al. (2022) found that animation improves response time and recall of banner advertisements. Those studies have confirmed that different ad types have different click-through rates, especially in the case of TV advertising.

In web advertising, static text and static graphics are still commonly employed. In fact, most advertisements today are actually static (also known as non-interactive). Is there any difference between these two types of static ads? According to Mergillano et al. (2022), online users are more likely to remember static image ads than static text ones. Some even complained that static text ads can be confused with application content. A static image ad is a single still frame with a catchy image (Jan et al. 2019). Static image ads can also be found in webinars, blogs, eBooks, texts and landing sites. The fact that static image ads were more effective than static text ads can be explained by the fact that static information helps past visitors instantly recognize the company brand and logo, which are normally in a static mode. On the other hand, static text ads consist of text with no brand logo at all, making them harder to improve brand, product or service awareness (Ansari and Riasi 2016).

Different from web advertising, mobile advertising especially the mobile in-app one has a limitation relating to their screen size and the mobile user behaviours of bouncing mobile apps. Due to the limitation of the screen size, it is not convenient to implement any type of ads. For example, it is not common to see video ads in the format of a banner in a mobile app (Ciçek et al. 2018). At the same time, the number of lines of text is very limited there. It is also not clear if an image without any copywriting messages could work effectively. When the time for each view is very short in the case of mobile in-app advertising, do different types of ads have different click-through is still an open question to answer (North and Ficorilli 2017).

Previous research appears to have established ad type as a factor that has a substantial impact on the effectiveness of online advertising. However, there is still a gap in our understanding of their effect in the context of small screen size and short screen time as in the case of mobile in-app advertising. This study, therefore, hypothesizes that:

Hypothesis 3

There is a significant relationship between Ad Type and CTR.

Ad medium

Grewal et al. (2016) consider Ad Medium as one type of ad element which are controlled by advertisers. The ad medium is the platform that makes the ad available to the user. The operating systems (for example, iOS and Android) on which the app is running can serve as the ad medium too. Because various mobile platforms have their disparities, it is reasonable to expect that advertisements displayed on different platforms generate different click-through rates (Bidmon and Röttl 2018). Because users’ intentions to access the Internet varied, such as information searchers versus entertainers, website visitors may respond differently to news and entertainment marketing ones (LotRiet 2018). Park and Park (2020) discovered that different websites had different users. Consumers on one social networking site favoured animated advertising, while consumers on the other social networking site preferred static advertising. When it came to surfing websites, animated adverts outperformed static ads by a wide margin (Kumar 2016). All those studies have confirmed that the medium on which the ads are shown plays an important role in determining the performance of those ads.

As one example, a web page or a mobile application are two different platforms and by definition are two different types of ad medium. The content of a web page or application can influence the perception of an advertisement (Grewal et al. 2016). For that reason, the design/aesthetics of the app/website/medium on which advertisements are presented and managed by advertisers is referred to as Ad Medium (Brakenhoff and Spruit 2017). Aesthetics is especially important since it could generate familiarity and convenience that most participants appreciate (Jan et al. 2019). According to a study (Brakenhoff and Spruit 2017), advertisements displayed on different websites with different designs may provide different outcomes. These days, some applications have a solid cognitive consumption design, which helps users easier to use. The term cognitive consumption relates to how much brainpower the app requires. The human brain has a finite amount of cognitive capacity. If an app suddenly provides too much information, it may confuse the user and force them to leave their current session immediately (Rosenthal et al. 2021).

In the current literature, even though there are some empirical studies about the relationship between the medium on which the ads are shown with their effectiveness, there is a gap in our understanding of it in the case of mobile apps. Different from websites, mobile apps are more touch based where users instead of using computer mice to click on an interface element of their choice, need to use their fingers to interact with them. Most companies today have to design both web and mobile versions of their applications separately in order to maintain the best experience for their users in both click-based and touch-based interfaces. A mobile app is not simply an extension of a web one.

Previous research appears to have established that ad medium has a substantial impact on the efficacy of online advertising. A mobile app with a touch-friendly user interface could have a different click-through rate than one with a click-friendly user interface. This study, therefore, postulated that:

Hypothesis 4

There is a significant relationship between Ad Medium and CTR.

Publishers-controlled factors

According to Brakenhoff and Spruit (2017), any mobile in-app advertising ad-serving process may be divided into two steps: ad space designing and ad space displaying. Ad space is a portion of a website or app that is utilized for advertising (Constantin et al. 2018). Ad space or ad slots was not addressed in the early days of web design, but it is now an essential aspect for website and app publishers who rely on advertising revenue (Kohavi and Longbotham 2017). One of the issues with web design is how to use ad space to incorporate advertisements without alienating visitors. The website used to have upper and lower banner space for ads and space for left and right ones too. Publishers have recently experimented with larger ad sizes, such as skyscrapers and rectangular advertisements.

The publisher could considerably improve mobile in-app advertising efficiency by designing ads with some preset and relevant attributes and displaying the ad spaces with appropriate schemes. The question is, what are those design elements and display schemes?

Ad space duration

According to the Interactive Advertising Bureau, ad space can have two characteristics: duration and size (Sabharwal 2021). Publishers control how long ads appear on their apps, regardless of how long marketers create them (Ji et al. 2019). They can accomplish this by determining the duration of their ad spaces. When a publisher provides an ad space with a preset duration or a predefined size, only ads with those elements are chosen to be displayed. Ad space and the ad itself are not the same. For example, even if an advertiser creates a 30-s video ad, that video ad may only be broadcast for 15 s due to ad space constraints when displayed (Sun et al. 2017). That means even though there are some studies about the effect of the duration of advertisements in the past, there were more about the content of it, which is created by the advertisers, not how it is displayed in the mobile apps themselves which is controlled by the app publishers.

Even so, much research on the efficiency of ad duration has been conducted in the fields of television and website advertising. One example is a study by Vilaro et al. (2017). The researchers discovered a link between increasing exposure time and greater awareness. In one experiment, increasing the time of a TV ad somewhat enhances the likelihood of recalling the ad (Vilaro et al. 2017). According to Tian et al. (2022), presenting two shorter commercials resulted in a higher recall rate than displaying a longer ad twice the duration. Longer advertisements had higher click-through rates, according to Sun et al. (2017).

However, one other study, by Ciçek et al. (2018) suggested that dynamic banners may be more difficult to remember than static ones, implying the negative effect of duration. Similarly, Edizel et al. (2017) discovered that banners with extensive messages and multiple frames received fewer clicks (animation). The author found that these two elements increase the ad’s complexity and, as a result, negatively impact the viewer’s reaction to the banner and its response. In the context of online advertising, studies on the impact of ad duration on advertising efficacy produced mixed results.

Not just that previous studies on web advertising yield conflicting results about the effect of the duration of ads, but they were not correctly measured. Traditional monetization strategies, in most circumstances, do not particularly include time as an optimizing tool (Sun et al. 2017). In previous studies, there was no difference in terms of measurement between a 30-s ad and a 5-s one. Because of that incorrect measurement, previous studies seem to yield different conclusions about the relationship between ad duration and click-through rates leading to a gap in the current literature about ad duration and its effectiveness (Tian et al. 2022).

All of the studies mentioned have indicated that the ad space duration has not been thoroughly studied (and properly measured) in the past. However, it may be a factor that can significantly moderate the relationships with the click-through rate of mobile in-app advertising, especially when the ad duration is taken into account. As a result, the following hypothesis was advanced in this study:

Hypothesis 5

Ad Space Duration significantly moderates the relationship between Location, Time, Ad Type, Ad Medium and CTR.

Ad space size

Publishers can set the size of their ad spaces the same way they can set the duration of their ad spaces (Sabharwal 2021). When a publisher provides an ad space with a predetermined ad size, only ads with that feature are chosen (Valaei et al. 2022). By doing so, the publisher could indirectly improve advertising effectiveness by supplying the ad space with preset and relevant features. Similar to the case of ad duration, that means even though there are some studies about the effect of the size of advertisements in the past, there were more about the content of it, which is created by the advertisers, not how it is displayed in the mobile apps themselves which are controlled by the app publishers.

The conventional wisdom in the business suggested that large banner advertising should draw more viewer attention as measured by clicks, which supports previous research findings (Ansari and Riasi 2016). The success of larger commercials in capturing attention could come from the viewer’s perception of brand quality. Larger advertising may represent a higher degree of promotional cost and effort, which the consumer should interpret as a higher level of brand reputation and popularity (Maehara et al. 2018). According to North and Ficorilli (2017) larger ads draw more attention and are more likely to elicit a response.

In contrast, empirical data from Wang et al. (2015) revealed that click-through rates do not rise proportionally to size. According to the authors, both smaller and larger ads perform equally well. Wang et al. (2015) claimed that no significant banner size-clicking association. As a result, the relationship between banner size and click-through rate is conflicting. The question of what size should a mobile ad keeps raising in the current literature without a valid answer.

Not just that previous studies on web advertising yield conflicting results about the effect of the size of ads, but they were not correctly measured. According to Herrewijn and Poels (2017), the effect of ad size is currently ignorable, partly because existing measurement methods do not account for the size variable. That could be negligible in the case of web advertising when the screen size of a computer is quite large. Because the screen size of a mobile device is much smaller, the effect of ad size on mobile apps may be significant there. That could be explained by the customer’s limited cognitive abilities as theorized in the Limited Capacity Model (Fisher et al. 2018). There are screen size limits in the mobile context. Mobile device screens can be as small as an Apple Watch, while smartphone screens are typically one-fourth the size of a conventional computer. This constraint should be seriously taken into account.

All the studies mentioned have indicated that the ad space size has not been adequately investigated (and correctly measured). However, it might be a factor that can considerably affect the click-through rate of mobile in-app advertising, especially when the ad size is taken into account. As a result, the following hypothesis is proposed in this study:

Hypothesis 6

Ad Space Size significantly moderates the relationship between Location, Time, Ad Type, Ad Medium and CTR.

Ad space position

Aside from providing ad space for bidding, the publisher is also in charge of delivering ad impressions. After the advertiser and ad network have chosen the ads, the publisher will have full control over how the ads are displayed to the user. In specifics, the publishers have complete control over how the adverts appear and when they appear on their applications. The Interactive Advertising Bureau recommends placing ads at the top or bottom of the screen and in the centre of page sections (Sabharwal 2021). They also advise that ads be placed before, during, or after the primary content experience. The display characteristics related to publishers are crucial in the ad-serving process, which could improve the click-through rate of mobile in-app advertising (Sahni et al. 2019).

Some authors have conducted studies on the positioning and timing of advertisements on websites such as Ciçek et al. (2018) and Sahni et al. (2019). These studies have demonstrated the significance of position in online advertising. According to Herrewijn and Poels (2017), the spatial position is the most critical placement element. Several authors discovered that the ad’s location in the Sponsored Search Result Pages significantly impacts its CTR. This location effect has been the subject of extensive investigation in the past, however unfortunately with conflicting results (Valaei et al. 2022).

On the one hand, several studies have found a strong relationship between position and CTR (e.g. Ciçek et al. 2018). They have revealed that banner adverts at the top of a website are more frequently clicked than banner advertisements elsewhere (Alanazi et al. 2020). Customers searched fewer than two stores in a typical search session (Chaffey 2023). Similarly, it was discovered that just a small percentage of shopbot users chose deals that were not on the first page (Albers and Passen 2019). Due to the cognitive cost of comparing options, customers frequently focus on a narrow range of results (Fisher et al. 2018).

On the other hand, Huang and Yoon (2022) examined the impact of native ad placement on sales and income. The authors examined the influence of ad placement on their effectiveness. They observed that the top position is often not the position of revenue or profit maximization. Native ads are placed alongside the contents, and normally in the middle of the screen. Similarly, Küçükaydin et al. (2020) discovered that a more prominent location in promotional emails increased the likelihood of clicking. Brand placements (e.g. full commercials, and centre advertisements) have been demonstrated to be effective in raising consumer awareness and significantly impacting brand recognition in other studies (Ayanso and Karimi 2015). They contradict those who previously demonstrated the top position’s efficiency.

Which position, top or centre, is the best for displaying ad space on mobile apps? The distinction is that in web advertising, the computer screen is constantly static, whereas mobile users can move their devices and subsequently their screens around (Murillo-Zegarra et al. 2020). This kind of screen movement can only be observed in mobile advertising and not in PC, and definitely not in TV either. There is a gap in our current literature about ad positions that we need to fulfil. In the meanwhile, until an answer is found, many publishers merely display certain banner advertising without considering how effective the placement of those ads is (King 2017). The issue of maximizing mobile advertising placements remains unresolved (Grewal et al. 2016).

All of the research discussed above provide some insight into how the position of advertisements can affect their efficacy in other types of advertising but with conflicting results. Furthermore, because of its own characteristics, the effect of ad position has not been fully studied and needs to be verified accordingly. This study, therefore, hypothesizes that:

Hypothesis 7

Ad Space Position moderates the relationship between Location, Time, Ad Type, Ad Medium and CTR.

Ad space timing

Ads can also be displayed before, after, or between sessions (Sahni et al. 2019). The click-through rate could be drastically different with different display strategies like that. However, as Goldstein et al. (2015) claimed there is no direction for publishers on how to schedule advertising until recently. Lee et al. (2021) have urged publishers to reclaim inventory control and to remember that timing is just as essential as audience targeting. In the context of mobile apps, publishers have full control over when the advertising is delivered and not fully making use of it (Brakenhoff and Spruit 2017).

Even in the absence of guidelines, website publishers have attempted to schedule the display of their adverts in one way or another (King 2017). Sahni et al. (2019) discovered that customers are more inclined to choose advertisements towards the beginning of an online directory. Weingarten and Berger (2017) investigated how temporal location influences word of mouth, whether it is past, present, or future events and found a significant relationship there. Similarly, Lee et al. (2021) emphasized the significance of what, when and where in delivering ads also known as ad scheduling. The first seconds of exposure resulted in a significant boost in the commercial’s memory, and the effect on recall declined as time passed (Albers and Passen 2019).

Compared to TV networks, website publishers can monitor traffic on their websites and, as a result, develop a click-generating strategy more effectively (Appel et al. 2020). Ciçek et al. (2018) created a programming technique incorporating ad-related functions to schedule banner advertisements. Goldstein et al. (2015) discovered that the interval between objects or events affects their outcomes, indicating that advertisements should be shown not too early in the customer journey. Similarly, Sahni (2015) conducted a field experiment on the restaurant search website. The main finding of the research was that lengthening the interval not too long between advertising exposures by up to 2 weeks enhances the likelihood of a purchase. All of those studies have confirmed the importance of ad space timing in the context of web advertising, where not only absolute but relative timing plays an important role.

In the context of mobile in-app advertising, Ad Space Timing refers to the timing of ad spaces delivered by the publishers. For example, when a user first opens the application and has not done any action yet, the ads showing at that time are considered the beginning. When the user has performed the main activity, e.g. capture a photo, finish one level in games and finish a call, the ads showing during that time are considered the end (Sahni et al. 2019). In mobile in-app advertising, similar to time, the timing is more relative. Mobile users tend to bounce from one app to another. With a shorter time for each app like that, is that enough for them to navigate through the whole session, and a later advertisement could be optimal timing in that case?

The current literature still cannot help us with the answer (Lee et al. 2021), even though previous studies seem to agree on the importance of ad space timing otherwise. This study, therefore, hypothesizes that:

Hypothesis 8

Ad Space Timing significantly moderates the relationship between Location, Time, Ad Type, Ad Medium and CTR.

In this section, the literature related to mobile in-app adverting has been throughout received. Clearly, there are gaps in our knowledge relating to the determinants of mobile in-app advertising effectiveness, especially the ones controlled by app publishers. Accordingly, this study has formulated eight hypotheses, which would then be evaluated following the methodology presented in section “Methodology”.

Methodology

The click-through rate is the main focus of this study. To examine that quantitative data, this study used a deductive approach for data analysis. Accordingly, the author generated a conceptual model based on the hypotheses illustrated in Fig. 1. As shown, it has eight factors and eight relationships. The eight factors are divided into two groups those are controlled by publishers and those are controlled by the other participants. The eight relationships relate to the eight hypotheses of this study. Four of the eight relationships are referred to as direct effects, while the other four are referred to as moderating ones.

Fig. 1
figure 1

The conceptual model of the present study

Full size image

This study began its data collection by gathering primary data via an online experiment. The measurement was done through ad spaces, which are formed based on four basic parameters specified by publishers. The research process begins with (1) developing two Android applications (named Camera 720 and Camera+, one is a social sharing app with a touch and mobile-friendly interface, and another is a utility one with a mouse click design as the two values of the Ad Medium variable) and releasing them to Google Play Store, (2) designing 16 ad spaces within those applications (that corresponds to 24 combinations of 4 ad space-related factors), (3) loading ads from Google Ads to those ad spaces, (4) collecting the number of views and clicks on loaded ad spaces via Google Admob and (5) calculating the click-through rates on those ad spaces taking into account the view durations and sizes.

The data were gathered beginning on 1 September 2021, and terminated on 31 August 2022. The total number of impressions recorded is 15,511, which meets the sample size requirement for non-probability sampling (Cochran 1977). Accordingly, the sample size is determined as follows:

$$N = \frac{{z^{2} \times p \times \left( {1 - p} \right)}}{{c^{2} }} \times 2^{n - 1} ,$$
(2)

where p is the percentage of selecting a choice and is computed as CTR, where CTR is the average CTR of the entire sample, which is 5% as the standard (Huang and Yang 2012); n is the number of factors, which is 4 in this study; c is the confidence level, which is 1% as the standard; and z is equal to 1.64 as a standard z-score with the probability of 95% (Cochran 1977). As a result, the sample size required is 10,224 impressions.

The acquired data were input into the Statistical Packages for Social Sciences (SPSS) application before its analysis. There the obtained data were examined for outliners and missing data before the normalization check. The data gathered had a normal distribution (Shapiro-w Wilk’s score = 0.949, p = 0.097), were reliable (Pearson’s r score = 0.99) and were externally valid (CTR = 5%) (Walliman 2021).

To test the moderating effects, the study used two statistical techniques to analyse the collected data. The first is Moderated Regression Analysis, a regression-based multi-factor analysis technique (Hayes 2017). In this method, a product of variables is introduced within an equation to reflect interaction effects. The regression equation has the form of Y = β1X. + β2M + β3XM. The coefficients β1, β2 and β3 are used to confirm the interaction and its magnitude. Another technique is Multigroup Moderation Analysis, which is based on the Structured Modelling Equation (Hair Jr et al. 2017). In Multigroup Moderation Analysis, one looks for changes in the structure of how variables are associated across groups (Hair Jr et al. 2017). Method triangulation refers to the utilization of multiple methodologies. Its goal is to double-check and increase the credibility of the findings (Hayes 2017).

Results

Four main effects and seven moderating effects were confirmed with the statistical tests. The full results are presented in Table 1.

Table 1 Hypothesis testing results
Full size table

Main effects

Unlike prior studies on Location (e.g. Thiga et al. 2016a, b), this study discovered a significant variation in click-through rates between regions. Region 1 includes countries in North America, Europe, East Asia, Australia and New Zealand, while Region 2 includes those in South America, Africa, the Middle East and South Asia. People in Region 2 appear to click on mobile apps more than those in Region 1. That could be explained by the fact that the economies in Region 2 are developing faster and there are increasing consumer demands, including advertisement consumption. Previous reports demonstrated a difference in click-through rates between countries worldwide (Chaffey 2023). However, no substantial difference between the two regions of countries was reported and statistically verified. Besides that theoretical contribution of confirming and location factor from MAEF, the outcome is critical because it allows businesses to decide where to spend their mobile advertising budget geographically to receive a higher return on investment.

In addition, unlike prior research on Time (e.g. Vilaro et al. 2017), this study concentrated on mobile apps and discovered similar results, namely that mobile ads are more effective on weekends than on weekdays. Some previous studies have discovered a variation in daily email marketing click-through rates according to days but in other types of advertising, e.g. email marketing (Kirsch 2022). Weekends are typically considered a time for individuals to unwind rather than work. During this period, customers will engage in more recreational and shopping activities. On weekends, people normally visit shopping malls and movie theatres instead of appointments and emails (Albers and Passen 2019). People tend to shop more on the Internet on weekends than on weekdays in the online world. That could explain the study’s findings. As a result, businesses may target their adverts on the weekends for a better click-through rate. This finding reconfirms that there is a significant relationship between Time and the click-through rate as conceptualized in MAEF (Grewal et al. 2016).

Furthermore, in contrast to prior research in web advertising (e.g. Cheung et al. 2017), this study discovered that text ads outperform image ones. That is explained by the fact that only banner ads were employed in this study. Image banner commercials could be more effective in a full-screen/interstitial format. Including a video on a page that already had multimedia material may not be a smart idea. This finding is noteworthy because it differs from the results of other types of advertising, such as web advertising. Image ads have been discovered to be more effective than text ads in web advertising because they appear to stand out from text-only websites. However, for mobile apps, people must write brief descriptions of their adverts to entice mobile users to click. In any case, Ad Type has been proven to be a component that can substantially impact advertising efficacy as conceptualized in MAEF.

This study expanded on prior research (e.g. Brakenhoff and Spruit 2017) by comparing the impact of ads across applications and discovered that ads displayed in different apps resulted in significantly different click-through rates. Mobile ads displayed on an app with a menu screen and an activity screen showed higher click-through rates than those displayed on an app with simply an activity screen. That is explained by the fact that advertising alongside the significant features of apps might be perceived as distracting information and ignored by users who should focus instead on the main functionalities. This finding is significant because it allows businesses to choose higher-performing applications to run their advertising campaigns, as advertising effectiveness varies from one app to another as suggested in MAEF.

Moderating effects

The two factors, Ad Space Duration and Location were found to interact significantly. People in different regions of the world seem to have different behaviour towards advertising (Chaffey 2020). Still, it is fascinating that people in developed and developing countries perceive differently how advertisements are brought up in shorter and longer forms. People in developed countries seem to like shorter advertisements much more than those in developing countries. This finding is noteworthy because it helps app publishers design their ad space according to the region of the published apps. The ad network could also benefit from this finding because ad networks are the party that has access to the location information (Thiga et al. 2016b). This finding implies that to enhance the effectiveness of mobile in-app advertising, publishers should not work alone but together with other participants. The moderating effect from Location to Ad Space Duration proves the necessity of collaboration between them and all other participants.

The factor Ad Space Duration was discovered to attenuate the effect of Ad Type significantly. It means that text and image ads have different effects, but it also depends on what type the ad is. This finding can be explained by the fact that a meaningful message may require more time in a video than in a text format (Dogtiev 2018). In reality, this study found that sorter advertisements in text format are more effective when their display time is taken into account. The finding assists publishers and advertisers in determining the optimal combination of ad type and duration to increase effectiveness even further. In the past, duration was not considered a factor to be optimized (Sun et al. 2017). Subsequently, the interaction of Ad Space Duration with other parameters such as Ad Type has never been thoroughly researched (Grewal et al. 2016). Again, this study underscores the significance of Ad Space Duration and its impact on the efficacy of mobile in-app advertising, either directly or indirectly.

The next two factors being strongly connected are Ad Space Duration and Ad Medium. The study revealed that the impact of shorter advertisements on different apps differs significantly from that of longer ones. In other words, Ad Medium moderates the effect of Ad Space Duration. That is understandable, given that different apps have varied designs (Lim et al. 2016). Some designs work better with shorter adverts than others. The finding has implications for publishers that want to optimize their ad spaces’ click-through rates by wisely combining the duration of their ad spaces with the design of their app. This finding also revealed a significant moderating influence of a publisher-controlled factor on the effect of an advertiser-controlled one.

According to the data, Ad Space Size also significantly moderates the effect of another factor. That is Ad Type. This research suggests a significant difference between text and image adverts when their sizes are altered. Text ads in smaller sizes are found to be more effective. That can be explained by the fact that image advertisements typically demand larger sizes than text-only ones (Cheung and To 2017). This finding is significant for publishers and advertisers because it encourages them to collaborate to determine the appropriate ad size for each ad type. If ad duration is a temporal dimension, size is a spatial one. Unfortunately, ad sizes were not taken into account in the past due to the lack of an appropriate metric. Without such a statistic, the influence of Ad Space Size could not be discovered, not to mention their moderating effects, such as the one between Ad Type and Ad Space Size.

The factor of Ad Space Position was also found to attenuate the effect of Ad Type significantly. This study discovered that the impact of ad space position on click-through rate differs dramatically between text ads and image ones. This finding revealed an interplay between an advertiser-controlled factor and a publisher-controlled one. In particular, based on the data gathered in this study, it is evident that the top position should be reserved for text ads. That appears to be the most convenient position for consumers to read, while a lower position could be dedicated to watching films or viewing photographs. Publishers might choose the best combination of the two factors to boost the effectiveness of their ad spaces even more.

Ad Space Position and Ad Medium are found to be very strongly connected. It revealed that the influence of ad space position varies significantly among apps. The top advertisements are more effective than the middle ones. Those distinctions, however, vary widely from one app to another. As in the case of the relationship between ad space duration and ad medium discussed above, each app has its design, and that design may influence how the position of ads affects the click-through rate (North and Ficorilli 2017). Combining which app with which position may result in more rewards for app publishers and advertisers.

Finally, it has been found that Ad Space Timing considerably moderates the effect of Ad Type. This study discovered that the influence of ad space timing on click-through rate differs significantly between text and image ads. In particular, the first time slot should be reserved for text adverts. That appears to be the most convenient time for consumers to read while watching films or viewing photographs can be happened later (Belanche et al. 2017). Publishers could select the optimal combination of the two ad characteristics to even improve the efficacy of their ad space inventory.

Discussion

As the key research questions and suggested directions summarized above revealed, publishers play an integral role in the ad-serving process, impacting the click-through rate individually and interactively. This argument was proven with this study. Several publishers-controlled factors determine these relationships, including ad space duration, ad space size, ad space location and ad space timing. Accordingly, this study proposed a conceptual model building around a common goal of all participants. One metric has also been developed to measure that common goal, facilitating the model’s evaluation process. The model is the backbone for this study to develop its hypotheses, which have been tested successfully with the collected data from thousands of mobile users worldwide. The contribution of this study is, therefore, threefold: theoretical, practical and empirical.

Theoretically, the research contributes to mobile in-app advertising literature by modelling publishers’ role and the impact of their design and display factors on the click-through rate of mobile in-app advertising. Models are how humans perceive reality. Physicists tend to find a universal formula of the universe one way or another. Biologists tend to find a typical pattern in all walks of life. Social scientists want to find typical behaviour among humans. Models are, therefore, the ultimate goal of our work in science. A theoretical contribution is the introduction of new constructs and relationships in a model. This study has done this part of extending previous models of mobile advertising effectiveness models (e.g. Grewal et al. 2016) to include more constructs and relationships. In specifics, this study has introduced four new conceptual constructs of ad space duration, ad space size, ad space position and ad space timing. It also introduced four new conceptual relationships between these new constructs with the existing theoretical constructs of location, time, ad type and ad medium. These new constructs and relationships are all drawn up into a conceptual model that this study has tested successfully.

While effective factors are currently raised more frequently in mobile research, there is no focus on mobile ads as a subject of their own (Hao et al. 2017). Instead, they research mobile ads using a theoretical framework for other platforms, such as the Internet or television (Choi et al. 2020). Researchers tend to believe that mobile ads’ ad characteristics are similar to those of other media (Boerman et al. 2017). Consequently, the literature was saturated with contradictory research attempting to apply established theories to mobile advertising and very little research attempting to understand mobile advertising from the foundation (Korula et al. 2016). In the context of advertising platforms, it is believed that continuous innovation in mobile technologies allows for new advertising methods that are not found on more traditional mediums like television and the web. So if we repeatedly apply findings from other media to the mobile platform without caring about its uniqueness, we will repeatedly find different results as seen in the literature. This study is for mobile in-app advertising and considers all the mobile characteristics. Subsequently, the study has drawn up a conceptual model to be tested and laid out a theoretical foundation for future studies on mobile in-app advertising effectiveness in that regard.

Practically, based on the data analysis results, this study then suggests new advertising strategies associated with publishers to enhance mobile in-app advertising further. By this, newly integrated advertising strategies were recommended to be applied in practice. They could increase mobile in-app advertising revenue significantly higher by balancing the benefits of all participants involved. Until recently, there are only three targeting options available using either ad elements, consumer information or context data (Boerman et al. 2017). This study proposed a new targeting method relating to designing and displaying ad spaces. The study proposed four values and seven combinations of their variants to optimize and further improve the advertising effectiveness in specifics. Many authors have called for publishers to take back control of their ad spaces. Until recently, publishers usually outsource their ad spaces to ad networks to optimize their inventory (Effendi and Ali 2017). That is nothing wrong, except that many features left that the ad networks cannot do on their behalf. Those are the duration, the size, the position and the timing of their ad spaces. An integrated advertising campaign, therefore, must include the publishers whose important role was shown in this study.

Furthermore, for publishers, who have more than one app published, applying the new ad space designing and displaying strategies could bring multiple benefits. For agents, who publish the apps on the publishers’ behalf, this strategy can bring even more value. Such agents could find the strategies proposed by this study useful when running mobile in-app advertising campaigns and further increase revenue. Today, many “big” publishers could have just a few apps, but each one attracted many users. With such a large installed base, applying the new strategies could bring back immediate results. Not only the publishers, but other participants could also find these strategies benefitable for them. Currently, most ad networks allow publishers to select the duration and size. However, their options are very limited. For example, Admob only allows ad spaces longer than 30 s and not smaller than 16 kilo pixels (Ceci 2022a). They could provide more options. Ad networks can also integrate new strategies associated with these factors to increase the matching and relevance of the ads. A higher click-through rate then benefits the advertisers as their ads are better consumed, and the customers find the ads more relevant for their own usage.

This study also developed a new empirical method, where multiple factors controlled by multiple participants could be tested concurrently. Previous studies have always struggled to test individual factors sequentially (Kohavi and Longbotham 2017). That will consume much time and could leave out high-level interaction effects. In fact, at Google, the technique they use is overlapping measurement (Kohavi and Longbotham 2017). The disadvantage of that approach is that it does not provide a full factorial analysis of the collected data. On the other hand, this study proposed a new way of measuring the click-through rate on at least 16 ad spaces concurrently. It started by designing all those ad spaces in one app and then scheduling to display them randomly. The use of a randomization mechanism helps all ad spaces chances to be equally displayed. Firebase employed some multiway testing techniques, which allows users to select a specific combination of factors (Khawas and Shah 2018). However, such a combination is tested over a limited period, before another test can be run. Therefore, the users will find that sequential testing challenging to keep track of and narrow down the chance for them to find out what combination of two or more factors could yield the highest click-through rate (Rojas et al. 2016). Instead, by using a new method, the data collected are in a multi-dimensional panel format, which could help us test one, two and multiway effects much more efficiently while minimizing the confounding effects at the same time.

This study also proposed an updated formula for click-through rates. The conventional formula of click-through rate was found to be not suitable to measure the impacts of time and size-related factors (Truong 2016a, b). By considering the total exposure of views, not just by the count, duration and size-related factors can now be tested more correctly. It helps eliminate current misunderstandings and explain previous contradicting results. Many authors have complained about the lack of measurement methods that could correctly measure advertising effectiveness. Some pinpoint clearly that previous studies have not successfully defined a view—half of a screen or a full screen. When working with spatial and temporal factors, this study has experienced many measurement insufficiencies. Accordingly, a new metric, an updated formula of click-through rate, which takes into measurement the duration and the size of ad spaces, has been constructed. This new metric has helped this study and could help future research when dealing with spatial and temporal factors. Without considering their duration and size, there is no significant difference between them, as shown in this study. That explained why previous studies showed contradicting results regarding these two variables.

The present study is quantitative, and a range of traditional and relatively new and more advanced statistical approaches was used to unravel the research problem. Ultimately, the main contribution to methodology and empirical measurement came from implementing Moderated Regression Analysis and Multigroup Moderation Analysis techniques. Considering the conceptual model from the two perspectives of a regression equation and a path diagram has led this study to apply both techniques. This practice could apply to other research with a similar set of categorical moderating variables. The use of more than one statistics technique is called method triangulation. Its purpose is to cross-check and improve the credibility of the findings. The use of both Moderated Regression Analysis and Multigroup Moderation Analysis to test the moderating effects set out an example for future research and could be considered another empirical contribution of this study.

This study, however, has several limitations that need to be mentioned. Firstly, in its theoretical conceptualization, the proposed conceptual model has included only a limited set of variables, although other potentially influential variables can also be included. In specifics, this study found four factors being controlled by publishers. Among them, two factors are related to the ad space designing process, and the other two are related to the ad space displaying one. Future research needs to explore deeply these two processes in order to find out more factors controlled by publishers. When designing the ad spaces, the publishers could specify the characteristics of those ad spaces. They could specify the duration and the size of them as studied in this research. They could also specify the shape of the ad space, for example. This study has not examined the difference between banner, rectangle and leaderboard ads. The leaderboard ads are the one that runs along with the mobile screen’s height, while the rectangle ads are usually placed in the middle of the screen with the width and the height roughly equal. The shape of the ad space could be a factor that can impact the advertisement’s effectiveness.

Besides the two-way interactions which have been tested, the factorial design could help detect even higher interactions among factors, making the practical contribution of this study more extensive and profound. Some other areas that could be explored include the interactions among publishers-controlled factors or the interactions among contextual factors themselves. Future research could evaluate the combined effects of these factors, which include multiway moderating effects. Not only finding the optimal value for each factor individually, but future research could also increase the effectiveness even higher if they could find the optimal combination of them as well. For example, ad spaces designed with the optimal duration and optimal size could increase individual click-through rates even higher. Similarly, other factors can also be combined. That could lead to a dramatic improvement in terms of engagement and revenue. This study responded to the call of many scholars in the field to explore the interaction of factors in mobile in-app advertising (e.g. Grewal et al. 2016). The study itself calls for even more research into this promising area. This study has confirmed that there are indeed interactions among factors controlled by different participants, but the search for them is just at its beginning.

Conclusions

In recent years, mobile in-app advertising has become one of the most common business advertising platforms. Annual spending on this new advertisement form keeps rising year after year. Despite its practical success, mobile in-app advertising’s background theory is still in its infancy. Subsequently, educational resources related to in-app advertising are scarce. Therefore further research on this new subject is required, both conceptually and empirically. The topic of improving advertisement efficacy in mobile apps continues and is more urgent than ever. In many respects, the challenge is that mobile in-app advertising is mostly different from other online advertising types with their smaller screen sizes and shorter screen times. Even today, there are ongoing challenges in assessing and maximizing the efficacy of advertising. This kind of advertising often witnesses the emergence of new actors, the ad network and the app publisher, leading to new theoretical constructs and more nuanced conceptual relationships. Besides, due to the inherent technical and organizational complexity of developing a realistic field experiment with mobile ads, it required close cooperation with practitioners and technicians who could provide greater access to relevant data, such as system traffic. No time in the past has combined advertising with technology as in the present, and no other advertising where technology plays such a significant role as in mobile in-app advertising. As a vibrant new discipline, mobile in-app advertising involves various research fields, including marketing, communications, data mining and analytics, statistics, economics and even psychology, to predict and understand consumer behaviours. Mobile in-app advertising is a new and challenging topic from both theoretical and empirical perspectives.

Previous research looked at the efficacy of interactive ads depending on the variables regulated by the advertiser, the user, or the ad network. Although the factors listed in mobile research are discussed more frequently, there is no in-depth analysis of mobile advertising as a subject of its own. Instead, they research mobile ads through a theoretical framework that is specific to another medium. It has been believed that such ad features are the same for various forms of advertisements. It was subsequently found the literature was saturated with contradictory research trying to apply current theories to mobile advertising and very little research trying to understand mobile advertising at its heart. Despite the apparent usefulness of the previous effectiveness frameworks, it only involves factors related to advertisers, and consumers—the demand side of an ad-serving process. On the supply side, the publishers also have their own influence over the supply of ad spaces and how many ads appear on their mobile apps. Data reveal that a significant percentage of the mobile in-app advertising budget is directly charged to the publisher. Furthermore, the publishers have their own agendas, profit maximization being one of them, which can often clash with advertisers. However, few studies have been completed on app publishers’ vital role, and there are not many optimization options available for them. Research on mobile in-app advertising needs to overcome the inherent technical and organizational challenge of implementing a reasonable field experiment with mobile ads and the need for close cooperation with practitioners/publishers who can provide greater access to relevant data, such as traffic acquired through apps.

Considering mobile in-app ads as a topic of its own, this study went deep into examining this new platform, finding new knowledge about its participants, roles, goals, outcome metrics and factors to create a conceptual model for mobile in-app advertising. The emphasis is on publishers, who received the least attention in the current literature. The research objective of this study is to identify the publishers-controlled factors and evaluate their impacts on the effectiveness of mobile in-app advertising. Four publishers-controlled factors were successfully found in this study and used to evaluate their moderating effects. The empirical evidence has shown that all the four publishers-controlled factors: Ad Space Duration, Ad Space Size, Ad Space Position and Ad Space Timing all have strong impacts on the effectiveness of mobile in-app advertising. That finding has confirmed the important role of publishers, closing our gap of understanding about through which factors this participant can impact the effectiveness of mobile in-app advertising.

To evaluate the moderating effects of contextual factors on the publishers-controlled effect, an experiment using a factorial 24 design was then performed. Besides the four publishers-controlled factors, four other factors controlled by advertisers, consumers and ad networks were also included in the experiments to test the moderating effects of the publishers-controlled factors. Both Structured Equation Modelling-based Multigroup Moderation Analysis and Regression-based Moderated Regression Analysis techniques were used to assess the discrepancies between the groups. Each technique has its benefits and drawbacks. Multiple statistics techniques are regarded as methodological triangulation. The experiment aims to compare each other’s results and ensure that the most accurate results are obtained. The conceptual model has been successfully validated with data from thousands of ad impressions, and more than 800 ad clicks from thousands of smartphone users in more than 160 countries worldwide, overcoming the challenge that previous researchers had when dealing with mobile traffic data.

The study has found that publishers play a crucial role in mobile in-app advertising and can enhance its effectiveness. It extends our knowledge about the publisher’s role in enhancing mobile in-app advertising’s effectiveness directly and indirectly. Empirically, this research established a new approach for creating multiple ad spaces in a single application and simultaneously testing multiple ad space-related factors. This research also initiated the development of a new metric to measure in-app mobile advertisement effectiveness, taking into account the mobile ads’ duration and size. Practically, this study proposed new integrated techniques for mobile in-app advertising campaigns to further improve its efficacy. By doing so, the study would support rising mobile in-app advertising revenue significantly higher by balancing the benefits of all participants involved.

Nowadays, a buzzword of “Adtech” has been emerging in this field, to illustrate the new trend of combining technology and advertising. Together with Fintech, Proptech, Biotech and Adtech will become a significant branch of Industry 4.0, which will change our society and the life of each of us for the better for many years to come. Adtech will become even more sophisticated and will be able to target consumers, based on behavioural, location, demographic and contextual data on an individual basis in real time. As funding keeps pouring into the Adtech industry, it looks like the future will come sooner than expected. The annoyance of irrelevant interfering ads on smartphones will soon be a problem of the past. That is good news for everyone: consumers, advertisers and of course app publishers. Adtech brings good changes to our society. That is also the purpose of this study. Not only highly applicable to the mobile in-app advertising area, but this study could also extend to other types of advertising where the role of publishers has not been well studied. This study sheds light on online marketing where interactive outcome metrics play a more critical role than ever before. The results could be immediately applied to programmatic advertising on emerging platforms of the web, smart TVs, smartwatches and voice assistants. In that sense, the study could lay the ground for future research in other emerging advertising types when the technology keeps evolving rapidly.