# Marketing communication in the digital age: online ads, online WOM and mobile game adoptions

Shiyang Gong (University of International Business and Economics, Beijing, China)
Wanqin Wang (University of International Business and Economics, Beijing, China)
Qian Li (International Business School, Beijing Foreign Studies University, Beijing, China)

ISSN: 2040-8749

Article publication date: 9 July 2019

Issue publication date: 15 August 2019

## Abstract

### Purpose

This study aims to explore the interdependent impacts of online word-of-mouth (WOM) and online ads on digital product adoptions, as well as their dynamic changes throughout the product life cycle.

### Design/methodology/approach

This study adopted an empirical approach by using a unique data set of five mobile games launched between 2012 and 2014 provided by Renren Games Ltd. in China.

### Findings

The results indicated that advertising generally has a positive impact on WOM. During the product life cycle, the influence on volume and variance gradually decreases, whereas the impact on valence increases over time. WOM (including WOM volume and WOM valence) and advertising both have positive impacts on game adoptions. They complement each other to shape adoptions throughout the product life cycle: advertising is more effective in encouraging adoptions in the early and later stages of the demand evolution process, whereas WOM has a greater impact on adoptions in the mid-stage.

### Practical implications

This study provided detailed managerial recommendations on how to effectively integrate different types of marketing communication and optimize the investment strategy of online ads and online WOM in different stages of the product life cycle.

### Originality/value

First, the study enriched the theory of digital marketing communication by studying the relationship between mass media (online ads), interpersonal media (online WOM) and product adoptions in the network context. Second, it provided an empirical basis for the inference of the dynamic development of media effect in the new product diffusion theory. Third, the results will be helpful to end the debate in current theoretical literature on whether there is a complementary or alternative relationship between the two effects. Last but not least, it enriched research on the antecedents and dynamic effects of online WOM.

## Citation

Gong, S., Wang, W. and Li, Q. (2019), "Marketing communication in the digital age: online ads, online WOM and mobile game adoptions", Nankai Business Review International, Vol. 10 No. 3, pp. 382-407. https://doi.org/10.1108/NBRI-12-2018-0073

## Publisher

:

Emerald Publishing Limited

## 1. Introduction

Consumers usually turn to two types of media to obtain information of products or services when making purchasing decisions: the mass media and the interpersonal media. The internet is playing an increasingly significant role against the backdrop of China’s “Internet plus initiative.” With the emerging of more digitalized and networked products or services, online ads and online word-of-mouth (WOM) have gradually become two most important marketing communication tools representing the two abovementioned media.

Online ads have been preferred by many companies right after the internet came into being. Compared with the traditional offline advertising, the biggest advantage of online advertising is that it can use the network technology to obtain rich consumer information, and thus has a more accurate access to target consumers (Evans, 2008). According to a recent global advertising survey carried out by eMarketer, global advertising spending reached a total amount of $615bn in 2016. Among different types of ad spending, online advertisement has maintained a high growth rate of more than 20 per cent since 2012, reaching a total of$198bn in 2016, which accounted for 32 per cent of global advertising spending.

As social networks and e-commerce develop, the role of online WOM has been attached greater importance by business managers. According to a statistical report, about 20 per cent of the information in social media contains consumers’ WOM comments on brands or products (Jansen et al., 2009). In addition, many e-commerce companies (such as Taobao, Jingdong, Amazon, etc.) and third-party consumer review websites (such as Dianping.com and Douban) have also established online comment systems to encourage consumers to post and share WOM information. It is found in a survey that more than 70 per cent of Chinese consumers take their initiative in looking up the WOM information on these websites when making online shopping decisions (Hu, 2008). Many consumers even refer to the WOM information to reduce risks when shopping offline (Derbaix and Vanhamme, 2003).

In the academia, online ads and online WOM are also interesting topics for researchers (Ambler and Vakratsas, 1999; Godes and Mayzlin, 2004). However, few studies have explored from the perspective of integrated marketing communication, especially the interaction among online ads, online WOM and digital product adoptions, and the dynamic changes of these relationships throughout the product life cycle. Therefore, this study aims to answer the following important questions: how online ads influence online WOM, how online ads and online WOM influence mobile game adoptions and how these two impacts change dynamically over time throughout the product life cycle.

This study adopted a unique data set of five mobile games launched between 2012 and 2014 by Renren Games Ltd, and adopted an empirical approach to study two main questions: the impact of online ads on online WOM and the joint impact of online ads and online WOM on product adoptions. Considering this special research goal, this study established a simultaneous equation model to systematically analyze these two impacts. On this basis, in accordance with the life cycle of mobile game products, the development process of products was divided into three stages in this study, early stage, mid-stage and later stage, and thus the dynamic changes of the two abovementioned impacts over time were further analyzed.

## 2. Literature review and hypothesis

### 2.1 The impact of online marketing communication on product adoptions

According to previous marketing research, the new product diffusion theory first emphasized the important influence of marketing communication and media effect on product adoptions (Bass, 1969; Mahajan et al., 1991; Rogers, 2003). Scholars believe that the diffusion of new products is largely attributed to the transmission of new product information through the mass media (e.g. advertising) and the interpersonal media (e.g. WOM). This theory paved the way for a series of marketing communication research.

In recent years, with the development of the internet, piles of marketing communication research focus on online WOM. Compared with the traditional offline WOM, online WOM has two prominent advantages: on the one hand, consumers can easily generate and look up WOM information online, relieving WOM from limitations of time and space, and greatly enhancing the promotion effect and influence of traditional WOM. On the other hand, it is relatively cheaper to produce and manage WOM once it is put online. Therefore, online WOM can effectively reduce companies’ costs in marketing communication (Chen and Xie, 2008).

Many studies have discussed the relationship between online WOM and product adoptions or corporate performance. In summary, prior research mainly used three dimensions of WOM when studying the impact of online WOM – volume, valence and variance – the volume of WOM measures how widely the WOM information has been spread, usually measured by the total number of online reviews; WOM valence shows whether the information is positive or negative, which is usually measured by the mean value of online ratings or the proportion of positive or negative comments; and the variance of WOM information measures the differences in WOM information, usually measured by online consumer rating variance. Given these three dimensions, relevant studies explored the relationship between online WOM and product adoptions or corporate performance in various industries (Table I), such as television ratings (Godes and Mayzlin, 2004), movie box office (Liu, 2006; Dellarocas et al., 2007; Duan et al., 2008; Chintagunta et al., 2010; Rui et al., 2013; Gopinath et al., 2013), book sales (Chevalier and Mayzlin, 2006; Sun, 2012; Gong et al., 2012, 2013), beer sales (Clemons et al., 2006), stock prices (Luo, 2009), restaurant turnover (Lu and Feng, 2009), game adoptions (Zhu and Zhang, 2010), online retail sales (Moe and Trusov, 2011) and video player sales (Ho-Dac et al., 2013). Recently, Baker et al. (Baker et al., 2016) took a large sample of 804 brands from 55 US industries as the research object to comprehensively analyze the impact of online WOM on consumer purchasing behaviors.

However, existing literature paid little attention to the joint effect of online WOM and other online communication tools (e.g. online advertising) on product sales. In fact, according to Mahajan et al. (1995), consumers have different preferences for different communication media, and this tendency changes with time. For example, Villanueva et al. (2008) used vector autoregressive models to analyze customer value brought about by WOM effects and other traditional marketing communication methods (such as radio, e-mail, etc.). They found that the customer value obtained through different marketing communication methods varies greatly in different stages. Onishi and Manchanda (2012) used a simultaneous equation model to study the impact of blogging activities and advertising on movie box office and found a positive synergy between the two.

This study focused on exploring the joint effect of online WOM and online ads on product sales. According to the summary of Mahajan et al. (1995) on the media effect and new product diffusion theory, different types of communication media (for example, mass media vs interpersonal media) may have different impacts on sales in different stages of the product life cycle. The follow-up question is: Does the dynamic effect of online ads and online WOM, the most common means of mass media and interpersonal media communication under the internet environment, on product sales works the same way? And how does the enterprise-led mass media (online ads) dynamically influence the consumer-led interpersonal media (online WOM)? This study assumed that there is a certain degree of interaction and dynamic linkage among online ads, online WOM and product sales. Figure 1 summarizes the theoretical framework of this study, and shows that the study focused on theoretical deduction in two aspects:

1. How does the investment in online ads affect the development of online WOM (volume, valence and variance), and how does online advertising affect online WOM dynamically in the product life cycle (early stage, mid-stage and later stage); and

2. How do online ads and online WOM affect product sales, and how do online ads and online WOM affect sales in the product lifecycle (early stage, mid-stage and later stage)?

### 2.2 The dynamic impact of online ads on online WOM

Existing research paid little attention to the impact of online ads on online WOM. Gopinath et al. (2013) considered the relationship between the overall expenditure on advertising in various media and online WOM. They found that advertising expenditure in a certain month was positively related to the volume of online WOM next month. This conclusion provided preliminary evidence for the relationship between online ads and online WOM. Recently, Fossen and Schweidel (2016) studied the impact of TV commercials on the WOM volume on social media. They found that television ads helped increase WOM volume of brands and TV programs simultaneously.

This study first analyzed the potential impact of online ads on online WOM from three dimensions (WOM volume, valence and variance). First, Berger and Schwartz (2011) and Lovett et al. (2013) both found that the major drivers for consumers to generate WOM are accessibility and visibility (i.e. whether consumers can easily access and know the products). Many literature on advertising have shown that advertising plays an important role in attracting consumers’ attention and improving product awareness (Aravindakshan and Naik, 2011). Therefore, this study can intuitively infer that online ads have a significant and positive impact on the volume of online WOM. Second, a lot of studies have pointed out that advertising can shape and change consumers’ emotions and attitudes toward products or brands (Miniard et al., 1990; Olney et al., 1991; Brown and Stayman, 1992). Under the influence of advertisements, consumers’ evaluations of products or brands tend to be more positive. Therefore, this study concluded that online ads have a positive effect on the valence of online WOM. Third, Sun (2012) pointed out that the main reason for WOM variance is that consumers in different segmentations have different preferences for products, and thus have different evaluations of products. As a result, product information will reach more consumers in different segmentations when companies’ online ads increase, thus increasing WOM variance. Therefore, this study believed that online ads also have a positive impact on online WOM variance, and put forward the following hypothesis:

H1.

Online ads have a positive effect on volume, valence and variance of online word-of-mouth.

Based on the analysis above, the study put forward the following hypotheses:

H2a.

The impact of online ads on the online word-of-mouth volume is gradually weakened throughout the product life cycle.

H2b.

The impact of online ads on online word-of-mouth valence is increasing throughout the product life cycle.

H2c.

The impact of online ads on online word-of-mouth variance is gradually weakened throughout the product life cycle.

### 2.3 The dynamically changing impacts of online ads and online word-of-mouth on product adoptions

Online ads and online WOM, as two important marketing communication tools, have impacts on product adoptions.

H3a.

Online ads have a significant and positive impact on digital product adoptions.

H3b.

The impact of online ads investment on product adoptions appears in a positive U curve throughout the product life cycle.

Then, this study discussed the impacts of online WOM volume, valence and variance on digital product adoptions.

First, it is generally believed that the impact of online WOM volume on adoptions or sales comes from the awareness effect – the more the WOM is, the more the consumers discussing about the product will be, which make other potential consumers more likely to know the product, and thus improve future product sales. Many empirical studies proved that the volume of online WOM has a positive impact on sales. Taking the movie industry as an example, Liu (2006) and Duan et al. (2008) explored the impact of Yahoo’s movie reviews on box office revenue, and found that the more the comments are, the higher the box office revenue of movies will be. Rui et al. (2013) used Twitter’s tweets to measure WOM and found that WOM volume was positively correlated with box office revenue. Dellarocas et al. (2007) added the volume of WOM to the box office prediction model and found that the prediction accuracy of the model significantly improved. In addition, Godes and Mayzlin (2004) studied the television program industry, Chevalier and Mayzlin (2006), Gong et al. (2012, 2013) studied the book industry, Lu and Feng (2009) studied the restaurant industry, Zhu and Zhang (2010) studied the digital games industry, Moe and Trusov (2011) studied online retailers and Ho-Dac et al. (2013) studied the Blue-ray DVD players. All of these studies supported the positive impact of online WOM volume on adoptions.

Second, for WOM valence, it is generally believed that its impact on adoptions mainly comes from persuasive effect – the more the positive comments toward the product are, the easier the consumers will be persuaded to buy. There were also many empirical studies on the relationship between WOM valence and product adoptions. For example, Duan et al. (2009) found that more than 20 per cent of consumers rank products according to other consumers’ ratings when browsing product information on CNET, which highly indicated that WOM is an important factor influencing consumers’ decision-making. Clemons et al. (2006) analyzed the role of WOM in the beer industry and found WOM valence positively correlated with beer sales, and the positive (negative) influence of the first-shown 25 per cent of WOM could best interpret the sales volume. Chintagunta et al. (2010) extended the WOM study in the film industry to multiple regional markets and pointed out that WOM valence is the most important factor affecting box office revenue. Ho-Dac et al. (2013) found that WOM valence only had an impact on products with lower brand equity, instead of products with higher brand equity. Baker et al. (2016) compared the influence of online and offline WOM valence on purchase predisposition and found that offline WOM valence had a greater impact on adoptions than the online one.

Third, there are two opposite views toward the impact of WOM variance on product adoptions. The first view is that WOM variance has a positive impact on sales. Martin et al. (2007), for example, held that high WOM variance would arouse consumers’ curiosity and drive them to purchase. Sun (2012) pointed out that products with high WOM variance are more likely to be preferred by minority consumers. The other view is, for products with higher WOM variance, consumers will perceive greater purchase risks and be less driven to buy the products. Findings of Meyer (1981) supported this view. He found that consumers will lower their evaluation of goods to adapt to inconsistent WOM. Zhang (2006) made an empirical analysis of 128 films released in 2004, and found that in the early stage of the film release, the higher the WOM variance is, the faster the box office revenue decreases.

Based on the above analysis and taking the research scenario into consideration, this study put forward the following hypothesis:

H4a.

Word-of-mouth volume and valence have a significant positive impact on digital product adoptions, but WOM variance does not necessarily have a significant impact on digital product adoptions.

For the dynamic relationship between online WOM and adoptions or sales, Liu (2006), Trusov et al. (2009) and Moe and Trusov (2011) found that the effect of online WOM also appears in a dynamically upward or downward trend throughout the product life cycle. Relevant research has put forward various viewpoints to explain this phenomenon. Mahajan et al. (1991) argued that most consumers are “innovators” or “experts” with extensive experience in the early stage of a product launch, whereas “followers” or “green hands” that are not familiar with the product tend to buy after a certain period of time. Among these four types of people, “followers” or “green hands” are far less good at distinguishing good products from bad ones compared with “innovators” or “experts,” and thus rely more on WOM. As the proportion of “followers” or “green hands” in consumers increases, the impact of WOM will change. Li and Hitt (53) held that consumers generating WOM are subject to strong self-selection bias. As consumers may have different preferences for products during different periods, WOM volume and valence will increase or decrease dynamically, which will further influence consumers’ purchasing behaviors. Moe and Trusov (2011) found that the dynamic changes in WOM effect in the product life cycle mainly come from the social influence of WOM generators. WOM produced at the beginning of a product launch will have a significant impact on WOM produced later, and have further impact on sales.

Given the above findings, this study assumed that the impact of online WOM on adoptions in the product life cycle appears in an inverted U-shaped curve over time. The impact of online WOM on adoptions is relatively weak in the beginning, increases and peaks in the mid-stage, and declines in the later stage. The explanation to this phenomenon is that, in the earlier product life cycle, there is only little WOM information with low volume, unstable valence and high variance. Early consumers are thus more likely to obtain product information from ads and make purchasing decisions based on their own judgments. Entering the mid-stage, WOM begins to enrich, and is gradually regarded as a more reliable source of information than advertising by consumers. New consumers also usually follow the earlier consumers’ WOM when making purchase decisions. In the later stage, WOM information has been well known to consumers, so its impact will decline. Thus, this study put forward the following hypothesis:

H4b.

The impact of online word-of-mouth (the overall effect of volume, valence and variance) on adoptions appears in an inverted U-shaped curve throughout the product life cycle.

## 3. Methodology

### 3.1 Data collection

Data in this paper were provided by Renren Game Company. Founded in 2006, Renren Game is a leading company in R&D and operation of mobile games in China.

First, the study obtained the data of five mobile game products released by Renren Game Company from 2012 to 2014, including the release time of the game, daily downloads, daily online advertising cost and so on. The five products belong to two of the most popular game types in the market at that time – three are role-playing games (RPG) and two are strategy simulation games (SLG)[1]. The games were launched in Taiwan and Southeast Asia (including the Philippines, Malaysia, Thailand, Singapore and Vietnam). There are great differences in the development of the mobile game industry between the two regions, and Taiwan has a higher coverage rate of smartphones than Southeast Asia[2]. By choosing these two regions as research objects, both developed and underdeveloped markets can be studied to make the conclusions more universally applicable. In addition, considering the different purchasing powers in these two regions, the study converted the advertising investment cost according to the purchasing power parity index to adapt to different situations[3].

Second, the online WOM data of the five games were collected by Renren Game Company through self-developed crawler software. Main sources of the WOM data were consumer reviews in Apple Store and Google Play. Promotion and adoptions channels of mobile games are relatively concentrated in Taiwan and Southeast Asia. As Apple Store and Google Play are the two most popular channels, most WOM information is from these two app stores. Taking one game in the study as an example, it had 5,376 online reviews in Apple Store and Google Play, and only 36 and 21 online reviews on Facebook’s homepage and Renren Game Forum during the same time period. Therefore, reviews from the Apple Store and Google Play are very representative. Reviews in these two app stores consist of two parts:

1. star rating (1 to 5 stars). More stars represent higher evaluation of the consumer; and

2. comment (a short text review commonly includes user experience and opinions).

When the consumer completes the review, the page will update in real time and show the statistics of reviews, including the number of reviews, average ratings, the ratio of 1 to 5 stars, and the specific ratings and text reviews of every consumer.

In addition, descriptive statistics were also made by region and game type based on the data collected, and the results were shown in Table III. First, the research found that adoptions in Taiwan were lower than those in Southeast Asia, but the user acquisition input was significantly higher than that in Southeast Asia, which was reflected in the ratio of advertising investment to game adoptions. This phenomenon showed that in Taiwan, with a more developed mobile game industry, the competition between game companies and users was more intense, so the cost of advertising was higher. Other dimensions of WOM data did not vary much between Taiwan and Southeast Asia. From the perspective of game type, SLG were obviously more popular with game companies and consumers than RPG. On the one hand, Renren Game spent twice as much on SLG as RPG. On the other hand, adoptions, WOM volume and the proportion of high-scored WOM of SLG were also significantly higher than those of RPG.

The difference in game adoptions between Taiwan and Southeast Asia was likely to be caused by the marketing and communication activities of companies, or by local cultural, technological, economic and other macro factors. Therefore, the research further collected a series of macro factors (Table IV) that might have an impact on game adoptions and tried to find out the relationship between macro factors and game adoptions. The research found that internet penetration (Internet user ratio), economic development (GDP per capita) and education development degree (literacy rate of population aged above 15) were inversely related to game adoptions in Taiwan and Southeast Asia, and the number of internet users (Internet broadband access number) and the population of adolescents (aged 10-24) were consistent with the number of downloads, but the difference between these numbers was much greater than that of the two adoptions numbers in the two regions. Therefore, from the numerical and logical perspectives, these macro factors were unlikely to become main factors that affect game adoptions.

### 3.2 Key variables

According to the raw data provided by Renren Game Company, we collected variables for the empirical analysis. Table V summarized and illustrated the variables. It is noteworthy that the linear transformation of natural logarithms was performed for continuous variables with values greater than 0. By doing this, the study can reduce heteroskedasticity and control the influence of outliers.

Dependent variable: In this study, adoptions of mobile games were measured by game downloads. Therefore, we performed natural logarithmic transformation for the dependent variable, namely downloads per day per game.

Independent variables: Independent variables of this paper included online ads and online WOM. First, advertising investment was measured by the actual advertising cost per day per game. As mentioned earlier, as there were two regions involved in the sample, Taiwan and Southeast Asia, the research adjusted their advertising investment according to the local PPP index, and then took the natural logarithm. Then, the research calculated variables related to online WOM based on consumer reviews from Apple Store and the Google Play. Among them, WOM volume was the sum of the number of reviews received in the two stores per day per game; WOM valence was the average scores per day per game in the two stores; and WOM variance was the variance of the scores per day per game in two stores. Natural logarithm transformation was taken for these three WOM variables.

Figure 2 showed the relationship between part of independent variables and the dependent variable. The top-down curves represented advertising investment, game adoptions and WOM volume, respectively. In Figure 2, the three curves shared a strict consistency in both the overall trend and fluctuations. It could be preliminarily inferred that advertising investment and online WOM volume have an impact on game adoptions.

In addition, as WOM might also be influenced by advertising investment, the research showed the relationship between advertising investment and WOM volume [Figure 3(a)], WOM valence [Figure 3(b)] and WOM variance [Figure 3(c)] in Figure 3. The dotted line in Figure 3 represented advertising investment, whereas the solid lines represented WOM volume, WOM valence, and WOM variance respectively. In the graphs, the three curves representing WOM values and the curve representing advertising investment showed strong consistency.

Control variables: To control the potential impact of factors other than advertising and WOM, the research calculated a series of control variables. First, as mobile phone games were released in two regions (Taiwan and Southeast Asia), and there were great differences in the mobile game markets of the two regions, so there might also be differences in advertising and WOM effects. To control the potential impact of regional differences, the research used regional dummy variables to distinguish Taiwan from Southeast Asia. Second, game types might also have an impact on game adoptions. Game types in the data were mainly divided into role-playing games and strategy simulation games, which covered more than 80 per cent of the popular games in the market[4]. The popularity of different types of mobile games among consumers might vary because of different themes, highlights and operating rules. Therefore, the study used game-type dummy variables to distinguish between RPG and SLG to control the impact of game type on results. Third, since the times of game release varied much, seasonal factors such as holidays and winter and summer vacations might result in differences in game adoptions. Therefore, the research used quarterly dummy variables to control the impact of time factors on game adoptions. Specifically, three dummy variables were generated based on the first quarter (January-March), representing the second quarter (March-June), the third quarter (June-September) and the fourth quarter (September-December).

## 4. Empirical model

### 4.1 Model specification

This paper took two impacts into consideration when building the empirical model: the impact of advertising on WOM and the joint impact of advertising and WOM on game adoptions. As the two effects exist simultaneously and are inter-related, the most ideal analysis method is to consider the two effects simultaneously by establishing simultaneous equation models (Greene, 2008). Based on the model of Onishi and Manchanda (2012), this research proposed the following linear simultaneous equations model:

In the model above, subscript i: 1,… N stands for games, subscript t: 1,… N stands for time.

This study used two-stage least squares to estimate the simultaneous equation model above. In the first stage, this study used exogenous variables lnAdit, lagged variables lnVolit−1, lnValit−1, lnVarit−1 and control variables Geoi, Genrei and Seat to estimate the WOM variables lnVolit, lnValit and lnVarit in equations (1) to (3) to analyze the impact of online advertising on online WOM. In the second stage, this study used the predicted values of the first stage WOM variables to estimate equation (4) and investigate the joint impact of online WOM and online advertising on sales or adoptions. Since both dependent variables and independent variables were transformed by natural logarithms, the estimates of regression coefficients, i.e. the effect of independent variable variance ratio on the dependent variable variance ratio, were elastic.

### 4.2 Impact of online ads on online word-of-mouth and dynamic changes

To analyze the overall impact of online advertising on online WOM, this study used the overall sample to perform regression analysis of Equations (1)-(3). Table VI showed the results of regression analysis. The goodness of fit of the model, R2, was 0.75, 0.48 and 0.33, respectively, which showed that advertising investment best explained WOM volume, followed by WOM valence, and then WOM variance. Overall, the fitting results of all models were very significant.

The results in Table VI Column (1) reflect the impact of advertising investment on WOM volume. The coefficient of lnAdit was positively significant (b1 = 0.026, p <0.01), indicating that the more the advertising investment was, the more WOM volume obtained by the game would be. In addition, early WOM also had an impact on WOM volume. The coefficients of lnVolit−1 and lnValit−1 were both positive and significant (b2 = 0.857, p <0.01; b3 = 0.110, p <0.01), indicating that the more WOM volume and the better WOM valence were, the more WOM volume would be obtained in the current period. The coefficient of lnVarit−1 was negative and significant (b4 = –0.152, p <0.01), indicating that the smaller WOM variance was in the earlier period, the smaller WOM volume would be in the current period.

The results in Table VI Column (2) reflect the impact of advertising investment on WOM valence. The coefficient of lnAdit was positive and significant (c1 = 0.015, p <0.01), indicating that the more the advertising investment was, the higher the WOM valence would be. In addition, the coefficients of lnVolit−1 and lnValit−1 were both positive and significant (c2 = 0.165, p <0.01; c3 = 0.352, p <0.01), indicating that the more WOM volume and better WOM valence were, the higher the game’s WOM valence would be in the current period. The coefficient of lnVarit−1 was not significant (c4 = –0.000, p >0.10), indicating that the earlier WOM variance had no significant effect on the WOM valence in the current period.

The results in Table VI Column (3) reflect the impact of advertising investment on WOM variance. The coefficient of lnAdit was positive and significant (d1 = 0.024, p <0.01), indicating that the more the advertising investment was, the greater the WOM variance would be. In addition, the coefficients of lnVolit−1, lnValit−1 and lnVarit−1 were all positive and significant (d2 = 0.129, p <0.01; d3 = 0.073, p <0.01; d4 = 0.110, p <0.01), indicating that the volume, valence and variance of WOM in the earlier period had positive effects on WOM variance in the current period.

Overall, the above regression results supported H1 proposed earlier.

Then, to study the dynamic changes in the impact of online advertising on online WOM, the study divided all samples into several sub-samples in different stages of the product life cycle, and then made regression analysis on each sub-sample.

Table VII showed the average life cycle of mobile games provided by Renren Game Company[5]. According to relevant literature (Zhu and Zhang, 2010), the life cycle of game products has two characteristics: firstly, the whole life cycle of a game is usually relatively short, and the update and iteration speed is very fast; secondly, the number of users of a new game will accumulate rapidly after product launch, and the product will pass the early stage and enter the mid-stage quickly. As you can see from the table, the average life cycle of mobile games is about one year. After data validation, the length of this life cycle also conforms to life cycles of the five mobile games analyzed in this study. As mentioned earlier, mobile games’ life cycle is characterized by a rapid transition from introduction to maturity, so this study divided the life cycle into three stages: the early stage, the mid-stage and the later stage. The specific criteria are as follows.

Early stage: About one month starting from the game launch to the end of the first month. During the early stage, early users of the game begin to accumulate. Game companies usually increase advertising awareness at this stage through increasing advertising investment. At the same time, this is the initial stage of Online WOM.

Mid-stage: About five months starting from the second month to the sixth month after game launch. During the mid-stage, the user number and activity of the game increase rapidly and reach the peak. The advertising investment of game companies gradually decreases, WOM volume increases rapidly, and WOM valence becomes more stable.

Later stage: Usually six months starting from the seventh month to game exiting from the market. During the later stage, users of games begin to decrease, and user activities decline significantly. Game companies restart a certain amount of advertising investment. Increase of WOM volume slows down, and WOM valence almost doesn’t change any longer.

Based on the above criteria, this study divided the overall sample into three sub-samples: samples at early stage, mid-stage and later stage. The study also used two-stage least squares regression method[6] to process the sub-samples. Table VIII showed the regression results from Equations (1)-(3). Among them, the dependent variable in Columns (1)-(3) is lnVolit, corresponding to equation (1) in the model; the dependent variable in Columns (4)-(6) is lnValit, corresponding to equation (2) in the model; the dependent variable in Columns (7)-(9) is lnVarit, corresponding to equation (3) in the model. In Table VIII, this study focused on the estimated coefficients of the variable lnAdit in each column, because the variation of lnAdit coefficients could reflect dynamic changes in the impact of advertising on WOM in different stages of the game’s life cycle.

The regression results from Columns (1)-(3) reflect the dynamic impact of online advertising on WOM volume. By comparing the regression results of the three columns, this study found that the positive effect of advertising on WOM volume decreased gradually as the life cycle developed. The estimated coefficient of lnAdit was 0.042 (p <0.01) in the early stage, 0.028 (p <0.01) in the mid-stage and 0.012 (p <0.01) in the later stage. The result supported H2a proposed earlier.

The regression results from Columns (4)-(6) reflect the dynamic impact of online advertising on WOM valence. This study found that the positive effect of advertising on WOM valence was gradually strengthened as the life cycle developed, which is just opposite to the result of WOM volume. In the early stage, the estimated coefficient of lnAdit was 0.009, and only marginally significant (p <0.10); in the mid-stage, the estimated coefficient of lnAdit increased to 0.017 (p <0.01); and in the later stage, the estimated coefficient of lnAdit further increased to 0.026 (p <0.01). The result supported H2b proposed earlier.

The regression results from Columns (7)-(9) reflect the dynamic impact of online advertising on WOM variance. This study found that the impact of advertising on WOM variance also gradually diminished as the life cycle developed. The estimated coefficient of lnAdit was 0.034 (p <0.01) in the early stage, 0.024 (p <0.01) in the mid-stage and 0.018 (p <0.01) in the later stage. The result supported H2c proposed earlier.

Figure 4 showed the dynamic impact of online advertising on online WOM more clearly. Throughout the product life cycle, the impact of advertising on WOM volume and WOM variance gradually weakened, yet the impact on WOM valence gradually strengthened.

### 4.3 Impact of online ads and online word-of-mouth on product adoptions and dynamic changes

To analyze the overall impact of online advertising and online WOM on adoptions, this study used the overall sample to make regression analysis of equation (4), and the results are shown in Table IX[7]. The study used stepwise regression method to analyze equation (4). In the beginning, this study only made regression analysis of dependent variables and control variables. Then, on this basis, the study gradually added in the regression analysis of the independent variable related to advertising and WOM.

Table IX Column (5) shows the regression results of the complete model, which illustrated the relationship between variables. First, this study found that the lnAdit coefficient was positive and significant (a1 = 0.066, p <0.01), indicating that the more the advertising investment was, the higher the number of downloads would be. Second, the coefficients of lnVolit and lnValit were both positive and significant (a2 = 0.105, p <0.01; a3 = 0.149, p <0.10), indicating that the volume and valence of WOM both had a significant and positive impact on game adoptions, while the coefficient of lnVarit was negative and insignificant (a4 = 0.126, p >0.10), indicating that WOM variance didn’t have a significant impact on game adoptions. Regression results above supported H3a and H4a proposed earlier.

In addition, for control variables, the coefficient of Geoi was very small and insignificant (a5 = 0.039, p >0.10), indicating that there was no significant difference in game adoptions between Taiwan and Southeast Asia; the coefficient of Genrei was significant and positive (a6 = 0.142, p <0.01), indicating that role-playing games were more popular with consumers than strategy simulation games. As Geoi coefficient was not significant, this study removed this variable, and made another regression analysis of the model to see whether the results would change. Column (6) shows the regression results after Geoi was kicked out. Overall, the coefficient estimates of variables did not change significantly.

To study the dynamic changes of the impact of online advertising and online WOM on game adoptions, this study divided the overall sample into three sub-samples, namely samples at early stage, mid-stage and later stage, and then made regression analysis of each sub-sample. Table X shows the relevant regression results[8].

The regression results in Table X show that advertising had a significant and positive impact on game adoptions throughout the life cycle, which was consistent with the regression results in Table IV. The coefficient of lnAdit was 0.157 (p <0.01) in the introducing stage, 0.056 (p <0.01) in the mature stage and 0.166 (p <0.01) in the declining stage, which showed that, throughout the product life cycle, the impact of online advertising on game adoptions was a positive U-shaped curve. This result supported H3b proposed earlier.

In addition, online WOM variables played different roles in different stages of the game life cycle. In the early stage, the coefficient of lnVolit was positive and significant (a2 = 0.842, p <0.01), whereas the coefficients of lnValit and lnVari were both insignificant (a3 = –0.724, p >0.10; a4 = 0.736, p >0.10), indicating that only WOM volume had an impact on game adoptions in the early stage. In the mature stage, coefficients of lnVolit, lnValit and lnVarit were all positive and significant (a2 = 0.237, p <0.01; a3 = 1.457, p <0.01; a4 = 0.170, p <0.01), indicating that the overall impact of WOM on game adoptions increased significantly. In the later stage, only lnVolit coefficient was positive and significant (a2 = 0.517, p <0.05), whereas lnValit and lnVari coefficients were insignificant (a3 = –0.242, p >0.10; a4 = –0.192, p >0.10), indicating that the overall impact of WOM on game adoptions was also declining. This showed that throughout the product life cycle, the impact of online WOM game adoptions was an inverted U-shaped curve, which supported H4b proposed earlier.

## 5. Conclusion and discussion

### 5.1 Conclusion

This study discussed the dynamically changing impacts of online WOM and online ads on product adoptions from the perspective of integrated marketing communication. A simultaneous equation model was built using data collected from five mobile games launched by Renren Game Company from 2012 to 2014. Based on the analysis of the model, the research drew the following conclusions:

First, the research studied the impact of online ads on online WOM, and dynamic changes in the impact. As the first step, the study made a regression analysis of the overall sample. Regression results indicated that online ads had a significant and positive impact on WOM volume, WOM valence and WOM variance. In the second step was, the study divided the sample into three sub-samples in accordance with the mobile game life cycle: the early stage, the mid-stage and the later stage. Results of the regression analysis of sub-samples indicated that the influence of online ads on WOM volume and WOM variance gradually weakened, but the influence on WOM valence gradually strengthened.

### 5.2 Managerial implications

Online ads and online WOM are the two most representative marketing communication methods in the digital era. It is a major concern for managers to effectively integrate different types of marketing communication tools and carry out targeted marketing communication in different stages of the product life cycle. According to the conclusion of this study, this paper offered three marketing suggestions for companies:

Third, companies should ensure the sustainability of online ads and online WOM investment throughout the product life cycle, so as to achieve sustainable marketing communication effect. The research found that online ads and WOM have significant impacts on product adoptions throughout the product life cycle. In addition, the volume of advertising investment and WOM volume in the early stage will have different degrees of positive impacts on the follow-up marketing and communication. Therefore, companies should consider the sustainability of online advertising investment and online WOM management when making marketing communication strategies. Marketing managers should also adjust strategies of advertising investment and online WOM management in a planned and sustainable way according to the characteristics of different marketing communication tools in different stages of the product life cycle, so as to generate a cumulative effect and achieve better marketing outcomes.

## Figures

#### Figure 1.

Conceptual framework

#### Figure 3.

Relationship between online ads and online WOM

#### Figure 4.

Dynamic impact of online ads on online WOM

## Table I.

Relevant literature and incremental contributions of this study

Author(s) Research domain Key variables of WOM
Godes and Mayzlin (2004) TV program Volume, dispersion
Liu (2006) Movie Volume
Chevalier and Mayzlin (2006) Book Volume, valence
Clemons et al. (2006) Beer Valence, variance
Dellarocas et al. (2007) Movie Volume, valence
Duan et al. (2008) Movie Volume
Luo (2009) Stock Valence
Lu and Feng (2009) Restaurant Volume, valence
Chintagunta et al. (2010) Movie Valence, variance
Zhu and Zhang (2010) Game Volume, valence, variance
Moe and Trusov (2011) Online retail Volume, valence, variance
Sun (2012) Book Valence, variance
Gong et al. (2012, 2013) Book Volume, valence, variance
Rui et al. (2013) Movie Volume, valence
Gopinath et al. (2013) Movie Volume, valence
Ho-Dac et al. (2013) Blue-ray DVD player Volume, valence
Baker et al. (2016) Various industries Valence

## Table II.

Descriptive statistics

Variables Mean SD Minimum Median Maximum
Adoptions 872 1,592 3 201 22,396
Advertising investment 14,381 34,808 0 0 936,098
WOM volume 4.50 13.9 0 1 360
WOM valence 3.64 1.22 1 4 5
WOM variance 1.78 2.14 0 1 8
Note:

2,497 samples in total

## Table III.

Descriptive statistics (by different regions and game types)

Variables Taiwan, China Southeast Asia SLG RPG
Advertising investment 16,775 11,595 22,851 10,671
WOM volume 4.33 4.70 9.49 2.32
WOM valence 3.60 3.68 3.63 3.65
WOM variance 1.67 1.90 2.15 1.44
Proportion of high-scored WOM (>4 stars) (%) 67.3 62.4 77.3 56.4
Sample volume 1,342 1,153 760 1,735
Note:

The data in the table are shown in terms of mean value

## Table IV.

Macro-economic and demographic factors (by difference regions)

Factors Variables Taiwan, China Southeast Asia
Internet penetration Internet user ratio (%) 80.00 51.56
Internet user number Internet Broadband access number (Million) 7.0 23.6
Youth population Population aged 10-24 (Million) 4.5 78.2
Economic development GDP per capita (Dollar) 41,538 25,590
Education development Literacy rate of population aged above 15 (%) 98.50 95.78
Notes:

Southeast Asia includes five countries: the Philippines, Malaysia, Thailand, Singapore and Vietnam. The numbers of internet penetration, internet cost, economic development and education development in Southeast Asia were mean values, whereas the numbers of internet users and adolescents were sums

## Table V.

Key variables

Variables Meaning Descriptions
lnDlit Adoptions per day per game After natural logarithmic transformation
lnVolit Online WOM volume per game per day After natural logarithmic transformation
lnValit Online WOM valence per game per day After natural logarithmic transformation
lnVarit Online WOM variance per game per day After natural logarithmic transformation
Geoi Region dummy variable Southeast Asia = 0, Taiwan = 1
Genrei Game type dummy variable SLG = 0, RPG = 1
Seat Quarterly dummy variable Three dummy variables were generated based on the first quarter (January-March), representing the second quarter (March-June), the third quarter (June-September) and the fourth quarter (September-December).

## Table VI.

Impacts of online ads on online WOM

Variables DV: lnVolit (1) DV: lnValit (2) DV: lnVarit (3)
lnAdit 0.026 (0.003)*** 0.015 (0.003)*** 0.024 (0.003)***
lnVolit−1 0.857 (0.018)*** 0.165 (0.019)*** 0.129 (0.018)***
lnValit−1 0.110 (0.022)*** 0.352 (0.023)*** 0.073 (0.022)***
lnVarit−1 −0.152 (0.022)*** −0.000 (0.022) 0.110 (0.021)***
Geoi −0.026 (0.022) 0.012 (0.023) −0.052 (0.021)**
Genrei −0.224 (0.028)*** −0.233 (0.029)*** −0.226 (0.028)***
Seat Yes Yes Yes
Constant term 0.296 (0.033)*** 0.400 (0.034)*** 0.269 (0.032)***
F value 1,781.69*** 430.32*** 77.27***
R2 0.75 0.48 0.33
Sample volume 2,497 2,497 2,497
Notes:

Values in brackets were standard errors; **p <0.05 and ***p <0.01

## Table VII.

Typical life cycle of mobile games

Game type Average life Early stage Mid-stage Later stage
Mobile game 1 Year 1 Month 5 Months 6 Months

## Table VIII.

Dynamic impact of online ads on online WOM

Variables DV: lnVolit DV: lnValit DV: lnVarit
(1) Early stage (2) Mid-stage (3) Later stage (4) Early stage (5) Mid-stage (6) Later stage (7) Early stage (8) Mid-stage (9) Later stage
lnAdit 0.042 (0.010)*** 0.028 (0.004)*** 0.012 (0.005)** 0.009 (0.005)* 0.017 (0.004)*** 0.026 (0.009)*** 0.034 (0.008)*** 0.024 (0.004)*** 0.018 (0.006)***
lnVolit−1 0.743 (0.060)*** 0.817 (0.026)*** 0.343 (0.056)*** 0.061 (0.034)* 0.184 (0.024)*** 0.210 (0.076)*** −0.085 (0.044)* 0.112 (0.025)*** 0.130 (0.063)**
lnValit−1 −0.038 (0.116) 0.007 (0.036) 0.041 (0.037) 0.578 (0.066)*** 0.372 (0.033)*** 0.192 (0.051)*** 0.250 (0.087)*** 0.147 (0.035)*** 0.012 (0.042)
lnVarit−1 −0.196** (0.081) −0.156 (0.031)*** −0.057 (0.033)* −0.038 (0.046) −0.006 (0.028) −0.031 (0.045) 0.131 (0.061)** 0.115 (0.030)*** 0.035 (0.037)
Geoi −0.070 (0.076) −0.026 (0.034) 0.064 (0.034)* −0.023 (0.043) 0.038 (0.032) 0.109 (0.046)** −0.129 (0.058)** −0.081 (0.034)*** 0.013 (0.038)
Genrei −0.612 (0.117)*** −0.093 (0.040)** −0.271 (0.046)*** −0.362 (0.066)*** −0.154 (0.038)*** −0.467 (0.063)*** −0.592 (0.088)*** −0.162 (0.039)*** −0.168 (0.052)***
Seat Yes Yes Yes Yes Yes Yes Yes Yes Yes
Constant term 0.292 (0.201) 0.243 (0.052)*** 0.317 (0.047)*** 0.246 (0.113)** 0.374 (0.049)*** 0.551 (0.064)** 0.409 (0.151)*** 0.275 (0.051)*** 0.195 (0.053)***
F value 201.14*** 411.58*** 56.42*** 162.91*** 161.40*** 48.69*** 48.39*** 76.51*** 11.09***
R2 0.87 0.75 0.34 0.84 0.54 0.30 0.61 0.35 0.08
Sample volume 2,700 1,237 990 270 1,237 990 270 1,237 990
Notes:

Value in brackets were standard errors, *p <0.10, **p <0.05, ***p <0.01

## Table IX.

Variables (1) (2) (3) (4) (5) (6)
lnAdit 0.068 (0.004)*** 0.064 (0.004)*** 0.064 (0.004)*** 0.066 (0.005)*** 0.064 (0.005)***
lnVolit 0.132 (0.024)*** 0.093 (0.033)*** 0.105 (0.038)*** 0.098 (0.037)***
lnValit 0.110 (0.065)* 0.149 (0.090)* 0.104 (0.051)*
lnVarit −0.126 (0.201) −0.007 (0.180)
Geoi −0.019 (0.027) −0.035 (0.026) −0.027 (0.025) −0.031 (0.025) −0.039 (0.028)
Genrei −0.127 (0.031)*** 0.040 (0.031) 0.133 (0.034)*** 0.163 (0.036)*** 0.142 (0.051)*** 0.161 (0.049)***
lnDlit−1 0.884 (0.008)*** 0.783 (0.010)*** 0.746 (0.012)*** 0.738 (0.012)*** 0.740 (0.013)*** 0.739 (0.013)***
Seat Yes Yes Yes Yes Yes Yes
Constant term 0.644 (0.056)*** 0.872 (0.054)*** 0.916 (0.053)*** 0.891 (0.055)*** 0.740 (0.013)*** 0.875 (0.054)***
F value 2,349.68*** 2,332.75*** 2,152.63*** 1,916.26*** 1,698.11*** 1,915.65***
R2 0.85 0.87 0.87 0.87 0.87 0.87
Sample volume 2,497 2,497 2,497 2,497 2,497 2,479
Notes:

lnDlit, namely, downloads of a single game in a single day, was the dependent variable. Values in brackets were standard errors, *p <0.10, ***p <0.01

## Table X.

Variables (1) Early stage (2) Mid-stage (3) Later stage
lnAdit 0.157 (0.032)*** 0.056 (0.006)*** 0.166 (0.007)***
lnVolit 0.842 (0.251)*** 0.237 (0.031)*** 0.517 (0.231)**
lnValit −0.724 (0.600) 1.457 (0.141)*** −0.242 (0.213)
lnVarit 0.736 (0.811) 0.170 (0.022)*** −0.192 (0.491)
Genrei 0.882 (0.557) 0.168 (0.054)*** 0.036 (0.082)
lnDlit−1 0.403 (0.041)*** 0.743 (0.024)*** 0.904 (0.024)***
Seat Yes Yes Yes
Constant term 1.815 (0.411)*** 0.817 (0.090)*** 0.328 (0.070)***
F value 70.35*** 1011.89*** 1403.24***
R2 0.70 0.88 0.93
Sample volume 270 1,237 990

Notes: lnDlit, namely, downloads of a single game in a single day, was the dependent variable. Values in brackets were standard errors; **p <0.05, ***p <0.01

## Notes

1.

This study has reached a confidentiality agreement with Renren Game Company, so the names of the five games could not be revealed in this article.

3.

According to statistics released by International Monetary Fund (IMF) in 2011, based on the US dollar purchasing power = 1 as the purchasing power parity index benchmark, China (RMB) was 1.550, Taiwan (TWD) was 1.879, the Philippines (PHP) was 1.837, Thailand (Thai baht) was 1.742, Malaysia (Ringgit) was 1.666, Singapore (Singapore dollar) was 1.213 and Vietnam (VND) was 2.447. First, we calculated the average purchasing power parity index of currencies in Southeast Asia. SoutheastAsianCurrency=(1.837+1.742+1.666+1.213+2.447)5=1.781.Then, the advertising investment cost was converted according to the purchasing power parity index of the corresponding region, that is, the advertising investment cost. ( afterconverting)=AdvertisinginvestmentbeforeconvertingxLocalcurrencyindexRMB index

4.

Statistics were from results of internal market research provided by Renren Game Company.

5.

Statistics from Renren Game Company on the company’s and domestic mobile game products.

6.

Another regression method is to introduce dummy variables representing different stages (early stage, mid-stage and later stage) in the same regression model, and then set up an interactive term between dummy variables and independent variables to observe and compare the coefficients in different stages. This study also used this method for robustness test and got the same result. Due to the limitation of length, the results of robustness test were not included in the article, and readers interested in the robustness testing are welcome to write to us and ask for results.

7.

Here, we used the dependent variable and independent variables in the same period to conduct regression analysis. In addition, considering that the impact of advertising and WOM may be hysteretic, this study also used the independent variable of lag1 variable to make a regression analysis of equation (4) to verify the robustness of the regression results. The regression results stayed consistent.

8.

This study used the model in Column (6) of Table IX for regression analysis, which removed the dummy variable Geoi because it was insignificant.

9.

In the study, the value of WOM effect was the sum of the coefficients of lnVolit., lnValit and lnVarit, aiming to reflect the cumulative effect of these three dimensions of online WOM. Since the estimation results of the coefficients of lnValit and lnVarit were not significant, this study set the values of these two coefficients to 0.

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