An Empirical Study of the Drivers of Consumer Acceptance of Mobile Advertising

Marko Merisavo, Sami Kajalo

Helsinki School of Economics, Finland

Heikki Karjaluoto

University of Oulu, Finland

Ville Virtanen, Sami Salmenkivi, Mika Raulas

Helsinki School of Economics, Finland

Matti Leppäniemi

University of Oulu, Finland


The ubiquity of text messaging (SMS) based mobile communication creates new opportunities for marketers. However, the factors that induce consumers to accept mobile devices as an advertising medium are not yet fully understood. This paper examines the drivers of consumer acceptance of SMS-based mobile advertising. A conceptual model and hypotheses are tested with a sample of 4,062 Finnish mobile phone users. Structural equation modeling is used to test five drivers of mobile advertising acceptance: (1) utility, (2) context, (3) control, (4) sacrifice, and (5) trust. The results show that utility and context are the strongest positive drivers, while sacrifice is negatively related to the acceptance of mobile advertising. Despite the concerns about privacy, our results indicate that control and trust are not that important to consumers in mobile advertising.


Consumer adoption of digital mobile telecommunications has in most countries been even faster than that of the Internet (Perlado and Barwise 2005). The number of mobile subscriptions worldwide was 2.4 billion by the end of the second quarter of 2006 (GSM Association 2006).  According to OECD, there is nearly one mobile phone per person in much of the developed world (Economist 2005). In Western Europe, mobile phone penetration reached 90% by the end of 2004, and was forecasted to reach 100% by the end of 2007. At the end of 2004, penetration already exceeded 100% in several countries including Italy, Sweden, and the United Kingdom, as consumers own multiple phones and/or SIM (Subscriber Identity Module) cards (Analysys 2005). Despite the high penetration of personal computers and broadband Internet access, the United States has been slower to adopt mobile phones than Europe (Economist 2005). According to CTIA (2005), mobile phone penetration in the United States was over 65% of the population in 2005. In Finland, the target country of this study, the penetration rate was 103% at the end of 2005 (Ministry of Transport and Communications of Finland 2006).

The majority of mobile phones are capable of sending and receiving text via mobile short message service (SMS) transmission. Text messaging is very popular; 49% of European Internet users frequently send SMS to their friends and family (Smith, Husson, and Mulligan 2005a). In the United Kingdom, for instance, 2.13 billion person-to-person text messages were sent in September 2004 (Mobile Data Association 2004). In Finland, the average number of SMS messages sent per month per one mobile phone subscription in 2004 was 37 (Ministry of Transport and Communications of Finland 2005).  The ubiquity of SMS-based mobile communications creates new opportunities for marketers to advertise, build, and develop customer relationships, and receive direct response from customers (Sultan 2005). Up to this point, mobile advertising has mostly been carried out by mobile operators and, to a lesser degree, by consumer brands (Virtanen, Bragge, and Tuunanen 2005).

Even though we focus on SMS-based mobile advertising in this paper, mobile advertising as a concept is much broader. New applications and services linked to mobile phones, such as multimedia messaging (MMS), games, music, and digital photography, have emerged and are already being used by some marketers. However, according to JupiterResearch, SMS messages were the dominant format of mobile marketing communication in Western Europe in 2005 (Smith, Husson, and Mulligan 2005b). The main disadvantage of SMS is its 160-character text-only format, while MMS messages can include pictures or video clips. Nevertheless, both SMS and MMS channels can have positive effects on consumers’ brand relationships (Nysveen et al. 2005).

Despite the potential of SMS as an advertising medium, its users, volume of usage, acceptance, and effectiveness have received little attention from academics and international market research institutes. Still, some initial research exists. According to Forrester Research, in Europe’s most advanced mobile markets, such as the United Kingdom and Finland, more than half of direct marketers and their agencies have adopted SMS for their advertising campaigns (de Lussanet 2004). According to A.T. Kearney and Cambridge University (2003), as many as 54% of European consumers received SMS advertisements in 2003; compared to 40% in 2002. In 2005, 45% of Finnish consumers received SMS advertisements; while in 2004 the number was 33% and in 2001 only 9% (Kauhanen 2005). Despite the widespread usage, the initial results of mobile advertising seem quite disappointing.  For example, in 2003 only 2% of European consumers said they had bought anything as a result of SMS-based mobile advertising (A.T. Kearney & Cambridge University 2003). However, a recent study shows that depending on the customer segment, SMS-based mobile advertising can increase sales of mobile services (e.g., news and sports information, ringtones, and graphics) up to 420% (Merisavo et al. 2006). In other words, mobile advertising seems to work differently for different customers and different products or services.

To better understand how mobile advertising works and to make it more effective, it is very important to explore human factors (Choi et al. 2005). The purpose of this study is to examine the drivers of consumer acceptance of SMS-based mobile advertising. Our conceptual model and hypotheses are tested with a survey of 4,062 mobile phone users in Finland. First we discuss consumer acceptance of mobile advertising and present our conceptual model and hypotheses. The research method and results obtained follow in the next two sections. Finally, the paper concludes with a discussion of the results, the academic and managerial implications, and suggestions for future research.

Consumer Acceptance of Mobile Advertising

As consumers are increasingly exposed to mobile advertising, their acceptance is also increasingly regarded as a critical success factor (Amberg, Hirschmeier, and Wehrmann 2004; Heinonen and Strandvik 2003). Although academic research on mobile commerce and consumer acceptance of mobile advertising is relatively new and thus still scarce, a number of studies have been conducted in this field. Many of them have been conceptual work (e.g., Balasubramanian, Peterson, and Jarvenpaa 2002; Barnes 2002; Kavassalis et al. 2003; Leppäniemi and Karjaluoto 2005). One of the first empirical studies was Barwise and Strong’s (2002) study of incentive-based mobile text message (SMS) advertising in the United Kingdom The respondents received over 100 messages during the six-week trial period and were paid a £5 fee on recruitment, and £0.05p per message. The results showed that almost all respondents were satisfied or very satisfied. Barwise and Strong (2002) suggested that mobile advertising works best for marketing simple and inexpensive products and services. Since mobile phones are very personal devices, mobile advertising can often be regarded as intrusive, although relevance and added value (e.g., discounts or special offers) can increase consumer acceptance (Patel 2001). When analyzing 26 different mobile advertising campaigns (5,401 respondents), Rettie, Grandcolas, and Deakins (2005) found that overall acceptance of SMS advertising was 44%, with response rates ranging from 3% to 68% (with an average of 31%). Acceptance was significantly correlated with campaign interest, campaign relevance, and monetary incentives.

Research on consumer acceptance of mobile advertising and related issues has often been conducted using structural equation modeling. The results from empirical studies of four mobile services (text messaging, contact, payment, and gaming) with 2,038 respondents by Nysveen, Pedersen, and Thorbjørnsen (2005) showed that perceived enjoyment, perceived usefulness, and perceived expressiveness had a strong overall impact on consumers’ intentions to use mobile services. While perceived usefulness was found to be a general antecedent for consumers’ intentions to use all kinds of mobile services, enjoyment, for instance, appeared to be particularly important as a driver for using experiential services like contact and gaming services. A recent survey (1,028 respondents) by Bauer et al. (2005) identified entertainment value and information value as the strongest drivers of mobile advertising acceptance. They argued that consumers develop a positive attitude towards mobile advertising – which in turn leads to the behavioral intention to use mobile services – only if mobile advertising messages are creatively designed and entertaining, or if they provide a high information value. Furthermore, Pura’s (2005) survey on location-based SMS services found that conditional value (i.e., context), commitment, and monetary value had the strongest influence on behavioral intentions to use mobile services.

Conceptual Model and Hypotheses

In this section we develop our hypotheses and a conceptual model based on the previous discussion on consumer acceptance of mobile advertising and additional relevant issues related to the specific nature of the mobile phone as a medium.

Many of the studies discussed above identified perceived usefulness, relevance, and monetary incentives, as well as entertainment and information value, as important factors affecting consumer acceptance of mobile advertising. Together these form the total utility that consumers perceive in mobile marketing. Thus, we expect that:

H1: Consumers’ perceived utility of mobile advertising is positively related to their willingness to accept mobile advertising.

Consumers carry their mobile phones almost everywhere, which creates new opportunities for marketers. This can be useful to both marketers and consumers.  It has been suggested that when using mobile services or receiving mobile advertising messages, consumers perceive value in relation to the utilization of time and place (i.e., contextual information) (Heinonen and Strandvik 2003; Pura 2005). For example, with location-based mobile services, the location of a single consumer at a given time can be identified and mobile advertising made contextually valid (e.g., a dinner offer when passing by a favorite restaurant in the evening), which in turn can provide more value for the consumer. In the literature this is often referred to as “conditional value” that depends on the context and occurs and exists only within a specific situation (Holbrook 1994). Thus:

H2: Consumers’ utilization of contextual information in mobile advertising is positively related to their willingness to accept mobile advertising.

In many countries mobile advertising is permission-based by law in order to keep mobile phones clear of spam. Accordingly, mobile advertising basically follows the ideas of permission marketing (see Godin 1999). As mobile phones are very personal devices, consumer perceptions of controlling that permission as related to the mobile advertising (e.g., how many messages they receive in a given period) are considered important factors that might affect consumer acceptance of mobile advertising (Leppäniemi and Karjaluoto 2005; Nysveen, Pedersen, and Thorbjørnsen 2005). Thus, we expect that:

H3: Consumers’ perceived control of mobile advertising is positively related to their willingness to accept mobile advertising.

It has been suggested that consumers’ risk perceptions can strongly determine their behavior (Mitchell 1999). This may also be the case with mobile advertising. Although consumers have given their consent to receive mobile advertising, what they actually get may not necessarily match their expectations. Therefore, they might perceive various risks (e.g., privacy, unsuitable content) or even feel irritated when receiving the communication (Bauer et al. 2005; Tsang, Ho, and Liang 2004). These risks and annoyances represent disadvantages (or sacrifices) that the consumers associate with mobile advertising. Thus:

H4: Consumers’ perceived sacrifice in receiving mobile advertising is negatively related to their willingness to accept mobile advertising.

Finally, extending the privacy issue, consumers’ trust in the use of their personal data and the laws protecting them might affect their acceptance of mobile advertising. Thus.

H5: Consumers’ trust in privacy and the laws of mobile advertising is positively related to their willingness to accept mobile advertising.

In Figure 1 we present a graphic representation and summary of the conceptual foundation.

A Conceptual Model for Consumer Acceptance of Mobile Advertising

A Conceptual Model for Consumer Acceptance of Mobile Advertising

Research Method

Survey respondents for this study were recruited from Finland in late 2005 by email and banner advertising. This resulted in a final sample of 4,062 respondents. The survey instrument was pre-tested with a small sample (n=90) of business students at the University of Oulu, Finland. After the pre-test, the wording of some questions was modified. Non-response bias was tested by following Armstrong and Overton (1977). No statistically significant differences were found for the study variables between early and late respondents.

Of the respondents, 48% were male and 52% were female. The sample was comprised of relatively young consumers, as 70% of the respondents were below 36 years of age. Thirty-five percent of the respondents were students and 47% were employed. Sixty-nine percent reported an annual personal income of less than $25,500, and only 14% earned more than $35,500. The respondents sent an average of 23 text messages per week and received an average of 24 text messages per week. The previous month they had received an average of three SMS advertising messages of a product or service to their mobile phone, and were therefore familiar with mobile advertising.

The questionnaire was developed based on the conceptual model (see Figure 1). We used seven-point Likert scales ranging from “strongly disagree” (1) to “strongly agree” (7) for measuring the different independent variables (Bauer et al. 2005; Nysveen et al. 2005; Tsang, Ho, and Liang 2004). In most questions the respondents were also provided with a “do not know” option.


Scale construction and validation was conducted using exploratory and confirmatory factor analysis. We followed the two-step procedure recommended by Anderson and Gerbing (1988) and conducted two types of assessment: the measurement model assessment and the structural model assessment. In order to achieve appropriate levels of unidimensionality, several items were ultimately excluded from the scales. Moreover, to assess the reliability of the latent variables, composite reliability values (rc) were calculated for each endogenous latent variable. Table 1 shows that all composite reliability values were clearly above the recommended level of .60 (Diamantopoulos and Siguaw 2000). A complementary measure to composite reliability is the average variance extracted (rv), which shows directly the amount of variance that is captured by the construct in relation to the variance due to measurement error. Again, Table 1 shows that all constructs exceeded the recommended level of .50 (Diamantopoulos and Siguaw 2000).  A complete list of items in each of these scales (after the exclusion of the items needed to achieve unidimensionality as discussed above) and the scale measuring the acceptance of mobile marketing are presented in Appendix 1. The means, standard deviations, and correlations are presented in Appendix 2.

Composite Reliability and Average Variance Extracted

Composite Reliability and Average Variance Extracted

Problems of missing data are often magnified in structural equation modeling, and so adequate missing-data computation is of particular importance (Ullman and Bentler 2004). Thus the multiple imputation option with Expected Maximization (EM) algorithm included in LISREL 8.72 was used for imputation of missing values. The technical details of this method are presented in Schafer (1997).

Table 2 shows the fit indices for the measurement model, which included the five factors measuring attitudes towards different aspects of mobile advertising. Overall, the fit indices for the measurement model indicate that the scale structures fit the data acceptably.

Fit Indices for Measurement Model and Structural Model

 Fit Indices for Measurement Model and Structural Model

The hypotheses presented in Figure 1 were tested simultaneously using structural equation modeling (SEM) via LISREL 8.72 (Jöreskog and Sörbom 2001). The modeling was undertaken by deploying covariance matrix and the maximum likelihood estimation procedure.

Table 2 presents the model fit measured using the chi-square statistic (c2), the root mean square error of approximation (RMSEA), the goodness of fit index (GFI), the non-normed fit index (NNFI) and the comparative fit index (CFI). Significant results of the chi-square statistic imply that the model was not acceptable. However, as the chi-square statistic is highly dependent on sample size, the fit of models estimated with large samples is often difficult to assess. Thus, caution needs to be exercised in its application and fit indices have been developed to address this problem (Diamantopoulos and Siguaw 2000; Ullman and Bentler 2004).

The root mean square error of approximation (RMSEA) is usually regarded as the most informative of the fit indices. Values less than .05 are indicative of good fit, and between .05 and under .08 of reasonable fit (Browne and Cudeck 1993; MacCallum, Browne, and Sugawara 1996). Thus, as seen in Table 2, the model fit is reasonable, as RMSEA does not exceed .08.

The goodness of fit index (GFI) is an absolute fit index, which means that it assesses how well the covariances predicted from the parameter estimates reproduce the sample covariances. Here values greater than .90 reflect acceptable fit (Diamantopoulos and Siguaw 2000), as reflected in the GFI values shown in Table 2.

The last two of the fit measures are relative fit indices, which show how much better the model fits compared to a baseline model, usually the independence model. Values of the non-normed fit index (NNFI), and the comparative fit index (CFI) range from 0 to 1 (with the exception that NNFI can have values greater than 1), and values close to 1 indicate a good fit (Steenkamp and van Trijp 1991). The fit indices shown in Table 2 suggest that the model fits well with our data, and thus all fit indices concerned indicate that the model fit is good.

Strucural Equation Model: Standardized Path Estimates

  Strucural Equation Model: Standardized Path Estimates

Figure 2 shows the final structural model with standardized path estimates and t-values. Four of our five hypotheses were supported. As expected, our first hypothesis was supported, as a very strong path (β= .41) between the consumers’ perceived utility of mobile advertising and the willingness to accept mobile advertising was found. Also, Hypothesis 2 was supported, as a strong positive path (β= .27) from the utilization of contextual information to the willingness to accept mobile advertising was found.

However, the consumers’ perceived control of mobile advertising did not significantly affect their willingness to accept mobile advertising (β= .03), and therefore our Hypothesis 3 was not supported. This finding might indicate that consumers take it for granted that marketers do not send them mobile advertising messages without their permission, and thus the whole question of control is less important to them. However, this finding warrants further research, especially in countries where the legislation concerning permission is different.

In Hypothesis 4 we predicted that the consumers’ perceived sacrifice is negatively related to their willingness to accept mobile advertising. Our study supported this hypothesis, as there was a strong negative path (β= -.32) between perceived sacrifice and the willingness to accept mobile advertising. Also, Hypothesis 5 was supported, as the consumers’ trust in privacy and the laws regulating mobile advertising were positively related to their willingness to accept mobile advertising (β= .11). However, this relationship was relatively weak, which implies that consumers do not consider these issues very important.

The explanatory power of the model for the acceptance of mobile advertising was examined by using R2 (squared multiple correlations). Together, all five constructs explain 63% of the variance observed in the acceptance of mobile advertising. This result provides confidence that the model is appropriate.

To summarize, we found relatively strong empirical evidence for the hypotheses stated, except our Hypothesis 3, which was rejected and thus warrants further research. Our results show that perceived utility and perceived sacrifice are of great importance in mobile advertising, being consistent with the general notion that customer-perceived value can be regarded as a ratio between perceived benefits and perceived sacrifice (Monroe 1991; Zeithaml 1988). The importance of the utilization of contextual information was emphasized as well, which implies that mobile advertising would benefit from being location-, time- and consumer profile-specific. However, the results also suggest that perceived control and perceived trust are not very important drivers of consumer acceptance of mobile advertising.


Our research makes a number of academic and managerial contributions. The results indicate that perceived utility and the utilization of contextual information are the strongest positive drivers of consumer acceptance of mobile advertising, which supports previous studies (e.g., Nysveen, Pedersen, and Thorbjørnsen 2005; Pura 2005). The role of perceived utility in accepting new technologies is widely validated in the literature on the adoption of technology (see e.g., Davis 1989; Gefen and Straub 2000). On the other hand, the study indicates that consumers’ perceived control of mobile advertising is not a strong contributor to their willingness to accept mobile advertising. This implication clearly differs from what has been suggested in previous studies (Bauer et al. 2005; Tsang, Ho, and Liang 2004).

Several managerial implications can also be drawn from the study. Firms making use of the mobile channel as part of their promotional strategies should always think of the perceived usefulness that the addition of the channel brings. Usefulness should not only be understood as providing discount messages or alerts, but it also refers to providing up-to-date information via this direct instant response channel, which in turn keeps the mobile audience constantly aware of the various promotions a firm has. In other words, using the mobile channel as an information channel might tie the customers even more closely to the firm, and by doing so, make them less receptive to other advertising, such as mass media advertising from competing firms and brands. With respect to the use of context in mobile marketing campaigns, successful campaigns have been incorporated within the context of a specific event like a concert or a game. Often these kinds of campaigns have been text-based, providing mobile users, for example, with the opportunity to participate in sweepstakes, to get more information via SMS about the players of a game, or lately even the option to control upcoming events in TV programs.

Perceived sacrifice was negatively related to the acceptance of mobile advertising, and so marketers should avoid any mobile advertising that consumers might find irrelevant or irritating. Despite increasing concerns about privacy and the protection of personal data in the public debate, this study finds trust for the appropriate use of personal information by marketers and mobile operators to be a relatively weak driver of the acceptance of mobile advertising. Furthermore, while previous literature on permission marketing emphasized the consumers’ control over the marketing, our study indicates that consumers might take it for granted that marketers do not send them mobile advertising messages without their permission, and thus the whole question of control is less important to them. These perceptions, however, may vary in different countries with different legislation and dissimilar ways of conducting mobile advertising. In Finland consumers are well protected by law from unsolicited mobile advertising.

Limitations and Future Research

We recognize that our study has three main limitations. First, this study was conducted with Finnish consumers and may not reflect the views of consumers in other countries. Thus, in order to reveal cultural and market differences, it would be interesting to repeat this study in different countries, such as the United Kingdom, the United States, or Japan. It would also be appealing to see if the research findings vary in other countries with far lower mobile phone penetration rates. Secondly, as is the case with most online studies, this study used a convenience sample of consumers. Thus, the respondents might see mobile advertising as more acceptable than other samples would. We would suggest future studies to test the external validity of our findings. The third limitation of the paper concerns the use of confirmatory factor analysis, which is based on a domain sampling paradigm with LISREL. The concern is that the study constructs can in some circumstances be criticized as more formative than reflective by nature; as a result the indicator variables can have the weakness of measuring different aspects of the latent variables (see Gefen, Straub, and Boudreau 2000) instead of the same aspect. When using a domain sampling paradigm the indicator variables should be highly correlated and thus exchangeable. In the present study both of these criteria were met, which can be seen from the high correlation between the indicator variables and scale development. The scale development was based mainly on modifying existing scales used in similar studies that were also conducted with LISREL. Even though the scale purifying can be considered valid, future studies should validate the findings by testing the model with other structural equation programs such as PLS (e.g., Chin 1998). Moreover, further work is required to develop the scaling procedure and to examine the impact of scale modifications on how well a modified scale performs in this new context under investigation (see e.g., Rossiter 2002).


Overall, our study indicates that marketers should pay particular attention to the utility and relevancy of mobile advertising messages. For example, mobile advertising should provide consumers with either useful information or a way to save time or money based on the consumer’s situation, location, or personal profile. Prior research has also found that the perceived relevance of mobile advertising is related to changes in purchase intention (Rettie, Grandcolas, and Deakins 2005). Thus, future research should focus on the content of mobile advertising messages and their effect on both the acceptance of mobile advertising and the purchase behavior of targeted consumers.

Finally, given the additional importance of trust on acceptance, it is no surprise that the most successful mobile marketers worldwide are well-trusted brands like Coca-Cola, McDonalds, and mobile operators, which have successfully incorporated the mobile channel into the promotion of their goods and services. Based on these facts it seems that it is much easier for a customer to get into a dialogue with a well-known and established brand than with an unknown one. Thus, companies should strengthen their brand among consumers in order to develop trust. By doing so, the addition of new marketing channels, such as mobile devices into the promotion mix becomes easier.


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The financial support of the Finnish Funding Agency for Technology and Innovation is gratefully acknowledged.

Appendix 1: Survey Items Used to Measure Constructs and Scaling

Survey Items Used to Measure Constructs and Scaling   

   The response options ranged from 1, “strongly disagree” to 7, “strongly agree”.

Appendix 2: Means, Standard Deviations, and Correlations

Means, Standard Deviations, and Correlations

About the Authors

Marko Merisavo is a researcher at the Helsinki School of Economics. His research focuses on the use of digital media in marketing and customer relationship management. He also concentrates on digital marketing metrics.

Sami Kajalo is an assistant professor in the Department of Marketing and Management at the Helsinki School of Economics. His research focus is on marketing orientation and digital marketingis a researcher at the Helsinki School of Economics.

Heikki Karjauoto is a research professor at the Faculty of Economics and Business Administration, University of Oulu, Finland. He received his Ph.D. in marketing from University of Jyväskylä, Finland in 2002. His research concerns electronic business in general and mobile business and commerce in particular. He has published extensively on electronic business in marketing and information system journals and collaborated with several researchers both in Finland and abroad, and with Finnish high-tech companies in joint research projects.

Ville Virtanen is a researcher at the Helsinki School of Economics. He received his M.Sc. (Econ.) in management science from the University of Vaasa, Finland in 1990. His research concerns adoption and business models of mobile and Web 2.0 media.

Sami Salmenkivi is a digital marketing specialist and entrepreneur working in the media industry. He received his M.Sc. (Econ.) in Information Systems Science from the Helsinki School of Economics in 2006. He specializes in social media, Web 2.0, and gaming.

Mika Raulas is a Director of the Future and Digital Marketing project in the Departments of Marketing and Management and Business Technology at the Helsinki School of Economics. His research and publication focus is on the impact of the digitalizing marketing environment on the roles of marketers, customers and other service institutions, business models, and on customer relationship management.

Matti Leppäniemi is a researcher in Integrated Marketing Communication at the University of Oulu, Finland, and the project manager of the FUMMAS ( project. His major areas of interest include mobile commerce, especially mobile marketing.