TRUST AND USER ACCEPTANCE OF MOBILE ADVERTISING

TRUST AND USER ACCEPTANCE OF MOBILE ADVERTISING Samer Barakat, Management Information Systems Department, Faculty of Economics & Administrative Science, Applied Science Private University, Jordan sbarakat@asu.edu.jo Asim El Sheikh, Computer Information Systems Department, Faculty of Information Systems & Technology, Arab Academy for Banking & Financial Sciences, Jordan A.Elsheikh@aabfs.org Abstract The technology acceptance model (TAM) has been widely used to examine the factors that influence a person s acceptance of new technology. In mobile advertising, trust has been proposed as an important factor affecting the adoption of this new advertising channel. The objective of this study is to test the impact of trust on mobile advertising adoption based on the technology acceptance model. A cross-sectional survey is conducted among 174 respondents from two shopping centres samples. Bivariate correlations and multiple regressions were used to test the hypotheses. The results shows that trust significantly influence the attitude towards accepting mobile advertising. Keywords: Technology Acceptance Model, Trust, Mobile Advertising 1 INTRODUCTION Davis presented in 1989 the Technology Acceptance Model (TAM) to explain the determinants of user acceptance of a wide range of end-user computing technologies [1]. The model is based on the Theory of Reasoned Action by Ajzen and Fishbein [2]. TAM points out that perceived ease of use and perceived usefulness affect the intention to use. Davis [1] defines perceived ease of use as "the degree to which a person believes that using a particular system would be free from effort" and perceived usefulness as "the degree to which a person believes that using a particular system would enhance his or her job performance". Perceived ease of use also affects the perceived usefulness (Figure 1). The intention to use affects the real usage behavior. TAM has been tested and extended by many researchers, including Davis himself. TAM has been designed to study information systems at work to predict if the users will actually take a certain system into use in their jobs. The model provides a tool to study the impact of external variables on internal beliefs, attitudes and intentions. TAM has been applied mostly in studying office software usage and in that application area the model can explain about 40% of system use [3]. Figure 1. Technology Acceptance Model [1]. 1

Trust is the people s acceptance is vulnerable to the actions of other people [4]. This is based on the notion that the other people are acting in a responsible manner [5], and they do not benefit from other people s dependence on them [6]. Trust also has a direct influence on a consumer's behavioral intention to use the service [7]. Trust is one of the determinants of perceived usefulness especially in a mobile environment. Mayer [8] also found that trust has a positive effect on perceived usefulness in an m-commerce setting. The study we report here, therefore, begins to explore this new territory both from a user s perspective, and with an eye to what this means for the design of new ubiquitous computing technologies. 2 PREVIOUS STUDIES Table 1 briefly explains some of the related studies made by various researchers. Davis [1] Technology Acceptance Model (TAM) measure perceived usefulness, perceived ease of use and user acceptance of information technology. Davis et al [9] compared Theory of Reasoned (TRA) and TAM in terms of user acceptance of computer technology. Both of the studies were conducted in an organizational context. Nevertheless, Yan et al. [10] used TAM to measure user acceptance of short message service (SMS) in Hong Kong and China and recommended that in order to formulate a successful business strategy for a specific service in a specific market, a mobile operator has to conduct a comprehensive study and identify the key external factors that will affect the perceived usefulness, perceived ease of use, and subjective norms towards acceptance of that particular service in a specific market. Baron et al. [11] indicated the need of further research into the importance of text messaging as a social and cultural practice in everyday lives, with emphases on addictive behaviors, learning and the development of repertoires of communication skills by increasingly sophisticated consumers, and feelings of exclusion by non-participants through qualitative research approach that reflect upon consumer text message (short message service SMS) behavior. Authors Relevant Findings [1] Usefulness was significantly more stronger linked than was ease of use [12] Both TRA and TAM postulated that Behavioral Intention (BI) is the major determinants of usage behavior [13] LOOP, which is a marketing campaign approach marketers use to build an effective process of interactivity with consumers [14] Perceived expressiveness and perceived enjoyment emphasize the importance of taking into consideration relatively untraditional antecedent of technology usage. [10] Research delineating the various stimuli to consider for successful technology acceptance in a global setting, which can account for differential impacts across regions [11] The existence of counter-intuitive behaviors, technology paradoxes and intense social and emotional elements in actual text message usage all point to the need for a review of the definition of the key TAM constructs. Table 1: Theories and models that inform the primary research Recently, the topic of mobile e-commerce and users perceptions of trust in this context has begun to emerge in the literature. Unfortunately, such studies seek to carry over to the mobile context lessons about trust by appealing to research on the use of the internet e.g. [25, 26]. There is little or no investigation of how mobile ecommerce transactions may be different, including the physical configurations of mobile devices, the fact that wireless connections are made, or the fact that there may be no history or experience of use built up in such circumstances. 2

Researchers found that perceived risk is influenced by trust toward the transaction partner [27][4]. They [27] also showed that trust works as a mechanism for reducing consumer s perceived risk in Internet shopping. A recent study of Internet banking showed that trust reduces perceived risk and invigorates the usage of online banking service [28]. In contrast, [29] found that higher perceived risk decreases the level of trust toward the partner. In addition, Mayer [4] insisted that it was unclear whether trust comes before perceived risk or otherwise. Although previous studies showed that perceived risk is an important determinant of online behavior, there was mixed reviews about the relationship between perceived risk and trust in the research literature. While consumers initially trust their e-vendors and have an idea that adopting online service is beneficial to job performance or life style, they will eventually believe that on-line services are useful [30]. In particular, a model of trust [30] and TAM in an on-line shopping setting explicitly indicated that trust is an antecedent of perceived usefulness. Trust also has a direct influence on a consumer's behavioral intention to use the service [7]. Trust is one of the determinants of perceived usefulness especially in an on-line environment. Mayer [4] also found that trust has a positive effect on perceived usefulness in an m-commerce setting. The study we report here, therefore, begins to explore this new territory both from a user s perspective, and with an eye to what this means for the design of new ubiquitous computing technologies. Relevant studies on trust and their findings are shown in Table 2 below. These studies support the importance of trust as a direct or indirect influencing factor in an individual s intention to engage in electronic business activities. Authors Relevant Findings [6] Purchase intention was influenced by trust, which in turn, was affected by integrity and benevolence. [15] Consumers willingness to transact online was influenced by trust, which in turn was affected by familiarity. Familiarity was significant on consumers willingness to transact. [5] Trust was a significant predictor of purchase intention for both potential and repeat customers. Familiarity and disposition to trust were significant on trust for both customers. [16] Privacy and Internet trustworthiness were significant determinants of attitude toward Internet purchasing. In turn, attitude had a significant effect on intent to purchase. [17] Willingness to buy in an Internet store was affected by attitude and perception of risk. Attitude and perception of risk were affected by trust, which in turn was affected by consumer s perception of size and reputation of the store. [18] Trust in one s bank had a significant influence on him or her to use Internet banking. Other factors were Internet accessibility, attitude towards change, computer and Internet access costs, security concerns, ease of use, and convenience. [6] Trust was a significant predictor of intention to transact in both samples. Trust had a significant effect on perceived risk, perceived usefulness, and perceived ease of use. Table 2: Studies on Trust 3

3 RESEARCH MODEL 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. Based on the above discussion and the lack of research on Trust of Mobile Advertising channel, this research seeks to develop an extended TAM by including the Trust construct to the model. The study shall test empirically the influence of trust, together with the attributes of the technology acceptance model (TAM) on mobile advertising acceptance. We used mobile advertising as the targeted technology and hypothesized that trust, utility and ease of use positively affect an individual s acceptance of mobile advertising. In addition, the model focuses on current usage instead of intention to use. The research model for this study is shown in Figure 2. Utility EOU Trust H2 H3 H1 Acceptance Figure 2. Research model EOU = Ease of Use There are three hypotheses in this study. H1: Utility of mobile advertising is positively related to the acceptance of mobile advertising. H2: Ease of use of mobile advertising is positively related to the acceptance of mobile advertising. H3: Trust of mobile advertising is positively related to the acceptance of mobile advertising. 4 METHODOLOGY Subjects for this study were Jordanian consumers age 15 and above surveyed at shopping centres in Amman - Jordan. All items intended to measure the variables in this study were adopted from previously validated instruments. Regression analysis was used to analyze the data. A confirmatory factor analysis was performed to assess the reliability and validity of the measurement model before the regression analysis was performed. 4

5 ANALYSIS The selection of the shopping centre sample of this study followed the sampling procedures suggested by Sudman [19]. According to Sudman, sampling from only one entrance of a shopping centre can create a socioeconomic or geographical bias. Therefore, various locations such as door entrances and different stores were rotated to avoid potential biases [19]. Intercepted visitors were informed of the purpose of the study. They were then asked to spare about 10 minutes to answer the questionnaire. A total of 342 visitors were asked to take part, and 186 people agreed. The response rate was a little higher for the group under age 25 and lower for the group over age 45. Among the questionnaires answered, twelve responses were unusable, because more than 15% of the total items were missing [20]. The total number of usable questionnaires was 174. Table 3 provides the respondents demographic characteristics. The size of the 15 to 44 age group was 88% which is larger than the Jordanian population statistics of 82%. Similarly, a sample size of participants who are age 35 and older was 24% and that is smaller than that of the Jordanian population statistic of 31%. The result is reasonable, since younger groups constitute important market segments for mobile advertising. Female and male respondents accounted for 48.3% and 50.6%, respectively. Thus, the difference between female and male sample is not significant. Age Frequency Percent 15-24 73 42 25-34 57 33 35-44 23 13 45-54 13 7 55 and older 6 3 Missing 2 1 Total 174 100 Gender Female 84 48.3 Male 88 50.6 Missing 2 1.1 Total 174 100 Table 3: Demographic Characteristics (n = 174) 5

The goodness of fit ( ANOVA ) was generated using SPSS. It represented a significance - sig value of.000 (sig <.01) therefore the model is significant at 99% and therefore a relationship was found and we accept the research model. The R value for the three independent variables (utility, ease of use and trust) was 0.778, and the R2 of 0.605 indicated that 60.5% of the variance in user acceptance of mobile advertising could be explained by the independent variables (utility, ease of use and trust). The results indicated that the three constructs were significantly related to user acceptance of mobile advertising. We conducted multiple regression analysis and extracted the table of coefficients as shown in Table 4 below. Standardized Coefficients Model Beta t Sig. 1 (Constant) 3.342.001 Utility.585 2.198.004 Ease of Use.176-1.969.001 Trust.726 6.654.000 Table 4: Coefficients(a) A graphic representation of the final structural model which includes the standardized path coefficients is displayed in Figure 3. PU β =.585 (Sig. =.004) β =.176 (Sig. =.001) PEU Acceptance β =.726 (Sig. =.000) PT Figure 3. Final model PU = Perceived Utility PEU = Perceived Ease of Use PT = Perceived Trust 6

6 FINDINGS AND PRACTICAL IMPLICATIONS The coefficients for the final model are reported above and the model is represented by: (H1): The regression coefficient for perceived utility is 0.585 (p = 0.004, p < = 0.05). The regression results in Table 4 indicated that the H1 hypothesis stands. Perceived utility of mobile advertising is positively related to the user s acceptance of mobile advertising. This result is similar to that of Davis et al. [9], Straub et al. [21], Szajna [22], Igbaria (1997) [23], and Thompson et al [24] that perceived utility is positively related to acceptance. (H2): For the second hypothesis, the regression coefficient is 0.176, and the significance level is 0.001 (p < 0.05). Therefore, Ease of Use is positively related to user acceptance of mobile advertising. (H3): The regression coefficient is.726, and the significance level is 0.000 (p < 0.05). Therefore, Perceived Trust is positively related to user acceptance of mobile advertising. Trust and Utility have a much stronger positive influence on Acceptance than East of Use. In particular, based on the Beta value and the significance levels, one may suggest that Trust component, followed by Utility are the strongest predictors of Acceptance. Ease of Use play relatively a marginal role in the prediction and the explanation of Acceptance. 7 CONCLUSION The theoretical background for the study was adopted from technology acceptance model. In addition to the utility and ease of use constructs found in the technology acceptance model, trust was added as a third construct to the model. These constructs were found to be significant in determining user acceptance of mobile advertising. Acceptance is one of the crucial keys to successful applications choice and use. Clearly, many factors influence technology acceptance according to Davis and others. The regression results indicated that Trust and Utility has a strong positive influence on acceptance. Therefore, the findings of this study partially supported the hypothesis designed in the research model: utility, ease of use and trust are the key attributes that affect user acceptance of mobile adverting. References [1] Fred Davis (1989), Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quartely, 13/1989, pp. 319.339. [2] Icek Ajzen, Martin Fishbein (1980), Understanding attitudes and predicting social behavior, EngleCliffs-Wood: NJ: Prentice Hall. [3] Paul Legris, John Ingham, Pierre Collerette (2003), Why do people use information technology?, A critical review of the Technology Acceptance Model. Information & Management, 40, pp. 191 204. 7

[4] Mayer, R.C., Davis, J.H., & Schoorman, F.D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734. [5] Pavlou, P.A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. [6] Gefen, D., Karahanna, E., & Straub, D.W. (2003). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50(3), 307-321. [7] Cunningham, S.(1967). The major dimensions of perceived risk. IN:Cox, D(Ed.), Risk taking and information handling in consumer behavior. Harvard university press, Cambridge, MA. [8] Mayer, R. C., David, F. D. and Schoorman, F. D.(1995). An integrative model of organizational trust. Academy of Management Review, 20(3):709-734. [9] Fred Davis, Viswanath Venkatesh (2004), Toward preprototype user acceptance testing of new information systems: implications for software project management, IEEE Transactions on Engineering Management, Vol. 51, No 1. [10] Yan, X., Gong, M., & Thong, J.Y.L. (2006): Two tales of one service: user acceptance of short message service (SMS) in Hong Kong and China. Emerald Group Publishing Limited, Vol.8 NO. 1 2006, pp. 16-28. [11] Baron, S., Patterson, A., & Harris, K. (2006): Beyond technology acceptance: understanding consumer practice. International Journal of Service Industry Management. Emerald Group Publishing Limited, Vol. 17 No.2, 2006. pp.111-135. [12] Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1989): User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management of Science; Aug 1989; 35, 8; ABI/INFORM Global pg.984 [13] Davis, R. & Yung, D. (2005): Understanding the Interactivity between Television and Mobile Commerce. Communications of ACM, July 2005/Vol.48, No. 7 [14] Nysveen, H., Pedersen, P.E., & Thorbjornsen, H. (2005): Intentions to Use Mobile Services: Antecendents and Cross-Service Comparisons. Academy of Marketing Science, Journal; Summer 2005; 33, 3;ABI/INFORM Global. [15] Bhattacherjee, A. (2002). Individual trust in online firms: Scale development and initial test. Journal of Management Information Systems, 19(1), 211-241. [16] George, F.F. (2002). Influences on the intent to make Internet purchases. Internet Research: Electronic Networking Applications and Policy, 12(2), 165-180. [17] Jarvenpaa, S.L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1, 45-71. [18] Sohail, S., & Shanmugham, B. (2003). E-banking and customer preferences in Malaysia: An empirical investigation. Information Sciences, 150, 207-217. 8

[19] Seymour Sudman, (1980), "Improving the quality of shopping center sampling," Journal of Marketing Research, 18 (November 1980), 423-31. [20] George Darren, Paul Mallery (2005), SPSS for windows step by step: a simple guide and reference 12.0 update, Boston: Allyna and Bacon. [21] Detmar Straub, Mark Keil, Walter Brenner (1997). Testing the technology acceptance model across cultures: A three country study, Information & Management, 33, 1 11. [22] Bernadette Szajna (1996), Empirical evaluation of the revised technology acceptance model, Management Science, 42, 85 92. [23] Magid Igbaria, Nancy Zinatelli, Paul Cragg (1997), Personal computing acceptance factors in SMEs: A structural equation Model, MIS Quarterly, 21, 279 305. [24] Ronald Thompson, Christopher Higgins, Jane Howell (1991), Personal computing: Toward a conceptual model of utilization, MIS Quarterly, 15, 125 143. [25] Siau, K. and Shen, Z. Building customer trust in mobile commerce. Communications of the ACM, Vol. 46(4), 2003, pp. 91-94. [26] Ventakesh, V. Ramesh, V., and Massey, A. P. Understanding usability in mobile commerce. Communications of the ACM, Vol. 46(12), 2003, pp. 53-56. [27] Javenpaa S. L. and Todd, P. A.(1998). An empirical examination of the determinants of attitudes and intention toward shopping on the Internet. Working paper,university of Texas. [28] Gefen, D.(2000). E-commerce: the role of familiarity and trust. Omega, 28(6):725-737. [29] Lee, H. G.(1998). Do electronic marketplaces lower the price of goods?. Communications of the ACM, 41(1):73-80. [30] Gefen, D., Karahanna, E. and Straub, D.(2003), Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1):51-90. 9