Attitude Change Process toward ERP Systems Using the Elaboration Likelihood Model

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1 Attitude Change Process toward ERP Systems Using the Elaboration Likelihood Model Euisung Jung University of Wisconsin-Milwaukee Lubar School of Business William D. Haseman University of Wisconsin-Milwaukee Lubar School of Business Mark Srite University of Wisconsin-Milwaukee Lubar School of Business Eun Ju Jung Michigan State University Eli Broad College of Business ABSTRACT This study examines how different types of information processing routes influence a user s attitude toward an Enterprise Resource Planning (ERP) system based on the Elaboration Likelihood Model. We tested the main effects of content quality (central route) and system credibility (peripheral route) on attitude change and the moderating effects of motivation and IT ability. Using student survey data, we tested the research model empirically. Consistent with previous research, the results reveal that content quality and system credibility are the primary factors affecting individual s attitude change. Along with this, individuals extrinsic motivation positively moderates the relationship between central route and attitude change towards the system, and negatively moderates the one between peripheral route and attitude change. However, we do not observe significant interaction effects of IT ability. This study will explain individuals dual processing mechanism in attitude change toward information systems, which has not been extensively studied in an IS education context. Keywords Enterprise resource planning, Elaboration likelihood model, Dual processing mechanism, IS adoption, IS education INTRODUCTION IT has become one of the necessities of life in today s information age. The growing number of IT applications and the rapid change of the technology environment has caused the significance of IT education to rise, especially in higher education institutions. Thus, the prominence of IT education has constantly been increased by the universities in the US. To enhance the efficiency and effectiveness of IT education, it is imperative to understand students cognitive processes toward IS. In other words, a knowledge of student s cognitive processes in IT learning would be a critical factor in achieving the fundamental purpose of IT education. This study focuses the attitude change process toward Enterprise Resource Planning (ERP) systems using the Elaboration- Likelihood Model (ELM). ERP systems are the most widely deployed and adopted business applications (Computer Economics 2011). According to a CIO magazine survey (2008), more than 85 percent of respondents agreed or strongly agreed that their ERP systems were essential to the core of their businesses. Computer Economics (2011) published a threeyear trend in the percentage of organizations that have adopted ERP systems and are currently investing in them (Figure 1). The report shows that 64% of IT organizations, across all industry sectors, have adopted ERP systems by There can be no doubt that ERP systems are one of the essential applications in most organizations. Considering this situation, it is reasonable that universities exert all possible efforts to provide ERP system courses. According to the SAP University Alliance (UA), more than 1,300 SAP UA program member institutions are providing ERP courses worldwide ( Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

2 Figure 1 Three-Year Trend in ERP Adoption and Investment Rates (Computer Economics, 2011) Jensen et al. (2005) investigated the issues in evaluation of educational ERP contents and composition using a content analyzing framework and two maturity models. They proposed two possible dimensions in ERP education. One is knowledge of general ERP concepts and the other is knowledge of system-specific functionalities. Due to the trade-off between two dimensions, Jensen et al. emphasized balance between two contents. Rosemann et al. (2000) explored collaboration issues in ERP education and suggested virtual team to facilitate exchange of knowledge and experience among the students. While the interest of ERP systems and ERP systems education has increased, only a few attempts have so far been made at examining students cognitive process in an ERP learning context. This study investigates the cognitive process of attitude change toward ERP systems based on an individual s IT ability and motivation. This study argues that there would be two different persuasive processes and an individual s IT skill and motivation would have a moderating effect on the persuasive process. The primary purpose of this research is to examine an individual s persuasive process toward ERP systems. Petty and Cacioppo (1981, 1986) proposed ELM, which includes two different persuasion routes, the central and the peripheral route. Although there is an extensive empirical literature testing variations of the ELM, little attention has been given to the attitude change of ERP systems due to their size, complexity, and limited accessibility. Therefore, research with ERP systems could not be easily conducted. In this regard, using ERP systems in an educational context would be significant and this study will shed light on the understanding of students cognitive process on attitude change toward ERP systems. THEORETICAL BACKGROUND Many researchers have investigated ways to improve users attitudes toward information systems (IS) and the antecedents of attitude toward IS and intention to use IS based on the theory of reasoned action (TRA), the theory of planned behavior (TPB), and the technology acceptance model (TAM) (Fishbein and Ajzen 1975; Ajzen 1991; Davis et al. 1989). Although research based on those theories provides us with explanations of individual s attitudes and intentions, it does not explain which type of influence process is most effective for a given IS usage context (Bhattacherjee and Sanford,2006; Angst and Argarwal, 2009). In other disciplines such as communication, psychology, and consumer behavior, a considerable number of studies have investigated individuals cognitive processes. There is some IS research which utilized the ELM in terms of knowledge adoption (Sussman and Siegel 2003), technology adoption (Bhattacherjee and Sanford 2006), and electronic health records (Angst and Agarwal 2009). Dual process theories such as ELM and the Heuristic-Systematic Model (HSM) suggest two alternative processes of attitude formation: a more effortful process and a less effortful process (Petty and Cacioppo, 1981; Chaiken 1980). Individuals may evaluate incoming messages through either a more effortful route or a less effortful route in certain contexts (Bhattacherjee and Sanford, 2006). To explain which information processing route is more effective on attitude formation, we adopt the ELM because it explains an individual s differential influence (persuasion) process and has been adopted in substantial empirical research especially in psychology, consumer behavior, organizational behavior, and communication. The ELM provides us with a theoretical framework for understanding how people process information and form their attitudes in a certain situation (Petty and Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

3 Cacioppo, 1981). The ELM theory distinguishes two influence routes (central and peripheral) based on the amount of individuals cognitive efforts (Petty and Cacioppo 1986; Petty et al. 1981). Prior to forming attitude toward an IS, Individuals think deeply and analytically to evaluate related contents in the central route. However, in the peripheral route individuals depend on cues such as system credibility, design, and reputation of information systems rather than on the quality of content (Sussaman & Siegal 2003; Bhattacherjee and Sanford, 2006). Based on the ELM theory, we propose our research model and hypotheses. RESEARCH MODEL AND HYPOTHESES Central vs. peripheral route The ELM theory suggests that central and peripheral routes are directly related to attitude change (Perry and Cacioppo 1981; Petty and Cacioppo 1986; Bhattacherjee and Sanford, 2006). In the ELM theory, individuals may reach the same conclusion (i.e IT adoption) via different processing routes (central or peripheral). We used content quality as a central route, which refers to the quality of information provided during the ERP (SAP) simulation. Content quality will influence individuals positive attitude toward ERP systems, because the central route requires individuals to think critically and deeply about the content and to thoroughly evaluate the potential benefits of information systems usage (Petty and Cacioppo 1986; Petty et al. 1981; Bhattacherjee and Sanford, 2006) H1: Content quality of ERP has a positive effect on potential users attitude of ERP system. In a certain context, individuals more depend on peripheral cues such as source credibility, prior users (reputation), and decision heuristics rather than on content quality in attitude formation (Chaiken 1980; Petty et al., 1981). Information system credibility, GUI appearance, reputation, and market share are used as peripheral cues (Bhattacherjee and Sanford, 2006, Angst and Agarwal, 2009). In the peripheral route, individuals put fewer cognitive efforts on forming target behavior s(petty et al., 1981). The peripheral route (system credibility) also positively influences potential users attitude toward the ERP system. H2: System credibility of ERP has a positive effect on potential users attitude of ERP system. Moderating effects The ELM theory points out the various levels of ability and motivation of individuals to elaborate content (or argument) quality, which leads to varying levels of influence process on attitude formation and change (Petty and Caciopp 1986). People in the high elaboration likelihood state are more engaged in central information processing (thoughtful processing) and tend to be affected by content quality more than peripheral cues. However, people in the low elaboration likelihood state rely mainly on peripheral cues to form their attitude (Bhattacherjee and Sanford, 2006). In the ELM theory individual s motivation and ability to elaborate are considered as elaboration likelihood constructs (Petty and Cacioppo, 1986). Different levels of elaboration likelihood can generate various results. Motivation is commonly operationalized as an individual s relevance of the available information, while ability is operationalized as prior knowledge and experience with the persuasion object (Bhattacherjee and Sanfor, 2006). In our research, we investigate the moderation effects of extrinsic motivation and intrinsic motivation on influence process and attitude change. Intrinsic motivation is defined as the doing of an activity for its inherent satisfaction (i.g., for enjoyment or challenge) (Teo et al., 1999; Ryan and Deci, 2000). In contrast extrinsic motivation is defined as the doing of an activity for its performance improvement and achieving valued outcomes (Teo et al., 1999; Ryan and Deci, 2000). If individuals consider messages as important and relevant to their work and intrinsic motivation, they spend more time and effort on evaluating the information contents. If the message has little relevance, individuals may use the peripheral route rather than the central one. Hence, we propose following hypotheses. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

4 H3: Extrinsic learning motivation has a positive moderating effect on the relationship between contents quality of ERP and attitude toward ERP system. H4: Extrinsic learning motivation has a negative moderating effect on the relationship between credibility and appearance of ERP and attitude toward ERP system. H5: Intrinsic learning motivation has a positive moderating effect on the relationship between contents quality of ERP (SAP) and attitude toward ERP system. H6: Intrinsic learning motivation has a negative moderating effect on the relationship between credibility and appearance of ERP and attitude toward ERP system. Different individual s IT ability can engender different effects of information processing on attitude toward IS usage. Savvy IT users who have a higher level of IT expertise and experience, will more carefully evaluate a new message about an IS and consider content quality to form their attitude. However, people who have low IT ability will simply make a judgment based on source credibility or the reputation of the system. We expect that personal IT ability moderates the effects of content quality and peripheral cues on attitude change. H7: IT ability has a positive moderating effect on the relationship between contents quality of ERP and attitude toward ERP system. H8: IT ability has a negative moderating effect on the relationship between contents quality of ERP and attitude toward ERP system. Figure 2 shows our research model. Central Route System Contents (Functions, Processes, Information) Learning Motivation (Intrinsic and Extrinsic), IT Ability Attitude toward ERP (SAP) Peripheral Route System Credibility and Appearance (Reputation / Market Share) Figure 2. Research Model Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

5 METHODOLOGY We used a quasi-experimental design approach to test the research model and hypotheses. We collected data from Midwest US business undergraduate students enrolled in a fundamental IS course. The survey was conducted at two points in time to measure changes in attitude. At the beginning of the first IS class, students responded to questions related to pre-attitude about the ERP system and their motivation and IT ability. After the first survey, students received a short lecture on SAP and the ERP system and had a chance to use the SAP ERP system via a business simulation game. One week after the first survey, students were given a second short lecture and another chance to use the SAP ERP system with the simulation game. We conducted the survey again to measure changes in students attitude along with the other research factors (central route and peripheral route). No incentive was given to subject. Students voluntarily participated in the survey. After removing the cases of those who did not complete the questionnaire, we had 162 usable cases out of 211. Survey respondents were 63% (102) male and 37% (60) female, with the age of the group primarily being between 18 and 25. Demographic statistics are presented in Table 1. Variable Gender Age Category Frequency N=162 (%) Male 102 (63%) Female 60 (37%) Below 18 years 1 (0.6%) (96.3%) (1.9%) (1.2%) over 66 - Status Freshman 90 (55.6%) Sophomore 29 (17.9%) Junior 29 (17.9%) Senior 12 (7.4%) Grad Student - Etc 2 (1.2%) Number of ITM or CS courses taken IT or SAP work Experience (Year) (63.6%) (32.7%) (1.9%) (1.9%) over (90.0%) (7.4%) (1.2%) (1.2%) over 7 - Table 1. Demographic statistics All constructs were measured using multiple-item perceptual scales, using pre-validated instruments from prior research wherever possible, and reworded to fit the context of the ERP system. We adapted most scales for the questionnaire from previous research including content quality (Bhattacherjee and Sanford 2006), credibility and appearance (Ray et al., 2011; Zo and Rammamurthy, 2009 ), attitude to ERP (Venkatesh and Goyal, 2010, Bock, Zmud, and Lee, 2005; Pavlou and Fygenson, 2006; Chen and Lee, 2008), IT ability(bhattacherjee and Sanford 2006), extrinsic motivation(venkatesh, Morris, Davis, 2003), and intrinsic motivation(mun and Davis, 2003). All items were measured on 7-point Likert scales except demographic information. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

6 We used EQS 6.1, a structural equation modeling software to test the hypotheses and the research model. Data analysis proceeded in two stages. In the first stage, all measurement scales were tested for reliability and construct validity using confirmatory factor analysis (CFA) (Anderson and Gerbing, 1988). After that, we tested the research model and hypotheses with structural model. Table 2 shows the measurement model fit. The measurement model fit adequately with a comparative fit index (CFI) of.97, a non-normed fit index (NNFI) of.96, and a root mean square error of approximation (RMSEA) of.07. DATA ANALYSIS AND RESULTS Data analysis proceeded in two stages. In the first stage, all measurement scales were tested for reliability and construct validity using confirmatory factor analysis (CFA) (Anderson and Gerbing 1998). After that, we tested the research model and hypotheses with structural model. Table 2 shows the measurement model fit. The measurement model fit adequately with a comparative fit index (CFI) of.97, a non-normed fit index (NNFI) of.96, and a root mean square error of approximation (RMSEA) of.07. Index CFA Acceptable Level χ 2 (p), degree of freedom(df) (194) Not significant Normed fit index (NFI).92 >.90 Non-normed fit index (NNFI).96 >.90 Comparative fit index (CFI).97 >.90 or >.95 GFI.85 >.90 Standardized RMR (SRMR).04 <.08 Root mean-square error of approximation (RMSEA), (90% CI).06 (.048,.073) <.08 AIC CAIC Table 2. Measurement Model Fit (CFA) We also tested for possible common method variance in our data using Harman s single-factor test (Podsakoff and Organ, 1986) and the marker-variable technique (Lindell and Brandt, 2000; Lindell and Whitney, 2001). These two analyses suggest that our data set does not have significant common method bias. Next, we tested convergent and discriminant validity using this model. Convergent validity of scale items was assessed using three criteria: (1) item factor loadings (λs) should be significant and exceed.70, (2) composite reliabilities (CR) for each construct should exceed.70, and (3) average variance extracted (AVE) for each construct should exceed.50 (Fornell and Larcker, 1981). The standardized items loading were greater than.70 and were statistically significant. From Table 3, we can see that composite reliabilities of all factors and AVEs exceeded the required minimum of.70 (Fornell and Lacker, 1981). Hence, all conditions for convergent validity were met. The diagonal values in the correlation table present the square root of AVE for each construct (Table 3). To support discriminant validity, all square roots of AVEs should exceed the correlations between that and all other constructs (Fornell and Larcker 1981). From the data presented in Table 3, we can see that the correlations between construct pairs were lower than the square root of the AVEs (with the exception of Content Quality System Credibility which although acceptable is somewhat high). Therefore, discriminant validity was also supported. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

7 Construct Mean Std. Dev AVE CR Inter-construct correlation a Content Quality System credibility Extrinsic motivation Intrinsic motivation IT ability Attitude toward ERP Table 3. Descriptive statistics, AVE, CR and correlation a Diagonal elements (in italics) represent square root of AVE for each construct. Estimates derived from the SEM analysis are used to test the research hypotheses. The overall fit statistics of the structural models met the acceptable levels: CFI =.93 and NNFI=.92, but overall RMSEA values were below the desirable level (Table 4). First we tested the main effects of content quality and system credibility and appearance on post-attitude change (H1, H2). Afterwards, we tested the moderating effects of learning motivation (extrinsic, intrinsic) and IT ability (H3 ~ H8). Table 4 summarized results of structural model analysis, including standardized path coefficients, path significances, variance explained (R 2 values) for each dependent variable, and fit statistics. We proposed a relationship between content quality and post-attitude. The path coefficient is positive and significant (β CQ =.50, p<.01, see Model 1) suggesting that positive content quality increases positive post attitude toward ERP, supporting H1. Also, the relationship between system credibility, which includes system appearance and reputation, and post-attitude toward ERP is statistically significant, supporting H2 (β SC =.12, p<.1, see Model 1). The results demonstrate that both the central and peripheral-route processes influence users attitude change toward ERP. Although both have significant effects, we observed the central route (content quality) to have a larger effect on attitude change toward ERP than the peripheral route. Next, we tested the moderating effects of the three factors (extrinsic learning motivation, intrinsic learning motivation, and IT ability) on the relationship between the central and peripheral routes and users attitude towards ERP. Many authors have derived techniques to represent latent variable interactions within the context of SEM (Jaccard and Wan, 1995; Jöreskog and Yang, 1996; Ping, 1996). We used Ping s two-stage technique to estimate moderator effects (Ping, 1996). First we estimated the measurement parameters for indicator loadings and errors of linear latent variables in a measurement model, and using these estimates we calculated values for the indicator loadings and error variances of the interaction variables. With those calculated values, we specified constants in the structural model containing the interaction variables (Ping, 1996). We expected that users who had higher motivation will be more likely to use the central processing route, so motivation would have a positive effect on the relationship between the central route(content quality) and attitude change toward to ERP (H3, H5). On the contrary, motivation would negatively influence the relationship between the peripheral route and postattitude (H4, H6). We used two types of motivation: extrinsic and intrinsic. The results show that extrinsic motivation positively moderates the relationship between the central route (content quality) and post-attitude (β E_CQ =.20, p<.01, see Model 5) and negatively moderates the relationship between the peripheral route (system credibility) and post-attitude (β E_SC =-.27, p<.01, see Model 5), supporting H3 and H4. However, intrinsic motivation does not have a significant effect on the relationship. We also tested a model that only includes intrinsic motivation. The results showed that intrinsic motivation only negatively influenced the relationship between the peripheral route and post-attitude toward ERP (β I_SC =-.15, p<.05, see Model 4), supporting H6. There is no moderation effect of intrinsic motivation on the central route and attitude change, so H5 was not supported. People who have higher level of prior knowledge may consider system content quality rather than system appearance, credibility, and reputation. However, we cannot see any significant moderating effects of IT ability on the relationship between the central and peripheral routes and attitude change toward ERP, not supporting H7 and H8. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

8 In addition, we compared the variance of dependent variables that explained of main effects model (R 2=.27) and the model that includes moderating effects (R 2=.44). The latter has a higher values and the size is significantly different. Model parsimonious index (CAIC) values are also lower in the moderating model than main effects model. It also supports general Elaboration Model argument. Description Variable Model_1 Model_2 (ITA) Model_3 (EM) Model_4 (IM) Model_5 (EM & IM) Model_6 (All) CQ a.50**.52**.48**.53**.48**.49** SC **.13.17**.16** Main Effects ITA EM ** -.25** IM CQ x ITA SC x ITA Interaction Terms CQ x EM.32**.20**.21** SC x EM ** -.26** CQ x IM SC x IM -.15* R 2 of DV Post attitude χ 2 (df) (52) (116) (132) (116) (226) (345) NNFI Overall Goodness of Fits CFI RMSEA, (90% CI).12 (.10,.13).10 (.09,.11).12 (.11,.13).10 (.08,.11).10 (.09, 11).10 (.09,.11) AIC CAIC Table 4. Models Tested and Results ( a: Content Quality: CQ, System Credibility: SC, Extrinsic Motivation: EM, and Intrinsic Motivation: IM, IT ability: ITA, ** p<.01, * p<.05, p<.1) DISCUSSION AND CONCLUSION Overall, results provide empirical support for the core hypotheses proposed in this research. Both content quality and system credibility influence individuals attitude change toward the ERP system. The central route (content quality) has a stronger effect on individuals attitude toward ERP systems (β CQ =.50, p<.01) than the peripheral one (β SC =.12, p<.1, see Model 1). Students think more critically and deeply about content quality to form their attitude toward ERP systems rather than simply Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

9 considering the reputation and credibility of system. In ERP education, to draw students attention we may need to more strongly emphasize the functionality and quality of ERP systems than their popularity. We further find that significant interaction effects of extrinsic motivation on each of the information processing routes on the attitude change of individuals. Students who think using the ERP system will increase their job performance and that it will also be more relevant will put more emphasis on the content quality than the reputation of the ERP system and its aesthetic appearance. Motivation negatively moderated the path between the peripheral route and attitude change. This result supports the traditional theory related to the ELM. While the ELM framework has been used for investigating attitude change extensively in marketing and communication, there has been little work conducted in the IS context, especially in the ERP system education arena. By demonstrating ELM in an ERP system context, we can clarify the cognitive mechanisms of individual s attitude change. With the research model, we can explain individual s differential information processing mechanisms which cannot be explained by prior IS adoption models (e.g. TAM). Also, our model emphasizes how individual differences in IT ability and motivation affect information processing, attitude, and behavior. This paper contributes toward the information system education area as well as to the business context. First, we can explain how students attitudes toward IS differ based on their motivation. When we use an information system for educational purposes, we need to select an appropriate strategy to attract students attention. To draw more careful and consistent intention, we can emphasize the relevance of using the system with their job performance, because students who mainly use the peripheral route tend to change their attitude frequently. We can also use different strategies based on their motivation. For the low motivation group, we can emphasize the cool appearance or the reputation of system, and for the high motivation group we need to focus on content quality. In a general business context, to increase an individual s intention and usage, companies can also emphasize how information systems are directly relevant to their job. Although we expected that IT ability would be a moderator, we were unable to observe any significant moderating effects. First, the gap of IT ability may not be large enough to explain the difference across the sample. Second, students who have only a general IT ability may feel that they are novices in different information systems. In the future, it will be interesting to investigate why IT ability does not moderate the effects of influence process on individual s attitude toward the ERP system. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

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