The Influence of Technology Readiness on the Theory of Planned Behavior with Self-service Technologies

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1 The Influence of Technology Readiness on the Theory of Planned Behavior with Self-service Technologies Shih-Chih CHEN Department of Computer Science and Engineering, Tatung University, No.40, Sec. 3, Chungshan North Road, Taipei, 104, Taiwan And Huei-Huang CHEN Department of Information Managements, Tatung University, No.40, Sec. 3, Chungshan North Road, Taipei, 104, Taiwan ABSTRACT Competitive world is increasingly characterized by technology-assisted services and transactions nowadays. Self-service technologies (SSTs), such as telephone banking, automated hotel checkout, and online investment trading, whereby customers produce services for themselves without assistance from firm employees. This research examined the influence of technology readiness (TR) index on Ajzen s theory of planned behavior (TPB) with SSTs. Specially, we measured the relation between TRIs personality trait dimensions (i.e. optimism, innovativeness, discomfort, and insecurity) - and the cognitive dimensions of TPB. The research model was tested with a sample of 563 SST users examined through structural equation modeling (SEM). Overall, the results revealed that the effect of optimism and innovativeness were very important but that insecurity and discomfort did not influence attitude, subjective norms, and perceived Moreover, this research represented the first study to empirically examine and discuss the relationships among TR and TPB. Implications of the empirical findings also are discussed. Keywords: Self-service Technologies (SST); Theory of Planned Behavior (TPB); Technology Readiness (TR); Technology Readiness Index (TRI); Structural Equation Modeling (SEM) 1. INTRODUCTION The proliferation of technology-based products and services encourages scholars to study how people embrace and use new technologies [1]. Growing numbers of customers interact with technology to create service outcomes instead of interacting with a service firm employee. Self-service technologies (SSTs) are technological interfaces that enable customers to produce a service independent of direct service employee involvement. Examples of SSTs include automated teller machines (ATMs), automated hotel checkout, banking by telephone, and services over the Internet etc. From users viewpoint, SSTs enable them to enjoy the services they require with a more flexible choice of time and space, and in more ways, e.g. telephone, internet and kiosks [15]. This not only increases the efficiency and effectiveness of service providers, but also gives customers a higher degree of satisfaction [13]. Besides, customers are dealing with services that are becoming increasingly sophisticated from a technological point of view. Parasuraman [1] suggests that technology readiness (TR) should also be taken into consideration when SSTs are being developed, in order to predict the behavior of customers more accurately. Therefore, one of the major issues of the injection of technology into service business is customers readiness and willingness to use technology-based systems as well as influence of such systems on service results. Theory of planned behavior (TPB) is the model widely used in predicting and explaining human behavior while also considering the roles of individual organizational members and social system in this process [8]. It underlying the effort of TRA has been proven successful in predicting and explaining human behavior across various information technologies. Accordingly, the three influencers in this theory, i.e. attitude, subjective norm and perceived behavioral control, can be interpreted as attitude for technology role, subjective norm for organizational members and social system roles, and perceived behavioral control for individual role. Researchers continue to be interested in how attitudes toward technology may influence the extent to which consumers interact with technology-based products and services [17]. Dabholkar [15] explored issues such as how attitudes toward computerized products and a need for interaction with service employees affect attitudes. The main focus of this study is on the influence of technology readiness on Ajzen s theory of planned behavior. In the sections which follow, we first review the relevant literature on SSTs, technology readiness, theory of planned behavior and explore their relationships. We then present our conceptual framework and hypotheses, summarize the research methodology, and report our results. Finally, we elaborate on the major findings, discuss the implications of our research, clarify the limitations of the study and suggest further research directions.

2 Table 1- Examples of SSTs [14][15] Interface Telephone/Interactive Online/ Interactive Kiosks Video/CD* Purpose Voice Response Internet Customer Service Telephone banking Flight information Order status Transactions Telephone banking Prescription refills Package tracking Account Information Retail purchasing Financial transactions ATMs Hotel checkout Pay at the pump Hotel checkout Car rental Self-Help Information telephone Internet information search Blood pressure machines Tax preparation software lines Distance learning Tourist information Television/ CD-based training *Video/CD is typically linked to other technologies to provide customer service and transactions. 2. LITERATURE REVIEW 2.1 Self-service technologies (SSTs) The continuing proliferation of SSTs conveys the need for research that extends beyond the interpersonal dynamics of service encounters into this technology-oriented context. SSTs are technological interfaces that enable customers to use a service independent of direct service-employee involvement [15]. The benefits of choice of SSTs are quite evident in terms of productivity and cost-saving for firms [17]. 2.2 Theory of planned behavior (TPB) TPB was developed from the theory of reasoned action (TRA) and Fishbein s original consumer expectancy-value model. TPB hypothesized that intention to perform a behavior is based on: attitudes, subjective norms and perceived behavioral control (the person s perception of his or her ability to perform the behavior). Attitude (ATT) explains the feeling of a person s favorable or unfavorable assessment regarding the behavior in question. Furthermore, a favorable or unfavorable attitude is a direct influence to the strength of behavior al beliefs about the likely salient consequences. Subjective norms (SN) are perception of whether people who are important to the person think they should or should not perform an activity; this assumes that the more an individual perceives that others think he or she should engage in a behavior, the more likely it is that the person will do so. Perceived behavioral control (PBC) is assumed to reflect past experience as well as anticipated obstacles. It concerns the beliefs about presence of control factors that may facilitate or hinder to perform the behavior. The more resources and opportunities that individuals think they possess and the fewer obstacles they anticipate, the greater their perceived control over the behavior. 2.3 Technology readiness index (TRI) While many literatures have studied customer reactions to technology [4] [13] [15], scholarly research on people s readiness to use technology-based systems is sparse [1]. The Technology Readiness (TR) refers to people s propensity to embrace and use new technologies for accomplishing goals in home life and at work [20]. Technology readiness index (TRI) defines four groups of users separated by their prevailing personality trait with two factors being motivators of new technology use and another two being inhibitors; they are: 1) Optimism: a positive view of technology (belief in increased control, flexibility, and efficiency in life due to technology). 2) Innovativeness: a tendency to be the first using a new technologies (a tendency to be a technology pioneer and thought leader). 3) Insecurity: distrusting of technology and skepticism about its ability to work properly. 4) Discomfort: a perception of lack of control over technology and a feeling of being overwhelmed by it. Optimism and innovativeness are the positive drivers of TR; they encourage customers to use technological products/services, and to hold a positive attitude towards technology. Insecurity and discomfort are the negative attitudes, i.e. inhibitors; they make customers reluctant to use technology. So far, little academic research has been done on the impact of TR on consumer behavior. 2.4 Research framework and hypothesis development Above explication provides theoretical fundamental for the correlations between TR, attitude towards performing the behavior, subjective norms, and perceived Figure 1 illustrates the integrated model. The summary of hypothesis in this research model is shown in Table 2. Technology Readiness Optimism (OPT) Innovativeness (INNO) Insecurity (INS) Discomfort (DIS) Figure 1- Research model TPB Attitude (ATT) Subjective norms (SN) Perceived behavioral control (PBC)

3 Table 2- Research hypotheses Hypothesis 1a Optimism positively influences attitude. Hypothesis 1b Optimism positively influences subjective norms. Hypothesis 1c Optimism positively influences perceived Hypothesis 2a Innovativeness positively influences attitude. Hypothesis 2b Innovativeness positively influences subjective norms. Hypothesis 2c Innovativeness positively influences perceived Hypothesis 3a Insecurity negatively influences attitude. Hypothesis 3b Insecurity negatively influences subjective norms. Hypothesis 3c Insecurity negatively influences perceived Hypothesis 4a Discomfort negatively influences attitude. Hypothesis 4b Discomfort negatively influences subjective norms. Hypothesis 4c Discomfort negatively influences perceived 3. RESEARCH METHODS 3.1 Measures The survey instrument included the tested and validated instruments developed by Parasuraman [1] and Ajzen [7] [8]. The items were clear and understandable, had already proven to be reliable and had been validated in former studies. The translations were performed by native speakers and were back translated to remove and reduce any translation errors. This study employed the partly 36-item TRI scales [1] to measure the four constructs of TR (i.e., 6 items for optimism, 5 items for innovativeness, 3 items for discomfort, and 3 items for insecurity). The items of Attitude (4 items), subjective norms (3 items) and perceived behavioral control (3 items) were adapted from Ajzen [7] [8]. Each item question was scored on a Likert scale from 1 to 7, with a 1 rating indicating strong disagreement and a 7 rating indicating strong agreement. while the second step analyzes the structural model. Both SPSS 14 and AMOS 6.0 are adopted as the tools for analyzing the data. 4. DATA ANALYSIS RESULTS 4.1 Reliability and Validity of research constructs Before the structural model analysis, this study used Cronbach s α to test the measurement scale reliability of three models components. Reliability can reflect the internal consistency of the indicators measuring a given factor. The item reliability for each scale was examined using Cronbach s α to confirm internal consistency of the measures. Nunnally [10] suggested that a scale can be considered to have high reliability if Cronbach s α is greater than 0.70 and should be removed if it was lower than Results showed that every component of the three clusters models had strong reliability with all Cronbach s α greater than 0.70 (shown in Table 3). Table 3- Summary of measurement scales Constructs Indicators Factor loading Cronbach s α (Reliability) Average variance extracted (AVE) Attitude ATT (ATT) ATT ATT ATT Subjective Norms SN (SN) SN SN Perceived Behavior PBC control (PBC) PBC PBC Optimism (OPT) OPT OPT OPT OPT Subjects A survey agency conducted a Web-based survey to evaluate the research model for 60 days. The participants for this study were 598 undergraduate students. After eliminating insincere responses through data filtering, we selected 563 usable responses as the sample finally. We used the value of Cronbach s α for identifying the reliability of the questionnaires, and conducted factor analysis for convergent validity. The result of our pilot test showed the high reliability of all the questionnaires. After data collection, a two-step procedure proposed by Anderson and Gerbing is applied during the Structural Equation Model (SEM) test. The first step involves developing an effective measurement model with confirmatory factor analysis (CFA), Innovative-ness (INNO) Insecurity (INS) Discomfort (DIS) OPT OPT INNO INNO INNO INNO INNO INS INS INS DIS DIS DIS3 0.65

4 Convergent validity is adequate when constructs have an Average Variance Extracted (AVE) of at least 0.5 [3]. Table 2 presents the results of the tests for internal consistency and convergent validity. All factor loadings were greater than 0.5 except for one item of project performance. In the other hand, all constructs also have an AVE of at least 0.5. Convergent validity is satisfactory for the constructs in the measurement model. Discriminated validity is the degree to which measures of different concepts or constructs are distinct. The chi-square difference test can be used to assess the discriminated validity [24]. Discriminated validity is demonstrated if the chi-square difference (with 1 d.f.) is significant, meaning that the model in which the two constructs are viewed as distinct (but correlated) factors is superior. By using the Bonferroni method under the overall 0.01 level, the critical value of the chi-square test is χ 2 (1, 0.01/21) = Because the chi-squared differences for every two constructs exceeded for the model (see Table 3), discriminant validity is successfully achieved. Table 4- Chi-square difference tests for examining discriminant validity Construct pair Research model (Unconstrained) χ 2 = (d.f.= 290) Constrained χ 2 χ 2 difference (d.f.=291) (ATT, SN) *** (ATT, PBC) *** (ATT, OPT) *** (ATT, INNO) *** (ATT, DIS) *** (ATT, INS) *** (SN, PBC) *** (SN, OPT) *** (SN, INNO) *** (SN, DIS) *** (SN, INS) *** (PBC, OPT) *** (PBC, INNO) *** (PBC, DIS) *** (PBC, INS) *** (OPT, INNO) *** (OPT, DIS) *** (OPT, INS) *** (INNO, DIS) *** (INNO, INS) *** (DIS, INS) *** Note: ** p-value <0.01; *** p-value < Empirical Results Every construct in the final measurement models is measured using at least two indicator variables. Seven common model-fit measures were used to assess the model s overall goodness-of-fit: the ratio of degrees-of-freedom (d.f.); goodness-of-fit index (GFI); comparative fit index (CFI); normalized fit index (NFI); incremental fit index (IFI); root mean square residual (RMR); and Root Mean Square Error of Approximation (RMSEA). As shown in Table 5, Comparison of all fit indices with their corresponding recommended values provided evidence of a good model fit (chi-squared/d.f. smaller than 3.0, GFI, AGFI, CFI, NFI all greater than 0.9, and RMR, RMSEA smaller than 0.08), thus demonstrating that the measurement model exhibited a fairly good fit with the data collected [23] [25] [26]. Table 5- Goodness-of-fit indices for the research model Fit index Measurement model Structural Model Chi-square/d.f GFI CFI NFI IFI RMR RMSEA Based on good model fitness as described previously, Table 6 lists the empirical testing results. Based on the entire sample, six path is not supported (H3a, H3b, H3c, H4a, H4b, and H4c is not supported), while the remaining paths are all significant at the 0.01 level (H1a, H1b, H1c, H2a, H2b, and H2c are supported). Table 6- Summary of hypothesis testing results Hypothesis path coefficients (t-value) Results (Supported?) Hypothesis 1a.69*** (10.61) Yes Hypothesis 1b.40*** (5.43) Yes Hypothesis 1c.31*** (5.48) Yes Hypothesis 2a.18*** (3.78) Yes Hypothesis 2b.16** (2.67) Yes Hypothesis 2c.54*** (10.16) Yes Hypothesis 3a -.08 (-.91) No Hypothesis 3b -.12 (-1.07) No Hypothesis 3c -.05 (-.59) No Hypothesis 4a.07 (.96) No Hypothesis 4b.04 (.40) No Hypothesis 4c.11 (1.54) No Note: ** p-value <0.01; *** p-value <0.001

5 5. DISCUSSION Findings and Implications Several implications can be obtained from the study results. We represented the first study to empirically examine and discuss the relationships among TR, attitude, subjective norms, and perceived behavioral control toward SSTs. This study extended the theory of planned behavior by integrating the construct of technology readiness. Technology readiness was theorized to be four causal antecedents of attitude, subjective norms and perceived behavioral control. We were able to show that personality makes a difference in the adoption process of IT and this may help to explain how its adoption may be influenced by the personality of users as well as the characteristics of the technology; personality characteristics as measured in the TRI have a significant effect on technology adoption. Users optimism has the strongest influence on attitude and subjective norms in this study. Users seem to confront IT more openly and positively and are less likely to focus on its negative aspects. Comparatively, optimism is significant but is a weaker indicator of perceived Innovativeness positively influences attitude, subjective norms, and perceived This finding was expected. It is accepted that some aspect of innovativeness would influence attitudes toward technological products [1] [5][27]. In the other hand, discomfort and insecurity have no significant effects on attitude towards using SSTs, subjective norms, and perceived behavioral control in this study. Limitations and future research The current study has a number of limitations. First, we adopted 17-item scale selected from 36-item TRI scale in this research. Still, all constructs and items applied in this research reveal high reliability and validity. Second, we cannot lay claim to a random sample across all users of SSTs, nor is the data causational in nature. Third, we didn't incorporate behavioral intention and actual user behavior in the proposed model. However, this is not a serious limitation as there is substantial empirical support for the causal link between intention and behavior [19][21][22]. Nevertheless, future research should include behavior intention and actual behavior for a more precise understanding of this issue. Fourth, additional variables to extend the conceptual framework will improve our ability to predict usage of SSTs. Finally, we should also investigate the possible influence some situational factors may have on the model in future. Factors such as SST attributes, time pressures, the presence of other customers, and waiting time [18], should be taken into consideration in future studies. 6. REFERENCES [1] A. Parasuraman (2000). Technology Readiness Index (TRI): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies, Journal of Service Research, Vol. 2, No.4, pp [2] A. Parasuraman, and C. L. Colby (2001), Techno-Ready Marketing: How and Why Your Customers Adopt Technology. New York: Free Press. [3] C. Fornell and D. F. Larcker (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, Vol. 18, No. 1, pp [4] D. G. Mick and S. Fournier (1998). Paradoxes of technology: Consumer cognizance, emotions and coping strategies. Journal of Consumer Research, Vol. 25, pp [5] Elizabeth C. H. (1980). Innovativeness, Novelty Seeking and Consumer Creativity. Journal of Consumer Research, Vol. 7, pp [6] Hubert G. and Thomas S. Robertson (1985). A Propositional Inventory for New Diffusion Research. Journal of Consumer Research, Vol. 11, pp [7] I. Ajzen (1985). From intention to actions: a theory of planned behavior, in: J. Kuhl, J. Bechmann (Eds.), Action Control: From Cognition to Behavior, Springer, New York, pp [8] I. Ajzen (1991). The theory of planned behavior, Organizational Behavior and Human Decision Processes, Vol. 50, pp [9] I. Ajzen (2002). Perceived behavior control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, Vol. 32, pp [10] J. Nunnally (1978). Psychometric theory (2nd). New York: McGraw Hill. [11] M. Fishbein and I. Ajzen (1975). Beliefs, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, Boston, MA. [12] M. Fishbein (1963). An investigation of the relationships between beliefs about an object and the attitude toward that object. Human Relations, Vol. 16, 1963, pp [13] M. J. Bitner, S. W. Brown, & M, L. Meuter (2000). Technology infusion in service encounters. Journal of the Academy of Marketing Science, Vol. 28, [14] M. L. Meuter, A. L. Ostrom, M. J. Bistner, and R. Roundtree, (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, Vol. 56, No. 11, pp [15] M. L. Meuter, A. L. Ostrom, R. I. Roundtree, and M. J.

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