INVESTGATING THE EFFECT OF SOCIAL INFLUENCE ON INFORMATION TECHNOLOGY ACCEPTANCE Jeoungkun Kim Graduate School of Management, Korea Advanced Institute Science and Technology, kimjk70@kgsm.kaist.ac.kr Heeseok Lee Graduate School of Management, Korea Advanced Institute Science and Technology, hsl@kgsm.kaist.ac.kr ABSTRACT Managers often complain that information systems are difficult to put to use. The problem would be solved if developers make systems more useful. This paper investigates the effect of social influence on the information technology adoption behavior of individual users. Several studies have attempted to incorporate the social influence factor into Davis (1989) Technology Acceptance Model (TAM). However, their studies consider social influence on technology acceptance as largely conceptualized as subjective norm. This paper suggests that the informational social factor should be more adequate to assess this influence. Our suggestion is empirically validated by the use of multiple regression analysis. KEYWORDS: TAM, Social influence, Network externality INTRODUCTION Under the current turbulent business environment, information system (IS) is playing a key role to remain competitive. In IS field, information system usage has been a major research subject. Among the several models proposed to explain this usage, the TAM (1989) is one of the most influential model. A number of prior studies have found that TAM explains more than 40% variance of intention to use with parsimonious variables perceived usefulness and ease of use (Legris, et al. 2003, Venkatesh and Davis 2000). Although TAM is the rigorous model with numerous empirical validations, its lack of social factor on behavior has been the source of criticism (Hartwick and Barki 1994, Taylor and Todd 1995b, Venkatesh and Davis 2000, Venkatesh and Morris 2000). Contrary to TAM based prior research results, a number of resources in social psychology (Abrams, et al. 1990, Deutsch and Gerard 1955, Festinger 1954) and consumer behavior (Oliver and Bearden 1985, Ryan 1982, Shimp and Kavas -1-
1984, Venkatesh and Davis 2000) consistently indicate that the influence of others plays a substantial role in explaining human behavior. This paper attempts to explain why this gap exists and then improve the technology acceptance model by reflecting others role in behavioral intention formation for information technology acceptance. This paper is organized as follows. First, the role of social influence in IS domain literatures is examined to identify the gap in prior studies. Second, related literatures in social psychology and consumer behavior research are reviewed. Third, information technology specific characteristics are investigated to reduce the discrepancy between IS and other research domains. Lastly, a modified technology acceptance model is proposed and then empirically validated. SOCIAL INFLUENCE IN TECHNOLOGY ACCEPTANCE Table 1. Technology acceptance and social influence Author Base Model Social Definition Sample Target Application Usage Condition Adoption Experience SI effect with UC SI effect with Exp Davis (1989) Matheison (1991) Thompson et el. (1991) Hartwick & Barki (1994) Taylor & Todd (1995a) Taylor & Todd (1995b) Karahana et el. (1999) Venkatesh & Moris (2000) Venkatesh & Davis (2000) Chau & Hu (2001) Venkatesh et el. (2003) TAM, TRA TAM, TPB MPCU TRA TAM, TPB, DTPB TAM, TPB, DTPB TRA TAM TAM TAM, TRA, DTPB TAM, TAM2, TRA, TPB, DTPB, MPCU Social Factor (ative), Social Factor (ative) 107 MBA students 262 college students 212 workers 127 people 786 students 786 students 268 salesmen 342 workers 200 workers 408 physicians 215 workers Word processor Spreadsheet PC organizational information system Computing Resource Center Computing Resource Center MS windows Data retrieval system 4 different organizational information system Telemedicine system New technologies in workplace /MN Pre: Post: MN /MN /MN 14 weeks Experienced; Inexperienced 4-22 month Pre-adoption; Post-adoption Pre-adoption; Post-adoption Pre-adoption, 1, 3 months adoption Pre-adoption, 1, 3 months adoption Initial adoption Pre-adoption, 1, 3 months adoption Sig : MN: Sig Sig : MN: Sig : MN : Mixed Pre: Sig Post: Exp: Sig Inexp: Sig Sig Pre: Sig Post: Pre: Sig 1 month: Sig 3 month: Pre: Sig 1 month: Sig 3 month: SI: Social influence, UC: Usage condition, Exp: Experienced, Inexp: Inexperienced, : Voluntary, MN: Mandatory, Pre: Preadoptoion, Post: Post-adoption, : Not applicable, : Non-significant, Sig: Significant TAM: Technology Acceptance Model, TRA: Theory of Reasoned Action, TPB: Theory of Control, DTPB: Decomposed TPB, MPCU: Model of PC Utilization Different from TRA (Fishbein and Ajzen 1975), Davis (1989) argues that the subjective norm has no significant effect on behavioral intention of technology adoption. He pointed out two possible reasons, (i) unreliability of subjective norm for measuring psychometric standpoint and (ii) target information technology - word processor - characteristics. This study result was supported by other researches (Chau and Hu 2001, Mathieson, et al. 2001). Several researchers have attempted to investigate social influence on technology adoption behavior (Hartwick and Barki 1994, Karahanna 1999, Lucas and Spitler 1999, Ryan 1982, Taylor and Todd 1995a, Taylor and Todd 1995b, -2-
Tompson, et al. 1991, Venkatesh and Davis 2000, Venkatesh and Morris 2000) and try to incorporate this social influence factor into TAM (Lucas and Spitler 1999, Venkatesh and Davis 2000, Venkatesh and Morris 2000). According to prior studies, subject norm affects behavioral intention to use when system usage is mandatory (Hartwick and Barki 1994, Venkatesh and Morris 2000, Venkatesh, et al. 2003) and users are inexperienced or at the pre-adoption stage (Hartwick and Barki 1994, Karahanna 1999, Venkatesh and Davis 2000, Venkatesh and Morris 2000). Table 1 summarizes the previous studies on information technology adoption with social factor. SOCIAL INFLUENCE Social factor has been considered as one of the essential determinants influencing human behavior. Asch (1995) s experiment indicates people tend to conform to the majority s opinion or use it as a base for their judgments. Deutsch and Gerard (1955) classified social influence into two types, normative social influence and informational social influence. Studies on social influence found that normative social influence is strong when people belongs to a group, their behavior is identifiable (Prentice-Dunn and Rogers 1985), they are open to anticipated surveillance (Lewis, et al. 1972), under power to reward and punish (Abrams, et al. 1990) and the conspicuousness of product is high (Lord, et al. 2001). On the other hand, informational social influence is greater when subjective uncertainty exists or it lacks objective evidence for evaluation (Abrams, et al. 1990, Deutsch and Gerard 1955, Festinger 1954). Informational social influence is operative in product selection when quality is somewhat ambiguous, one s own ability to discriminate is not satisfactory (Cohen and Golden 1972, Venkatesan 1966), and decision complexity is high (Lord, et al. 2001). Table2. Social influence definitions Classification Source Definition ative Social Deutsch and Gerard (1955) An influence to conform with the positive expectations of another Informational Social Deutsch and Gerard (1955) An influence to accept information obtained from another as evidence about reality The person s perception that most people who are important Fishbein and Ajzen to him think he should or should not perform the behavior in (1975) question norm that Fishbein and Ajzen (1975) developed can be considered as a sort of normative social influence (See Table 2 for the conceptual definition of the each social influence construct). The research findings about normative social influence explain why subjective norm in information technology acceptance literatures is typically significant at mandatory usage conditions and is not significant at voluntary usage environment. Considering information technology is characterized by rapid changes, complexity of evaluation, and difficulty in specifying required function, -3-
informational social influence would be more salient than normative social influence within the context of information technology adoption. RESEARCH MODEL Our research model is found in Figure 1. Based on a prior study (Davis, et al. 1989), our model illustrates how perceived usefulness and perceived ease of use predict behavioral intention and perceived ease of use influences perceived usefulness. Informational social influence has both direct effect and indirect effect on behavioral intention. network externality moderates the effect of informational social influence on perceived usefulness and behavioral intention. INFORMATIONAL SOCIAL INFLUENCE People tend to accept information obtained from others as evidence about reality. This informational social influence operates as credible belief about the true state of an object. Therefore, it can be considered as a kind of credible belief about the true nature of information technology. According to Burnkrant and Cousineau (1975), informational social influence is internalized to attitude through Kelman (1961) s term internalization. TAM does not include attitude. Therefore, it would appear that informational social influence affects behavioral intention directly. H1: Informational social influence has a positive effect on behavioral intention H2: Informational social influence has a positive effect on perceived usefulness PERCEIVED NETWORK EXTERNALITY Network externality is a common phenomenon in IS domain. Computer hardware, software and communication networks are famous examples of system competition and both exhibit network externalities (Katz and Shapiro 1994). Significant network effect is also empirically shown in PC operating systems (Schilling 2002) and PC package software (Brancheau and Wetherbe 1990, Brynjolfsson and Kemerer 1996, Gandal 1994). Informational social influence which come from observing other s use of specific information system or technology will enhance perceived usefulness and behavioral intention to use. In this case, greater technology specific network externality will cause more impact on perceived usefulness and behavioral intention because people get more benefit with additional technology adopters under high network externality. H3: The effect of informational social influence on behavioral intention is moderated by perceived network externality. H4: The effect of informational social influence on perceived ease of use is moderated by perceived network externality. -4-
Informational Social H2 H4 Usefulness Network Externality H3 H1 Behavioral Intention Ease of Use Figure 1. Research Model RESEARCH DESIGN AND ADMINISTRATION The target survey respondents had the experience of using internet messenger or word processor. Convenient sampling method was adopted due to exploratory nature of this study. Most of the measures used to operationalize the constructs in the research model were adopted from relevant previous studies. A multiple-item method was used to construct the questionnaire with 7 point Likert-type scale from strongly disagree to strongly agree. The conceptual and operational definitions of instruments and their related researches are summarized in Table 3. Table 3. Construct conceptual definitions, attributes and sources Construct Conceptual Definition Attributes Sources Usefulness The degree to which a person believes that using a particular system would enhance his or her job performance Usefulness Critical to my job Effectiveness Makes job easier Davis (1989) Increased productivity Ease of Use Behavioral Intention Informational Social The degree to which a person believes that using a particular system would be free of effort The degree to which a person intend to use a system An influence to accept information obtained from another as evidence about reality Easy to use Easy to learning Controllable Understandable Effort to be skillful Intention to use Intention for future use Observing related people s usage Knowledge seeking from related people Knowledge seeking from expertise Observing independent agency s evaluation Davis (1989) Karahana et el. (1999) Operationalized by adapting Park and Lessig (1977) s informational influence profile and the definition of Deutsch and Gerard (1955) Network Externality strength of the value of membership when another user joins and enlarges the specific technology network Overall perceived network externality Job efficiency through large network size Benefit from large network size Operationalized by adapting Shapiro and Katz(1990) s network externality definition in the context of individual technology acceptance -5-
SAMPLE CHARACTERISTICS Through personal visit and web survey, 191 questionnaires were collected. 4 of them had missing values for more than 10% of items and 3 questionnaires were found to be outliers; they were excluded for further analysis. 184 questionnaires were used for final analysis. Table 4 shows detailed sample characteristics. Table 4. Sample characteristics Item Category Frequency Percent 20-29 118 64 Age 30 39 62 34 40 or more 4 2 Total 184 100 Gender Male Female 86 98 47 53 Total 184 100 Jobs College student Graduate student Office worker Private business owner 8 80 91 5 5 43 59 3 Adoption Time Current Usage Total 184 100 Less than 3 months 4 months to below 6 months 7 months to below 12 months 13 months to below 24 months More than 24 months Missing 40 13 20 31 70 10 22 7 11 17 38 5 Total 184 100 Using Not Using Missing 127 55 2 69 30 1 Total 184 100 MEASUREMENT RELIABILITY AND VALIDITY The reliability of measurement items was assessed by the internal consistency method according to measuring Cronbach s alpha, which is generally considered to provide a reasonable estimate of internal consistency (Segars 1997). Cronbach s alpha values range from 0.888 to 0.981 and they all surpass the recommended value of 0.70 or 0.60 (Hair, et al. 1998, Nunnally 1978). Validity is the extent to which measures accurately represent the concept of interest(hair, et al. 1998). Convergent and discriminant validity were assessed for testing construct validity. For convergent validity, item-to-total correlation is calculated, which is the correlation of each item to the sum of the remaining items. All item-to-total correlation values exceed the cut off value 0.4. For discriminant validity, a principal component factor analysis with varimax rotation is performed. 16 items of independent constructs were grouped into 4 factors as expected. Each item loading on single factor exceed 0.5, which is the recommended cut off value. Relatively high values of -6-
reliability and validity imply that the instruments used in this study are adequate (See Table 5 for detailed reliability and validity test results). Table 5. Reliability and validity test results Convergent Validity Constructs Item Mean S.D. Cronbach α (item to total correlation) PU1 5.06 1.37 0.744, 0.863, 0.928, PU2 4.57 1.62 0.891, 0.863 PU3 4.49 1.49 0.948 Usefulness PU4 4.49 1.46 Ease of Use PU5 4.25 4.46 EU1 4.76 1.42 EU2 4.72 1.37 EU3 4.53 1.35 EU4 4.56 1.33 EU5 4.74 1.36 Behavioral BI1 4.81 1.76 Intention BI2 4.78 1.83 SII1 4.49 1.79 Informational SII2 4.40 1.52 Social SII3 4.34 1.27 SII4 4.32 1.22 PNE1 4.87 1.48 Network Externality PNE2 4.81 1.41 0.949 0.974 0.981 0.888 0.845, 0.866, 0.863, 0.881, 0.851 0.951, 0.951 0.988, 0.988 0.764, 0.896, 0.799, 0.798 0.799, 0.799 0.871, 0.895 Discriminant Validity (factor loading on single factor) 0.651, 0.867, 0.895, 0.880, 0.886 0.773, 0.882, 0.842, 0.895, 0.841 0.660, 0.722, 0.802, 0.856 ANALYSIS RESULT Multiple regression analysis was adopted for hypothesis testing. Table 6 and 7 show path coefficients and explanatory power. Hypothesis 1 and 2 are accepted. Hypothesis 3 and 4 are accepted by the use of the moderator effect testing procedure by Baron and Kenny (1986). See Table 6 and 7 for further details. Variable Table 6. Regression result on behavioral intention Regression with PU and PEOU Regression with PU, PEOU and SII Regression with PU, PEOU, SII and interaction R 2 β R 2 β R 2 β 0.606 0.740 0.769 Usefulness 0.591** 0.343** 0.324** Ease of Use 0.293** 0.080 0.013 Social Informational 0.531** 0.342** Social Informational 0.280** Network Externality * p < 0.05, ** p < 0.01-7-
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