Four Types of Attitudes in ICT Acceptance and Use? A Critical Assessment on the Basis of Empirical and Scientometric Data

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1 th Hawaii International Conference on System Sciences Four Types of Attitudes in ICT Acceptance and Use? A Critical Assessment on the Basis of Empirical and Scientometric Data Julia Kroenung, Steffen Bernius Goethe University Frankfurt, Germany {kroenung/bernius}@wiwi.uni-frankfurt.de Abstract User attitudes are a major predictor of the endogenous variables in ICT adoption models. However it is known in the community that the predictive validity of user attitudes on behavioral intention to adopt ICT suffers from inconsistencies. Being a basic predictor of behavioral intention in models like TRA or TPB, user attitudes turned out not to be predictive of behavior in other studies [1]. Some authors have addressed this problem and introduced solution approaches based on splitting the construct into four different subconstructs. This paper evaluates these approaches on the basis of social psychological literature, PLS results of a survey, and scientometric data of fourteen IS top journals. The findings indicate that the problem of insignificance is not caused by a conceptual misspecification, but rather by a combination of conceptual and situational factors affecting the relationship between attitude and behavioral intention. 1. Introduction In the information systems discipline, research on the acceptance and use of information and communication technologies (ICTs) has become one of the most mature research streams [1]. Since the introduction of the technology acceptance model (TAM) by Davis et al. in 1989, many ICT adoption models have been introduced, refined and unified [1, 2, 3]. Based on the theories of reasoned action (TRA) [4, 5] and planned behavior (TPB) [6], the construct of user attitudes that influences the behavioral intention to adopt, is well established within contemporary ICT adoption models. According to a meta-analysis by Jeyaraj et al. (2006) the construct is among the top 5 of the most frequently applied determinants to explain endogenous variables [7]. However, the construct s predictive validity on behavioral intention has shown inconsistent results in the literature. In some cases, user attitudes turned out to predict intention quite well, while in others it was insignificant or its effect on intention was dominated by the effect of other constructs. Therefore, attitude was removed in later versions of the TAM [1, 3]. To explain these inconsistencies and in order to detect systematic misconceptions of the construct in the IS discipline, authors as Yang and Yoo (2004), Zhang et al. (2008) or Zhang and Sun (2009) [8, 9, 10] have introduced the following approaches: One is based on a strict definitional distinction between two types of user attitudes, namely attitude towards an object (A(O)) and attitude towards a behavior (A(B)). Another is to explicitly separate affective from cognitive scales in attitude and create two attitude constructs, each capturing either emotions (affect) or beliefs (cognition). This research provides a critical evaluation of these approaches on the basis of three sources: conceptual reflections on the basis of social psychological literature, survey data, and scientometric literature data from fourteen IS top journals. Thereby, the following research questions will be at focus: 1) Does our data (either scientometric or empirical) support the approaches introduced in the literature to explain the inconsistencies of attitudes? 2) Based on social psychology literature, are there other factors, which have not yet been taken into consideration, but could explain part of the inconsistent results of attitudes? 3) On a descriptive basis, is there evidence leading to the conclusion that the insignificancies between attitude and behavioral intention do not occur randomly? In order to address these questions, the paper is structured as follows: First, the existing approaches in the IS literature that attempt to explain the insignificancies between attitude and behavioral intention are introduced and discussed by means of social psychological literature. The distinction between /12 $ IEEE DOI /HICSS

2 affective and cognitive attitudes and attitude towards and object/behavior will be at major focus. Subsequently, the PLS results of a survey of 1900 German researchers is regarded in order to look if one or two of the four different attitude types existent in literature have a significantly higher predictive validity on behavioral intention than others. The approaches introduced within the second section will then be evaluated by means of our scientometric data, in order to overcome limitations of the empirical study. Finally, the overall results are discussed in line with alternative approaches to the problem of inconsistencies within the relationship of user attitudes and behavioral intention to adopt ICT. At special focus will be the importance of this sort of research to the IS discipline. 2. Four types of attitudes in ICT acceptance and use A general definition of attitude toward an object, A(O), in the discipline of social psychology is provided by Eagly and Chaiken (1993), defining attitude as a psychological tendency that is expressed by evaluating a particular entity with some degree of favor and disfavor [11, p. 1]. They further comment on their conceptual understanding of the term psychological tendency as referring to (...) a state that is internal to the person and evaluating to (...) all classes of evaluative responding, whether overt or covert, cognitive, affective or behavioral [11, p. 1]. In line with the conceptualization of the TRA, Fishbein and Ajzen defined attitude towards a behavior, A(B), as an individual s positive or negative feelings (evaluative affect) about performing the target behavior [5, p. 216]. This definition has been established in the IS domain at least since the introduction of the TAM in 1989, which based on the TRA. Grounded on these definitions, two distinct approaches were introduced by IS authors in order to explain the inconsistencies of the attitude behavior relationship. The first was given by Zhang et al. (2008) and Zhang and Sun (2009). It is based on an explicit distinction of A(O) and A(B) in line with a literature review of IS models and the finding that both types of attitudes are not applied according to definition in most studies and are used more or less interchangeably [9]. They hypothesized that A(B), following Fishbein and Ajzen s definitional and conceptual operationalization, positively influences behavioral intention. A(O) s effect on behavioral intention was hypothesized to be mediated by A(B). The empirical results supported the positive relationship between A(B) and behavioral intention, but it did not always fully mediate the effect of A(O) on intention. Rather, A(O) was found to have a relatively strong direct effect [9, 10]. The second approach to the explanation of the inconsistencies between attitude and behavior was introduced earlier by Yang and Yoo (2004). In that paper the authors argue that attitude should be divided into two constructs, cognitive attitude (A(c)) and affective attitude (A(a)). They referred this dyadic attitude approach to Petty et al. (1998), who argued that the most common classification for the basis of attitude is affect and cognition [12, p. 613]. The affective attitude is thereby defined as a person s liking of an object, and the cognitive refers to a person s thoughts about the object [8]. To empirically confirm their hypothesis of two attitude constructs, the authors modeled a TAM with the standard operationalization of attitude scales and the same model with operationalizations of affective and cognitive attitude. The results showed that, against the hypothesis, cognitive not affective attitude had the strongest and significant effect on behavioral intention even a stronger effect than the mixed scales in the single attitude construct of the TAM [8]. What can we draw from these two approaches as to the explanation of insignificancies between attitude and behavioral intention apart from the fact that four types of attitudes can be distinguished? First, the difference between A(O) and A(B) is unlikely to explain systematic misconceptions of attitude in ICT adoption models, because the results do not show strong effects of one category and constant insignificance of the other. Moreover, even the operationalization of the authors makes both constructs appear very similar, so that it would be very surprising if one of the two was significant and had a strong effect, while the other had not. Nevertheless we support the critics of the authors that attitude should be thoroughly operationalized either according to one or to the other category dependent on the attributive structure of the relevant survey. Second, the difference between cognitive and affective attitude seems to make a difference as to the predictive validity of the construct. Regarding social psychology literature as the root discipline of attitude research, how can these two approaches of IS authors be evaluated conceptually with reference to their importance to ICT adoption models? As to the definition of attitudes, the concept of splitting evaluative responses into three classes (affect, cognition, and behavior), is widely spread within social psychology and McGuire (1969, 1985) claims that it goes as far back as classical Greek philosophy [13, 14]. These classes of responses are central to the attitude concepts, since an attitude develops on the basis of responses [15, 16]. A person does not have an attitude, 3140

3 until he or she responds evaluatively to an entity (stimulus or attitude object) on an affective, cognitive, or behavioral basis [11]. Referring to Fishbein and Ajzen s definition of attitude, which is predominantly applied in ICT adoption studies, the focus clearly lies on affect [2, 5]. Why is this an important detail? One answer is given by Eagly and Chaiken (1993) [11]. Besides providing an umbrella definition of attitudes (see also [17]) including the three response classes, they point out that the attributive structure of the attitude object can cause a weighting towards one or two attitudinal response classes. In simple terms, attitude objects that have predominantly cognitive/affective attributes cause predominantly cognitive/affective responses. Van der Heijden (2004) confirmed this thesis for the IS context, showing that for hedonic ICT, affect based constructs better predict behavioral intentions, and for utilitarian ICT, cognitive constructs have higher predictive validity [18]. Combining both arguments, we draw the conclusion that the results Yang and Yoo had in their study were dependent on the attributive structure of the ICT at focus, rather than a general advocacy for the usage of cognitive attitude as a construct. But, based on social psychology and IS literature, the division of attitude in affect and cognition seems a valuable approach. A third aspect that was not mentioned in IS so far is the influence of situational components on the relationship between attitude and behavior in ICT adoption models. Based on a meta-analysis by Glasman and Albarracín (2006) on the attitude behavior relationship, the importance of situational components such as voluntariness of usage as well as the mentioned compliance between attitude scaling and attributive structure of the attitude object were shown to have a significant influence on the predictive validity of attitudes on behavior [19]. In the next section we present the results of an empirical validation of the four attitude types discussed within this section on the basis of a survey of 1900 German researchers on their attitudes towards selfarchiving their publications. 3. Empirical results The survey was launched in October 2010 and distributed to sixteen German universities. The research subject was the usage of self-archiving facilities to publish or archive research articles. Therefore, the target addressees were researchers and scholars that frequently publish their work. Participation was voluntary. Once subjects agreed to participate in the survey they accessed the questionnaire that was operationalized with use of the online survey tool EFS Survey. The invitations to the survey were distributed via mailing lists of the universities in order to ensure that only the group of addressees was invited. In total, approximately 12,000 people were invited to participate in the survey. The link was clicked by 5,148 people resulting in an average response rate of 44.77%. The survey was finished by 2,095 scholars and after quality tests incorporated in the survey tool, the final sample counted 1,883 complete questionnaires yielding an ending ratio of 36.78%. For purposes of the present research questions, we grouped attitude items towards self-archiving publications into four groups, each representing one of the attitude constructs discussed in the precedent section, namely attitude towards behavior (A(B)) and attitude towards object (A(O)) according to Zhang et al. (2008) and affective attitude (A(a)) and cognitive attitude (A(c)) according to Yang and Yoo (2004). Originally, each subconstruct was subordinated three items. Because only attitude was at interest, our structural model consists of three main constructs: attitude, behavioral intention, and usage. Given some high cross loadings, we tested for multicollinearity using the collinearity statistics in SPSS Two items showed variance inflation factors (VIF) that exceeded the commonly accepted threshold of 10 [20]. Thus they were deleted from the model in order to avoid multicollinearity issues. All other items had VIFs far below 10. Item operationalizations are attachted in Appendix A. Partial Least Squares (SmartPLS Version 2.0, [21]) was used for the analysis of the structural models displayed in Figures 1 and 2. The Tables 1 and 2 show the results for composite reliability and construct correlations of the models. The average variance extracted (AVE) measures the amount of variance that a construct captures from its indicators, relative to the amount that is caused by measurement error. As a measure for convergent validity, the AVE is recommended to exceed 0.5 [22]. Both tables show that this criterion is met. AVE also serves as means of evaluating discriminant validity [23]. The square root of the AVE should therefore be higher than the correlations among the constructs [24]. The shaded numbers on the leading diagonals in the Tables 1 and 2 represent the square root of the AVE of each model. Compared to the numbers of each column, representing the construct correlations, the shaded numbers are the highest values. The significance of the model was tested by means of an ordinary bootstrapping procedure with 1000 samples [21]. The resulting t- values all exceeded the threshold of a 0.01 p-value. To test for common methods bias, we used the approach 3141

4 suggested by Liang et al [25]. The tables are attached in Appendix B. According to the approach used, Common Method Bias was not an issue. In order to compare the two different approaches on attitudes by Zhang et al. (2008) and Yang and Yoo (2004), we calculated two separate models, one including A(O) and A(B) (see Figure 1), and the second including A(a) and A(c) (see Figure 2). The numbers in the middle of the constructs symbolize the according R² value. The numbers on the arrows represent path coefficients and factor loadings. The results depicted in Figure 1 show that, as Zhang and colleagues predicted, A(B) has a slightly stronger effect on behavioral intention than A(O). But, the effect of A(O) is still strong and significant, which indicates that a misconception of attitude as A(O) in behavioral models is very unlikely to be an explanation to the percentage of insignificancies of the attitude behavior relation in IS models. Item 1 Item 2 Item 3 Item A(O) 0.347*** ***.492 Item 2 Item A(B) 0.402*** Intention Usage Figure 1. Empirical model including A(O) and A(B) Item Item A(c) 0.391*** 0.701*** Item Item A(a) 0.373*** Intention Usage Figure 2. *p < 0.05; **p <.01; ***p <.001 ) Table 1. Composite reliability and correlations of constructs A(O) and A(B) Item # Composite Reliability AVE A(B) A(O) Intention Usage A(B) A(O) Intention Usage Table 2. Composite reliability and correlations of constructs A(a) and A(c) Item # Composite Reliability AVE A(a) A(c) Intention Usage A(a) A(c) Intention Usage

5 Figure 2 represents the approach introduced by Yang and Yoo (2004), who split attitudes into the two constructs affective and cognitive attitude. Here, our model shows results that are slightly different from the authors. Both constructs, A(a) and A(c), have strong and significant effects on behavioral intention. Therefore, we cannot derive the conclusion that either of the two constructs is a better solution to the Ajzen and Fishbein scales in general. However, since empirical studies are unique and represent the view of a micro level research, we decided to further investigate the reasons for the inconsistencies of the attitude behavior relationship on the basis of scientometric data from fourteen IS top journals from 1989 to This approach was necessary in order to address the research questions from a macro level perspective and include aspects (like voluntariness of usage and type of ICT) that are difficult to capture in a single empirical study. Thus, within the next section, we aim at evaluating the two approaches by Zhang et al. and Yang and Yoo and specifically focus on the influence of the situational factors discussed in section Scientometric data results A scientometric literature review was conducted in order to gain an overview on the status quo of the attitude construct in the IS discipline and take this data as a basis for evaluating different approaches. Leydesdorff (2001) defines scientometrics as the quantitative study of scientific communication [26, p. 1], while Lowry et al. (2004) regard it as the scientific study of the process of science [27, p. 30]. Lewis et al. (2007) lauded scientometric studies to facilitate the ongoing evaluation and improvement of an academic discipline [28]. As to the IS domain, Straub (2006) emphasized the importance of these self-studies to the development and progress of the IS field [29]. Scientometric studies differ from regular surveys as they focus on the article itself and not about people s behavior or background [30]. With tools as citation analysis or meta-analysis, scientometric studies observe paper titles, author names, paper abstracts, texts, references, and appendices. In order to ensure the meaningfulness of the results and an appropriate sample size, we included 14 top peer-reviewed journals of the IS field, namely Management Information Systems Quarterly (MISQ), Information Systems Research (ISR), European Journal of Information Systems (EJIS), Journal of the Association for Information Systems (JAIS), Journal of Information Technology (JIT), Journal of Management Information Systems (JMIS), Journal of Strategic Information Systems (JSIS), Information Systems Journal (ISJ), Management Science (MS), Communications of the AIS (CAIS), Decision Sciences (DSI), Decision Support Systems (DSS), Communications of the ACM (CACM) and Information and Management (I&M). Beside including in the AIS Journal Basket [31], the journals were chosen because of their topical relevance and their appearance among the top ten ranked journals according to the ranking by Peffers and Tang (2003) [32]. A timeframe of 20 years was determined for our approach, starting with the early beginnings of IS adoption research and the introduction of TAM in 1989 [2]. Our four-headed research team searched through every single issue of the journals selected between spring 1989 and winter In total, more than 19,500 articles were accessed via Business Source Complete by EBSCOhost. Extracted were articles that contained attitude as a construct and fitted into the ICT adoption context. The final sample yielded 147 articles and 378 single measurements of attitude. In order to work with the data, we coded each article according to bibliographic data and several adoption characteristics of the study, such as adoption context or voluntariness of usage as well as numerical values (i.e. significance levels and beta values). With reference to this paper, the scientometric data was used to address the following questions on the basis of the precedent sections. For all three questions, scientometric instead of empirical data is advantageous, because the results can be interpreted on a more general level compared to single empirical studies. The first question is, does the conceptualization of attitude according to the concept of A(O) or A(B) have an influence on the significance level of the attitude behavior relationship in the studies? Second, does the scaling of attitudes according to affect or cognition have a significant influence on the significance level of the attitude behavior relationship? And third, do situational factors as voluntariness of usage and the attributive structure of the ICT at focus (classified into hedonic and utilitarian ICTs [18]) have a significant influence on the significance level of the attitude behavior relationship? As to the first, the 378 measurements of attitudes were sorted according to their attitude concept (A(O) vs. A(B)) and the significance level of the attitude behavior relationship was recorded. Among the 378 measurements, 25 had not applicable results in one or two of the categories, which reduced the sample at focus to 353 measurements. 20 measurements were conceptualized according to the concept of A(O) and 3143

6 333 according to Fishbein and Ajzen s definition of A(B). Thus, the rare occurrence of the A(O) concept in ICT adoption studies makes it highly unlikely that a misconception of this sort causes a general ratio of 20% insignificant attitude behavior relations. In order to address the second question, we took a close look at the attitude scales that were used in the studies and categorized them according to Yang and Yoo (2004) into cognitive and affective items [8]. In line with this procedure, we extracted the significance level of the attitude behavior relation and the situational information as to the attributive structure of the ICT and the voluntariness of usage. All measurements, where one of these categories was not applicable, were deleted. This reduced the sample to 239 measurements. As we mentioned in the beginning of this research, the affective attitude scales introduced by Fishbein and Ajzen (1975) and adopted by Davis et al. (1989) were dominant in the sample [2, 5]. Among the 239 measurements, 187 (78.2%) applied affective scales. However, in 21.8% of the cases, cognitive attitude scales were dominant. For the purpose of the second question, an unpaired t-test was conducted in order to test the affective and cognitive groups on differences of the significance level of the attitude behavior relationship. As expected, the results were insignificant. According to our data it does not affect the significance of attitude and behavioral intention if affective or cognitive attitude scales are applied. The meta-analysis by Glasman and Albarracín (2006) on determinants of the attitude behavior relationship was the basis for the reflections leading to the third question. In their paper they explicitly stress the importance of situational factors on the one hand, and the compliance of attributive structure of the attitude object and attitude scales on the other hand [19]. This leads to the consideration that these factors could cause a major proportion of the insignificancies between attitude and behavior in the IS discipline. For the purpose of this paper, we chose two situational components of ICT adoption to test this consideration: voluntariness of usage and the distinction between hedonic and utilitarian attitude objects. As to the first, the distinction between a voluntary or mandatory usage context was named as a key characteristic by many authors in IS literature [e.g. 1, 33, 34]. Wu and Lederer (2009) have even investigated the role of environment-based voluntariness in information technology acceptance [34]. It revealed that user-based and environment-based voluntariness moderate the relationships between the determinants in behavioral models. Regarding users attitude, we already stated that the behavioral relevance of attitudes in mandatory settings is relatively low up to zero. However, regarding empirical adoption studies in mandatory usage settings, it is possible that user attitudes incidentally correspond to the behavior of interest. In this case, the parameters of a structural model would show some kind of correlation between attitude and behavior, which could occur as workaround, compliance, or even resistance concerning the mandated usage [35]. As to the compliance of user s attitude with the imposed usage in mandatory settings, this would be measureable as a positive and significant relationship between user attitude and behavior. However technically, user s attitude in this case cannot be assumed to be a main determinant of behavior. As to the second, in 2004 Van der Heijden classified information technology in hedonic and utilitarian systems. Derived from consumer behavior literature, he stated that hedonic systems aim to provide self-fulfilling value to the user, in contrast to utilitarian systems, which aim to provide instrumental value to the user [18, p. 696]. Instrumentality thereby implies the existence of an objective external to the interaction between user and system, such as increasing productivity. Hedonic systems by contrast do not aim to facilitate any comparable [18]. According to Glasman and Albarracín (2006), hedonic-utilitarian consistency is assumed to positively influence the behavioral relevance of attitude and thereby the attitude behavior relationship. Transferred to the IS adoption context, this implies that the adoption of hedonic systems is basically affectively/emotionally driven, while the adoption of utilitarian systems is basically cognitively driven [11, 18, 19]. Therefore, assumed an affect-based measurement of attitude, regarding hedonic systems, the hedonic-utilitarian consistency would be high and thus attitude predictive of behavior, and low regarding utilitarian systems. Analogously, cognitive-based measures of attitude would result in a high hedonicutilitarian correspondence regarding the adoption of utilitarian systems. With reference to the scientometric data, among the 238 measurements extracted for this research, 163 of the studies were conducted under voluntary usage, 75 under mandatory usage. Given the management focus of many of the journals, it was not surprising, that utilitarian ICTs made up 69.9% of the studies compared to 30.1% hedonic ICTs. To address the question of the influence of these factors on the significance level of the attitude behavior relationship, we conducted unpaired t-tests to detect differences between the groups. Table 3 depicts the results for the utilitarian and hedonic ICTs. The implications are twofold: First, that both groups are significantly different regarding the dependent 3144

7 variable. Second, the mean significance level of the hedonic ICT group is below 0.1, while the mean significance level of attitude and behavior regarding utilitarian ICTs is higher than Given that predominantly affective scales are applied in 78.2% of cases, this result indicates that these scales obviously perform better in combination with the adoption of hedonic ICTs. These findings confirm the results of Van der Heijden (2004) and Glasman and Albarracín (2006) [18, 19]. Table 3 Unpaired t-test results on the difference of hedonic and utilitarian ICTs on the significance level of attitude and behavior utilitarian hedonic mean variance observations hyp. mean difference degrees of freedom t-value P(T t) one-sided critical t-value P(T t) two-sided critical t-value two-sided Table 4 summarizes the results of the t-test for the voluntary and mandatory usage studies. The test indicates that voluntary usage and mandatory usage are also significantly different regarding the significance level of the attitude behavior relationship. Moreover, regarding the mean values, it can be stated that in studies with voluntary usage, the significance level of attitude and behavior is significantly lower. Beyond that, since the already mentioned effect of mutual compliance between user attitude and behavior in mandatory usage situations cannot be separated within scientometric studies, the difference between the groups should be even more distinctive. Table 4. Unpaired t-test results on the difference of voluntary and mandatory usage on the significance level of attitude and behavior mandatory voluntary mean variance observations hyp. mean difference degrees of freedom t-value P(T t) one-sided critical t-value P(T t) two-sided critical t-value two-sided On the basis of these findings, we grouped the data into four groups: hedonic/voluntary, hedonic/mandatory, utilitarian/voluntary and utilitarian/mandatory in order to detect peaks in the distribution of insignificancies of the attitude behavior relationship within these groups. The distribution of insignificancies within these groups is displayed in Figure 3. As assumed on the basis of the precedent considerations and analyses, the ratio of insignificancies of the attitude behavior relationship has the highest value in the utilitarian/mandatory group (42.3% insignificancies). In this specific group, two effects can be delineated that affect the relationship between attitude and behavior negatively. The first, as was already mentioned above, is that in mandatory usage scenarios attitude is unlikely to be dominant predictors of behavior since usage is imposed by a third party. The second is that, on the basis of social psychology literature and Van der Heijden (2004) [18], affective attitude scales work better with hedonic ICTs not significant Figure 3. Distribution of insignificancies of the attitude behavior relationship Furthermore, Figure 3 shows the lowest ratio of insignificancies within the hedonic/voluntary group. Based on the same reflections as explained above, this is not surprising. Given the dominance of affective scales in our sample and the results of the t-tests, this outcome confirms the argumentation of this research that neither the distinction between affective and cognitive attitude scales or subconstructs guarantees a strong and significant relationship between attitude and behavior. It is more a combination between the right scales for the ICT at focus and some sensitivity for the situational components of the adoption situation itself. Within the next section the outcomes of the empirical and scientometric data analyses are discussed 73 significant

8 with reference to the research questions derived within the introduction. 5. Discussion and further research This research aimed at addressing the following research questions: First, does our data (either scientometric or empirical) support the approaches introduced in the literature to explain the inconsistencies of attitudes? Second, based on social psychology literature, are there other factors, that have not yet been taken into consideration that could explain part of the inconsistent results of attitudes? And third, on a descriptive basis, is there evidence, leading to the conclusion that the insignificancies between attitude and behavioral intention do not occur randomly? To the first question, the answer is yes and no. Yes, because significant effects of different attitude concepts could be confirmed by means of our empirical data. No because on the grounds of scientometric data and theoretical reflections, we doubt that either the distinction of A(O) and A(B) or the distinction between A(a) and A(c) make a valuable contribution to explaining the relatively high rate of insignificancies of attitude and behavior. All four operationalizations of attitudes showed a strong and significant effect on behavioral intention in our empirical study, therefore it is unlikely, that authors would risk insignificance if they chose the wrong concept. As to the second question, the answer is yes. Based on a meta-analysis by Glasman and Albarracín [19] on the determinants of the attitude behavior relationship combined with well-known IS concepts [18, 34], we derived the proposition that situational components as ICT type and voluntariness could affect insignificance of attitude and behavior. The answer to the third question is also yes. By means of t-tests and descriptive analyses, we detected that insignificancies between attitude and behavior in IS adoption studies indeed do occur predominantly within those situational combinations that are assumed to be the least advantageous for a strong predictive validity of attitudes on behavior and vice versa. With reference to the contribution to IS innovation diffusion and adoption, this research provides a systematic analysis leading to the conclusion that neither the chosen adoption model nor the operationalization of the attitude construct is the overall reason for the high ratio of insignificancies regarding the attitude behavior relationship. In fact, the insignificancies seem to be due to a lack of understanding of the influencing factors of the relationship between attitude and behavior. Therefore, we aimed at a systematic approach to this issue basing on scientometric and empirical data on the one hand, to overcome limitations of those approaches when used individually and, on the other hand, to conduct analysis on a micro and macro level view. With regards to further research, more situational factors such as the adoption context [33] or pre/postadoption status [36] should be included in the analysis. Furthermore, an empirical test of the effects of these situational components on the significance level of attitude and behavior would be beneficial, in order to verify the findings of the scientometric analysis presented in this research. 6. References [1] Venkatesh, V., Morris, M., Davis, G., and F. Davis, User acceptance of information technology: Toward a unified view, MIS Quarterly, 27(3), 2003, pp [2] Davis, F., Bagozzi, R., and P. 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10 Appendix A. Item operationalizations Intention I will try to self-archive my articles. totally agree / totally disagree (Likert 7) I intend to self-archive future articles. totally agree / totally disagree (Likert 5) Usage I self-archive my scientific articles always / never (Likert 7) I self-archive my scientific articles totally agree / totally disagree (Likert 5) A(B) To self-archive my scientific articles is useful / useless (Likert 7) advantageous / disadvantageous (Likert 7) valuable / non-valuable (Likert 7) A(O) Self-archiving is useful / useless (Likert 7) advantageous / disadvantageous (Likert 7) valuable / non-valuable (Likert 7) A(a) Taken together, to self-archive my scientific articles satisfying / unsatisfying (Likert 7) I perceive as I perceive self-archiving as satisfying / unsatisfying (Likert 7) A(c) Self-archiving is useful / useless (Likert 7) To self-archive my scientific articles is useful / useless (Likert 7) Appendix B1. Test for Common Method Bias for empirical model including A(B) and A(O) Construct Indicator Substantive Factor R1² Method Factor R2² Loading (R1) Loading (R2) Attitude towards Item *** Behavior A(B) Item *** Attitude towards object A(O) Item *** Item *** Item *** Item *** Intention Item *** Item *** Usage Item *** ** Item *** ** Average *p <.05; **p <.01; ***p <.001 Appendix B2. Test for Common Method Bias for empirical model including A(a) and A(c) Construct Indicator Substantive Factor R1² Method Factor R2² Loading (R1) Loading (R2) Attitude Item *** affective A(a) Item *** Attitude Item *** cognitive A(c) Item *** Intention Item *** Item *** Usage Item *** ** Item *** ** Average *p <.05; **p <.01; ***p <