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Economics, Law and Policy 
ISSN 2576-2060 (Print) ISSN 2576-2052 (Online) 

Vol. 3, No. 1, 2020 
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57 
 

Original Paper 

Can Customer Intimacy Strategy Generate Intention to Buy? 

MS. Eric Santosa1 

1 Economics & Business Faculty, Unisbank University, Semarang, Indonesia 

 

Received: April 11, 2020       Accepted: April 21, 2020        Online Published: May 7, 2020 

doi:10.22158/elp.v3n1p57                 URL: http://dx.doi.org/10.22158/elp.v3n1p57 

 

Abstract 

Creating customer intention to buy is obviously a major task of every marketer and/or firm. Many 

tactics are exercised to generate the intention, in which a buying behavior is hopefully occured. A 

customer intimacy strategy supposedly be a particular way to do it. However, its power to generate the 

intention hypothetically is not straight forward, but through other variables. It is commonly known, in 

accordance with the Theory of Planned Behavior (TPB), the intention could be predicted by consumer 

attitude and subjective norm. Meanwhile, the attitude and subjective norm theirself are frequently 

in-line with the product’s performance. Therefore.the purpose of the study is to investigate the power of 

customer intimacy strategy in creating the customer intention to buy through the product’s brand 

equity and both the consumer attitude and subjective norm. A 108 sample is withdrawn from those who 

recognize, are interested of and want to buy Dagadu products. Amos 16.0 and SPSS 16.0 are employed 

in analyzing data. The result shows that the customer intimacy strategy has significant effects to the 

brand equity, attitude and subjective norm. In addition, the brand equity also has a significant 

influence to the intention. 

Keywords 

customer intimacy, brand equity, attitude, subjective norm, intention to buy 

 

1. Introduction 

Commonly the consumption goods market contains numbers of likely similar products. It absolutely 

leads to tight competition among the similar products. While generating customers’ intention to buy is 

inevitably an obligation of every marketer and/or firm, the goal certainly depends on an efficacy of a 

selected strategy. A suitable product firstly determines the success of the goal. It should be based on a 

market preference, otherwise a failure takes place. Though the product has high quality and 

well-designed, but if it is not in accordance with the market preference, the desire is distant. Secondly, 

a situation analysis is should be carefully taken into account (Hunger & Wheelen, 2001; Thompson, 

Strickland III, & Gamble, 2010). While it considers the competitive advantage of the product, the 



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activities or strategy of competitors should be receptively respected. 

Treacy and Wiersma (1997) introduce three strategies to generate customers, i.e., product leadership, 

operational excellence and customer intimacy. They insist not to implement the three simultaneously, 

since a concentration supposedly is a critical matter. Santosa (2011, 2014a) investigates the efficacy of 

the product leadership and customer intimacy, particularly their effect to brand equity and customer’s 

loyalty. The results show that their effect whether to brand equity and customer’s loyalty are significant. 

Further, he examines the power of product leadership in generating customers’ intention to buy (2013a, 

2015b). The findings demonstrate that through variables such as perceived quality, perceived value and 

attitude, the product leadership is able to produce the intention. 

While the product leadership can create the customer’s intention, an interesting question likewise arises 

as follows, can the customer intimacy strategy establish the intention as well? Following the study of 

Santosa (2011, 2014a) the effects of whether the product leadership or customer intimacy to the brand 

equity are significant. In addition the finding of some studies (i.e., Cathy et al., 1995; Aydin & Ulengin, 

2015; Hakkak et al., 2015; Walangitan et al., 2015) point out that the brand equity significantly affects 

the intention. Furthermore, Shin et al. (2014) examine that there are significant effects as well of brand 

equity to attitude and to the intention, and similarly, Santosa (2013a, 2015b) identifies that the brand 

equity affects the intention through subjective norm. Consequently, it is supposed that the customer 

intimacy strategy can create the intention to buy too. Thereby, the purpose of the study is to identify the 

effect of the customer strategy to the customer intention to buy, particularly through the brand equity, 

customer attitude, and subjective norm. Hopefully, it will be a bridge of other previous study. The 

findings also will be expectantly support the theory of Treacy and Wiersma (1997). The empirical data 

are drawn from Dagadu’s customers. It is assumed that the brand is a successful brand which inspired 

others to imitate it, or try to produce something similar (Trieha, 2014; Wirausaha Online, 2014). Some 

theoretical reviews, our methods and analysis are provided, and our findings are reported. 

 

2. Formulating Hypotheses  

a. The Relation between Customer Intimacy and Brand Equity 

Customer intimacy especially produces a unique one-to-one product design (Zeithaml & Bitner, 2003). 

This unique design allows the product to be superior and distinctive (Cravens, 2000). It apparently 

encourages the favorable customer’s cognitive process. Furthermore, Santosa’s study (2014) indicates 

that there is an effect of customer intimacy strategy on brand equity. As a result, a hypothesis can be 

withdrawn as follows: 

H1: Customer intimacy influences brand equity 

b. The Relationship between Customer Intimacy with Attitude and Subjective Norm 

While the strategy is on line with the company’s effort to meet consumers’ preferences which is created 

by the long-term relationship along with customers, the products and/or services produced hopefully 

are in accordance with the customers; satisfaction (Zeithaml & Bitner, 2003, 



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http://www.topdimension.eu; Agilier, 2014; Gruber, 2011; MISC, 2014; Sandvall, 2013). Basically, an 

attitude is a total evaluation of a concept, which might generated whether by affective or cognitive 

system. The affective system will produce an affective response, such as moods, emotion, or even an 

attitude (Peter & Olson, 2002). An attitude comprises knowledge and perception which are along with 

experiences and information involved (Schiffman & Kanuk, 2000). Whereas a subjective norm 

illustrates one’s perception to do something in accordance with other’s wants, it relates his/her 

motivation to comply the wants (Azjen, 1991). Thereby, hypotheses can be pulled out as follows: 

H2: Customer intimacy affects one’s attitude 

H3: Customer intimacy affects one’s subjective norm 

c. The Relationship between Brand Equity with Attitude and Subjective Norm 

The formulation of the following hypothesis is based on some considerations as follows: 

(1) Brand equity might be depicted as an added value of a brand and/or the product which drives 

consumers to think, feel and act toward the brand and/or the product (Kotler & Keller, 2006). 

(2) Brand equity lead consumers to have a favorable attitude toward the brand and/or the product 

(Peter & Olson, 2002). 

(3) Brand equity leads to brand attitude which provokes a favorable perception of the brand’s or 

product’s value and its quality (Schiffman & Kanuk, 2000).  

(4) While an attitude is a total evaluation of a concept, generated by whether affective or 

cognitive system (Peter & Olson, 2002), which comprises knowledge and perception along 

with experiences and information involved (Schiffman & Kanuk, 2000), the finding of Shin 

et al. (2014) denote that there is a significant effects of brand equity to attitude.  

Thereby, the following hypothesis is: 

H4: Brand Equity affects one’s attitude 

Furthermore, while a subjective norm illustrates one’s perception to do somthing in accordance with 

other’s wants, which relates his/her motivation to comply the wants (Azjen, 1991), the finding of 

Santosa (2013a, 2015b) demonstrates that the brand equity affects the intention through subjective 

norm. So, can be hypothesized as follows: 

H5: Brand Equity affects one’s subjective norm 

d. The Relationship between Brand Equity and Behavioral Intention 

Since an intention supposedly ignited by such driving forces who later on creates a particular behavior, 

it presumed as an indicator of the behavior probability (Ajzen, 1991). In addition, some studies (Cathy 

et al., 1995; Shin et al., 2014; Aydin & Ulengin, 2015; Hakkak et al., 2015; Walangitan et al., 2015) 

apparently denote the relationship between brand equity and intention. As a result, a hypothesis might 

be proposed as follows: 

H6: Brand Equity affects Behavioral Intention 

 

 



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e. The Relationship among Variables Attitude, Subjective Norm, and Intention to buy  

Fishbein and Ajzen (1975) proclaim that intention is predicted by attitude and subjective norm. Such 

studies (i.e., Jyh, 1998; Okun & Sloane, 2002; Martin & Kulinna, 2004; Wiethoff, 2004; Marrone, 

2005; Kouthouris & Spontis, 2005; Santosa, 2013b; Santosa, 2014a; Santosa, 2014b; Santosa, 

2015a) support the theory of planned behavior that two predictors of intention are attitude and 

subjective norm. Therefore, such hypotheses can be formulated as follows: 

H7: The more favorable the Attitude is, the greater the Behavioral Intention will be. 

H8: The more favorable the Subjective Norm is, the greater the Behavioral Intention will 

be. 

f. Effect of the Hypotheses already Formulated: an intervene position of the Attitude and Subjective 

Norm 

It is hypothesized that brand equity affects the behavioral intention. Further, it is hypothesized that 

brand equity affects both attitude and subjective norm. While it is hypothesized as well that whether 

attitude or subjective norm affects behavioral intention, consequently, both attitude and subjective 

norm likely post as mediator. Therefore, next hypotheses can be drawn as follows: 

H9: Attitude mediates the relationship between brand equity and behavioral intention 

H10: Subjective norm mediates the relationship between brand equity and behavioral 

intention 

 

3. Research Model 

Based on the hypotheses a research model can be developed as follows in Figure 1: 

 

 

 

Figure 1. Research Model 

 

 

 

 

 



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Identification: 

CI : Customer Intimacy 

BE : Brand Equity 

Ab : Attitude toward Behavior 

SN : Subjective Norm 

BI : Behavioral Intention  

 

4. Methods 

The population of the study is consumers who know Dagadu, are interested of and want to buy the 

products, and live at Central Java, Indonesia. A sample is drawn using the convenience and judgment 

technique (Cooper & Schindler, 2008). Data are collected by questionnaires, which consist of five 

items for the customer intimacy variable, four items for the brand equity variables, six items for the 

attitude variables, six items for the subjective norm variables, and four items for the behavioral 

intention. They are distributed to respondents who live at Semarang, Yogyakarta, and other cities at 

Central Java. After examining the forms for the data’s completion, 108 out of the 110 questionnaire 

forms are accepted which supposed meet the sample adequacy (Ghozali, 2004, 2007; Hair et al., 1995). 

A Likert scale is operated corresponding to a five-point scale ranging from 1 (=completely disagree) to 

5 (=completely agree). The instrument, which denotes to indicators, will firstly be justified through 

confirmatory factor analysis, Construct Reliability and Variance Extracted. Further, data are analyzed 

by employing Amos 16.0. 

 

5. Result and Discussion 

a. Confirmatory Factor Analysis 

The confirmatory factor analysis is not simultaneously carried out, but done in phases. The first phase 

contains two variables, i.e., Customer Intimacy (CI) and Subjective Norm (SN). The second phase 

examines two variables, attitude (Ab) and Behavioral Intention (BI). The third phase considers one 

variable, i.e., Brand Equity (BE). The process illustrated at Appendix A, while its result exemplified at 

Table 1. 

 

 

 

 

 

 

 

 

 



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Table 1. The Result of CFAon Variables CI, BE, Ab, SN and BI 

Indicators Loading Factor threshold Criteria 

CI1 0.572 0.4 Valid 

CI2 0.485 0.4 Valid 

CI3 0.603 0.4 Valid 

CI4 0.650 0.4 Valid 

CI5 0.641 0.4 Valid 

b 0.929 0.4 Valid 

ev 0.935 0.4 Valid 

NB 0.905 0.4 Valid 

MC 0.919 0.4 Valid 

BE1 0.384 0.4 Not Valid 

BE2 0.535 0.4 Valid 

BE3 0.868 0.4 Valid 

BE4 0.608 0.4 Valid 

BI1 0.656 0.4 Valid 

BI2 0.781 0.4 Valid 

BI3 0.720 0.4 Valid 

BI4 0.628 0.4 Valid 

Source: data analysis. 

 

All indicators denote of more than 0.4 which indicate of their validity (Ferdinand, 2002) except BE1. 

b. The Structural Equation Model 

The model has one initial independents variable (CI) and four dependent variables (BE, Ab, SN, BI) in 

which the three dependent variables (BE, Ab, SN) at some extent are treated as independent variables 

as well. Since the purpose of the study is eagerly to know the relationship between the one initial 

independents variable (CI) and the primary dependent variables (BE, Ab, SN, BI), likewise among the 

four dependent variables separately and simultaneously, a Structural Equation Modelling (SEM) is 

employed (Hair et al., 1995). In addition, the use of SEM will give advantages such as fast, accurate 

and more detail. It is possible since the method performs a unification of factor analysis and path 

analysis (Ghozali, 2004, 2007). 

An initial structural equation model is drawn by connecting all variables as hypothesized. This model is 

likely not thoroughly appropriate to expectancy, since all indicators, i.e., Chi-Square/Prob, Cmin/df, 

GFI, AGFI, TLI, RMSEA, do not meet the criteria (Appendix B). Consequently, a modification model 

is generated by connecting e1↔e2 and e3↔e4, This modification model seemingly produces better 

scores than before (Table 2, Figure 2). 



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Table 2 denotes that although not all the model’s indicators meet the criteria, most (Chi-square, Cmin/df, 

GFI, TLI and RMSEA) equalize the requirements. It means that the model’s data are in accordance with 

the structural parameter. As a result, the model is worthy of use. 

Evaluation of Normality. Evaluation of normality is carried out by univariate test (Ferdinand, 2002; 

Ghozali, 2004). It is exercised by scrutinizing the skewness value whether its critical ratio values are 

less or equal to ±2.58. As a matter of fact, there is one variable, i.e., SN, whose c.r of the skewness 

value are more than ±2.58. As a consequent, it indicates that univariately the data distribution is not 

normal. To check further, a multivariate test is executed. The result of the data analysis shows up that 

the multivariate critical value is 18,937. It is more than 2.58 as required (Appendix C) . As a result, the 

normality test needs a bootstrap analysis. 

 

Table 2. The Second Indicators Resulted from Modification 

Indicators Initial Scores Second Scores Threshold Justification 

Chi-square/Prob 226.136/0.000 27.172/0.205 40.790/p>0.05 Meet the criterion 

Cmin/df 9.422 1.235 ≤ 5 Meet the criterion 

GFI 0.768 0.949 High Meet the criterion 

AGFI 0.564 0.896 ≥ 0.9 
Not meet the 

criterion 

TLI 0.741 0.903 ≥ 0.9 Meet the criterion 

RMSEA 0.281 0.047 0.05 s.d 0.08 Meet the criterion 

Source: Data Analisis.  

 

Bootstrap Analysis. A bootstrap analysis is used to gain a fit model, since the normality test does not meet 

the pre-requisite. A Bollen-Stine’s bootstrap analysis illustrates the following: (a) The model fits better in 

242 bootstrap samples, (b) it fits equally well in 0 bootstrap samples, (c) it fit worse or failed to fit in 258 

bootstrap samples, (d) testing the null hypothesis that the model is correct, Bollen-Stine bootstrap p=0.517. 

The result indicates that the probability is more than 0.05 which denotes that it can reject the hull 

hypothesis. In addition, the model’s indicators of goodness of fit indicate that most meet the 

requirements (Appendix D). Consequently, the model is worthy of use. 

 



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BE

Ab

SN

BI

b

ev

NB MC

,55

e1

,43

e2

,71

e3
,61

e4

,04 ,05

,01

899,95

z3

1182,02

z2

3,77

z4

1

chi-square= 27,172
prob = ,205
cmin/df = 1,235
GFI = ,949
AGFI = ,896
TLI = ,993
RMSEA = ,047

,05

1
1

1

1

5,83

CI
,30

5,63

7,93

1

4,66

4,70

3,37

z11

,04

,01

,48

1

- 57

-,29

 

Figure 2. The Modification Model 

 

Outliers. Evaluation of the outliers can be carried out by either a univariate test or a multivariate test 

(Ferdinand, 2002). The univariate test is successfully employed by firstly converting the data to 

Z-scores, which should be less than ±3.0 (Hair et al., 1995). The result indicates that most of the 

variables’ Z-scores are less than ±3.0, except BE1, ev3, NB2, and MC3,, which their scores are more 

than ±3.0 (Appendix E). Therefore, the existence of outliers is indicated. 

To check further, a multivariate outliers test is needed. It determines the chi-square value which 

subsequently is used as the upper limit, which could be calculated by searching on a chi-square table whose 

degree of freedom is equal to the number of variables employed, which is 17, under the degree of 

significance (p)=0.001. The chi-square value is found to be 40.790. In fact, most of the scores for 

Mahalanobis’s distance are less than 40.790, except observations number 1, which inevitably suggests 

outliers (Appendix F). However, because there is no specific reason to dismiss them, the outliers are 

worth being used (Ferdinand, 2002). 

Multicollinearity and Singularity. According to the output from Amos, the determinant of the sample 

covariance matrix should be equal to 964089,522. This value is far above zero. Consequently, it 

belongs to no multicollinearity or singularity category (Appendix G). 

Test of Hypotheses. The regression weights output indicates that the influence of CI on BE, BE on Ab 

and SN, CI on Ab and SN, SN on BI, and BE on BIare significant. The influence of Ab on BI under 

assumption that p<0.10, belongs to be significant as well (Table 3). 

 



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Table 3. Regression Weights: (Group number 1-Default model) 

   Estimate S.E. C.R. P Label 

BE <--- CI 0,482 0,074 6,561 *** par_11 

SN <--- BE 5,632 1,810 3,111 0,002 par_5 

Ab <--- BE 7,934 1,580 5,023 *** par_6 

Ab <--- CI 4,661 1,422 3,277 0,001 par_7 

SN <--- CI 4,697 1,630 2,881 0,004 par_8 

NB <--- SN 0,045 0,002 22,044 *** par_1 

MC <--- SN 0,045 0,002 24,082 *** par_2 

BI <--- SN 0,011 0,006 2,035 0,042 par_3 

b <--- Ab 0,048 0,002 26,009 *** par_4 

ev <--- Ab 0,044 0,002 27,344 *** par_9 

BI <--- Ab 0,010 0,006 1,650 0,099 par_10 

BI <--- BE 0,304 0,110 2,762 0,006 par_14 

Source: Amos output. 

 

Intervene Position Test. Based on Table 4, the total effects of BE-BI=0.426. Likewise, it points up the 

total effects of BE-Ab (0.441), Ab-BI (0.178), BE-SN (0.305) and SN-BI (0.199). The sum of the total 

effects of BE-Ab and Ab-BI is 0,619. Whereas the sum of the total effects of BE-SN and SN-BI is 

0.504. These mean that whether the sum of the total effects of BE-Ab and Ab-BI or the sum of the total 

effects of BE-SN and SN-BI is bigger than the total effects of BE-BI. Consequently, both Ab and SN 

are mediators. 

 

Table 4. Standardized Total Effects 

 CI BE Ab SN 

BE 0.536 0.000 0/000 0.000 

Ab 0.524 0.441 0000 0.000 

SN 0.446 0.305 0.000 0.000 

ev 0.490 0.412 0.935 0.000 

b 0.486 0.409 0.929 0.000 

BI 0.336 0.426 0.178 0.199 

MC 0.410 0.280 0.000 0.919 

NB 0.404 0.276 0.000 0.905 

Source: Amos output. 

 

 



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6. Discussion 

Table 3 shows that the influence of CI to BE is denoted by p=0.000. It means that the influence of CI to 

BE is significant. Likewise the influences of CI to AB and CI to SN belong to be significant as well, 

since their probabilities are less than 0.05 (p=0.001 and p=0.004). The probabilities of BE to Ab, BE to 

SN, and BE to BI are also less than 0.05, indicating that the influence of the variables are significant 

(p=0.000, p=0.002, and p=0.006). While the influence of SN to BI is positively less than 0.05 

(p=0.042), the influence of Ab to BI has probability more than 0.05 (p=0.088). However,it can be 

categorized to be significant when the threshold is altered from 0.05 to 0.10.  

Testing of intervene position indicates that whether the indirect effect of BE to BI through Ab, or the 

indirect effect of BE to BI through SN, is bigger than the direct effect. Consequently, both Ab and SN 

post as mediators. 

 

7. Conclusion  

The hypotheses of, i.e., “Customer intimacy influences brand equity (H1)”, “Customer intimacy affects 

one’s attitude (H2)” and “Customer intimacy affects one’s subjective norm (H3)” are really empirically 

supported. Likewise, the hypotheses of “Brand Equity affects one’s attitude (H4)”, “Brand Equity 

affects one’s subjective norm (H5)”, and “Brand Equity affects Behavioral Intention (H6)” are also 

empirically supported. The findings are in accordance with studies of Shin et al. (2014), Santosa (2015), 

Cathy et al. (1995), Aydin (2015), Hakkak (2015) Walangitan et al. (2015). 

The influence of both attitude and subjective norm to behavioral intention (H7, H8) are also empirically 

supported. The findings are also in favor with other studies such as Jyh (1998) Okun and Sloane 

(2002),Martin and Kulinna (2004), Wiethoff (2004), Marrone (2005), Kouthouris and Spontis 

(2005), Santosa (2013), Santosa (2014) and Santosa (2015), that support the theory of planned 

behavior, in which attitude and subjective norm are predictors of behavioral intention. This can be 

explained by the intention to buy, while being determined by attitude (Fishbein & Ajzen, 1975), and 

likewise shaped by the subjective norm, obviously suggests that whatever happens to the attitude or the 

subjective norm, the intention to buy apparently also follows, and the alteration of intention to buy is in 

accordance with the change of them. 

The hypotheses of Ab and SN as mediators (H9, H10) are also supported. As a matter of fact, all 

hypotheses are successfully proven. The consequences of the study carries out two things, firstly that 

the findings contribute as a bridge of other previous studies. Secondly the study justifies the theory of 

Treacy and Wiersma (1997). 

Back to the title of the manuscript, i.e., “Can Customer Intimacy Strategy Generate Customer Intention 

to Buy?” The answer is, yes and not. The meaning of yes is, that the effect of the customer intimacy 

strategy later on generates the behavioral intention, particularly intention to buy. Whereas the meaning 

of not is, the stategy could not directly generate the intention. However, it is empirically supported that 

customer strategy leads to the creation of behavioral intention, particularly intention to buy, whether 



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through brand equity, through both brand equity-attitude and brand equity-subjective norm, or through 

both attitude and subjective norm. 

 

8. Limitations and Future Directions 

There are some limitations of the study, firstly, the customer intimacy is supposed operated by 

indicators as follows, the diversification of the product is in line with consumers’ taste; the product’s 

message is personal; customer oriented; managers, staffs and employees are responsive; and 

personalized program. They are fully genuine which are not employed in such topic beforehand. 

Though based on CFA test they belong to valid indicators (Table 1), it is not impossible that other 

indicators might be employed which might contribute better results. 

Secondly, it likely the model is not in accordance with the title. The customer intimacy variable is not 

directly regressed to the behavioral intention variable. It is based on such point of view as follows. 

Since an intention to buy does not likely arise spontaneously but through something impressively, the 

brand equity variable is supposed worthy to trigger the intention. However, it might be possible, under 

such assumption, that the customer intimacy is regressed directly to the intention. 

Thereby, it is recoomended to carry out such study which firstly, exploring other indicators of the 

customer intimacy variable. Secondly, developing other model that leads to regress directy the 

customer intimacy to the intention.  

 

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Appendixes 

Appendix A 

 

chi-square= 44,185
prob = ,003
cmin/df = 2,008
GFI = ,922
AGFI = ,839
TLI = ,968
RMSEA = ,097

CI1

CI2 CI3 CI4 CI5

,37

e1

1

,55

e2
1

,66

e3
1

,33

e4
1

,31

e5
1

5,83

CI

,18
,17,25,20 ,19

1609,36

SN

NB MC

,71

e6
,61

e7

,04 ,05

1 1

43,22

-,57

-,21-,28
-,28

,21

 

Standardized Regression Weights: (Group number 1 - Default model) 

Estimate 

CI1 <--- CI ,572 

CI2 <--- CI ,485 

CI3 <--- CI ,603 

CI4 <--- CI ,650 

CI5 <--- CI ,641 

NB <--- SN ,905 

MC <--- SN ,919 

  



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1532,11

Ab

5,38

BI

b ev

,55

e1

,43

e2 chi-square= 71,372
prob = ,000
cmin/df = 4,198
GFI = ,868
AGFI = ,721
TLI = ,921
RMSEA = ,173

1

,04

-,29

40,77

,05

1

BI1 BI2 BI3 BI4

,50

e3

1
,22

e4

1 ,24

e5

1
,55

e6

1

,26 ,25 ,22 ,26

-,31
-,21

-,22

 

 

Standardized Regression Weights: (Group number 1 - Default model) 

 

 

 

 

 

 

 

 

 

chi-square= ,587
prob = ,444
cmin/df = ,587
GFI = ,997
AGFI = ,973
TLI = 1,059
RMSEA = ,000

BE1 BE2 BE3 BE4

,55
e8

1

,60

e9
1

-,09

e10

1

,66

e11

1

,17

BE

,24 ,452,27 1,00

,18

 

 

 

 

Estimate 

ev <--- Ab ,935 

b <--- Ab ,929 

BI1 <--- BI ,656 

BI2 <--- BI ,781 

BI3 <--- BI ,720 

BI4 <--- BI ,628 



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Standardized Regression Weights: (Group number 1 - Default model) 

Estimate 

BE1 <--- BE ,134 

BE2 <--- BE ,232 

BE3 <--- BE 1,055 

BE4 <--- BE ,455 

 

Appendix B 

 

BE

Ab

SN

BI

b

ev

NB MC

.55

e1

.43

e2

.71

e3
.61

e4

.04 .05

.01

899.95

z3

1182.02

z2

3.77

z4

1

chi-square= 226.136
prob = .000
cmin/df = 9.422
GFI = .768
AGFI = .564
TLI = .741
RMSEA = .281

.05

1
1

1

1

5.83

CI

5.63

7.93

1

4.66

4.70

3.37

z11

.04

.01

.48

1

.30

 

 

Appendix C  

 

Assessment of normality (Group number 1) 

Variable min max skew c.r. kurtosis c.r. 

CI 14,000 25,000 ,208 ,883 -,166 -,353 

BE 8,000 20,000 -,020 -,086 ,451 ,958 

Ab 36,000 225,000 ,556 2,359 ,077 ,164 

SN 49,000 225,000 ,902 3,825 ,661 1,401 

ev 6,000 15,000 ,083 ,354 -,315 -,669 

b 6,000 15,000 ,066 ,280 -,311 -,660 

BI 10,000 20,000 -,108 -,459 -,746 -1,582 



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Variable min max skew c.r. kurtosis c.r. 

MC 6,000 15,000 ,213 ,904 -,157 -,333 

NB 6,000 15,000 ,280 1,186 -,023 -,048 

Multivariate 51,281 18,937 

 

Appendix D 

Bootstrap 

 

BE

Ab

SN

BI

b

ev

NB MC

,55

e1

,43

e2

,71

e3
,61

e4

,04 ,05

,01

899,95

z3

1182,02

z2

3,77

z4

1

chi-square= 27,172
prob = ,205
cmin/df = 1,235
GFI = ,949
AGFI = ,896
TLI = ,993
RMSEA = ,047

,05

1
1

1

1

5,83

CI
,30

5,63

7,93

1

4,66

4,70

3,37

z11

,04

,01

,48

1

- 57

-,29

 

 

The model fit better in 242 bootstrap samples. 

It fit about equally well in 0 bootstrap samples. 

It fit worse or failed to fit in 258 bootstrap samples. 

Testing the null hypothesis that the model is correct, Bollen-Stine bootstrap p = ,517 

 

Appendix E 

 

Z-Score 

Descriptive Statistics 

 N Minimum Maximum Mean Std. Deviation 

Zscore(CI1) 108 -2.70605 1.33432 .0000000 1.00000000 

Zscore(CI2) 108 -2.52334 1.56447 -3.3452889E-16 1.00000000 

Zscore(CI3) 108 -2.32287 2.17704 -3.0619295E-15 1.00000000 

Zscore(CI4) 108 -2.44008 1.51285 .0000000 1.00000000 

Zscore(CI5) 108 -1.33302 1.40920 -1.3445716E-16 1.00000000 

Zscore(CI) 108 -1.96178 2.57245 -1.1079784E-16 1.00000000 



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Zscore(BE1) 108 -3.03427 1.20324 .0000000 1.00000000 

Zscore(BE2) 108 -2.21312 1.66283 -3.5481533E-16 1.00000000 

Zscore(BE3) 108 -1.87760 1.63883 .0000000 1.00000000 

Zscore(BE4) 108 -1.18050 2.42783 -4.5369456E-16 1.00000000 

Zscore(BE) 108 -1.72088 2.77562 .0000000 1.00000000 

Zscore(b1) 108 -1.85733 2.07584 .0000000 1.00000000 

Zscore(b2) 108 -2.90618 1.81363 .0000000 1.00000000 

Zscore(b3) 108 -2.12067 1.78329 -1.8905432E-15 1.00000000 

Zscore(b) 108 -2.24160 2.23239 .0000000 1.00000000 

Zscore(ev1) 108 -2.04799 2.23297 -1.1486601E-15 1.00000000 

Zscore(ev2) 108 -1.83790 2.02885 -2.0027205E-16 1.00000000 

Zscore(ev3) 108 -3.42527 2.05516 .0000000 1.00000000 

Zscore(ev) 108 -2.35508 2.50508 -3.6364543E-15 1.00000000 

Zscore(Ab) 108 -1.93993 2.86621 -7.1187952E-16 1.00000000 

Zscore(NB1) 108 -2.71512 1.88461 .0000000 1.00000000 

Zscore(NB2) 108 -3.01569 1.82735 .0000000 1.00000000 

Zscore(NB3) 108 -2.50276 1.83358 -6.0578801E-16 1.00000000 

Zscore(NB) 108 -2.30177 2.21807 -3.0291404E-15 1.00000000 

Zscore(MC1) 108 -1.74651 2.02596 .0000000 1.00000000 

Zscore(MC2) 108 -2.87243 1.95593 -6.5612697E-16 1.00000000 

Zscore(MC3) 108 -3.58134 1.94415 -1.1846798E-15 1.00000000 

Zscore(MC) 108 -2.18845 2.32784 .0000000 1.00000000 

Zscore(SN) 108 -1.57186 2.79497 .0000000 1.00000000 

Zscore(BI1) 108 -1.67818 1.87562 .0000000 1.00000000 

Zscore(BI2) 108 -2.13844 1.82073 .0000000 1.00000000 

Zscore(BI3) 108 -1.38206 1.43421 -5.0588321E-16 1.00000000 

Zscore(BI4) 108 -2.66158 .91664 .0000000 1.00000000 

Zscore(BI) 108 -2.24465 2.04601 .0000000 1.00000000 

Valid N 

(listwise) 
108     

 

 



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Appendix F 

 

Observations farthest from the centroid (Mahalanobis distance) (Group number 1) 

Observation number Mahalanobis d-squared p1 p2 

79 63,860 ,000 ,000 

78 32,016 ,000 ,000 

31 26,357 ,002 ,001 

80 25,098 ,003 ,000 

61 25,068 ,003 ,000 

73 24,734 ,003 ,000 

76 24,025 ,004 ,000 

92 23,315 ,006 ,000 

88 23,315 ,006 ,000 

95 19,555 ,021 ,000 

44 18,843 ,027 ,000 

28 17,811 ,037 ,001 

41 16,136 ,064 ,021 

6 15,529 ,077 ,039 

48 15,032 ,090 ,061 

23 14,275 ,113 ,157 

5 14,023 ,122 ,159 

38 13,617 ,137 ,217 

97 13,555 ,139 ,166 

35 12,999 ,163 ,300 

45 12,989 ,163 ,223 

74 12,533 ,185 ,344 

50 12,224 ,201 ,415 

67 11,910 ,218 ,499 

72 11,739 ,228 ,507 

30 11,160 ,265 ,748 

94 11,121 ,267 ,694 

25 10,788 ,291 ,793 

71 10,667 ,299 ,787 

105 10,227 ,332 ,906 



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Observation number Mahalanobis d-squared p1 p2 

69 10,120 ,341 ,901 

55 9,774 ,369 ,954 

19 9,725 ,373 ,942 

53 9,399 ,401 ,975 

54 8,870 ,449 ,997 

39 8,824 ,454 ,996 

49 8,802 ,456 ,993 

64 8,775 ,458 ,990 

13 8,123 ,522 1,000 

81 8,096 ,524 1,000 

70 8,029 ,531 ,999 

40 7,958 ,538 ,999 

46 7,772 ,557 1,000 

3 7,743 ,560 ,999 

93 7,736 ,561 ,999 

42 7,659 ,569 ,999 

22 7,548 ,580 ,999 

24 7,377 ,598 1,000 

66 7,231 ,613 1,000 

102 7,224 ,614 ,999 

16 7,181 ,618 ,999 

86 7,148 ,622 ,999 

63 7,120 ,625 ,998 

17 7,062 ,631 ,998 

57 6,994 ,638 ,998 

84 6,585 ,680 1,000 

33 6,498 ,689 1,000 

91 6,489 ,690 1,000 

34 6,486 ,690 ,999 

96 6,377 ,702 1,000 

68 6,090 ,731 1,000 

11 6,072 ,733 1,000 

14 5,857 ,754 1,000 



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Observation number Mahalanobis d-squared p1 p2 

52 5,799 ,760 1,000 

9 5,762 ,764 1,000 

87 5,723 ,767 1,000 

62 5,243 ,813 1,000 

12 5,138 ,822 1,000 

10 5,120 ,824 1,000 

26 5,100 ,825 1,000 

21 4,913 ,842 1,000 

51 4,817 ,850 1,000 

100 4,603 ,867 1,000 

90 4,577 ,870 1,000 

103 4,558 ,871 1,000 

85 4,493 ,876 1,000 

99 4,472 ,878 1,000 

56 4,325 ,889 1,000 

58 4,314 ,890 1,000 

7 4,170 ,900 1,000 

47 4,162 ,900 1,000 

32 4,129 ,903 1,000 

108 4,063 ,907 1,000 

27 3,936 ,916 1,000 

4 3,867 ,920 1,000 

75 3,858 ,921 1,000 

89 3,626 ,934 1,000 

18 3,597 ,936 1,000 

106 3,427 ,945 1,000 

36 3,208 ,955 1,000 

29 3,200 ,956 1,000 

77 3,199 ,956 1,000 

82 3,197 ,956 1,000 

15 3,082 ,961 1,000 

37 2,924 ,967 1,000 

65 2,534 ,980 1,000 



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Observation number Mahalanobis d-squared p1 p2 

8 2,528 ,980 1,000 

104 2,514 ,981 1,000 

2 2,479 ,981 1,000 

60 2,427 ,983 1,000 

 

Appendix G 

 

Sample Covariances (Group number 1) 

 CI BE Ab SN ev b BI MC NB 

CI 5,831 

BE 2,812 4,727 

Ab 49,486 50,609 1532,112

SN 43,224 39,831 815,399 1609,358

ev 2,068 2,152 67,480 34,225 3,397

b 2,613 2,479 72,826 42,321 2,918 4,009

BI 1,908 2,422 40,767 39,030 1,759 1,956 5,382 

MC 1,902 1,949 35,804 73,114 1,471 1,798 1,648 3,934 

NB 2,094 1,688 38,277 71,980 1,641 2,083 1,911 2,697 3,928

 

Condition number=29270,467 

Eigenvalues 

2401,867 757,603 5,142 3,689 2,208 1,164,745,178,082 

Determinant of sample covariance matrix=964089,522 


