Microsoft Word - ELP-V3N1-p57 Economics, Law and Policy ISSN 2576-2060 (Print) ISSN 2576-2052 (Online) Vol. 3, No. 1, 2020 www.scholink.org/ojs/index.php/elp 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 A 108 sample is withdrawn from those who recognize, are interested of and want to buy Dagadu products.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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 58 Published by SCHOLINK INC. 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, www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 59 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 60 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 61 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 62 Published by SCHOLINK INC. 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). www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 63 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 64 Published by SCHOLINK INC. 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). www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 65 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 66 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 67 Published by SCHOLINK INC. 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. 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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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 71 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 72 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 73 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 74 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 75 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 76 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 77 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/elp Economics, Law and Policy Vol. 3, No. 1, 2020 78 Published by SCHOLINK INC. 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