Dataset Business Innovativeness and Consumer's New Values Adoption

Data were collected within the quantitative research paradigm using self-administered questionnaire. Items were derived from four theories as the foundation of the research, namely: (1) Diffusion of Innovation Theory ; (2) Social Learning Theory ; (3) Self-identity Theory ; and (4) Consumer-Brand Relationship. Then, each item in the questionnaire was measured using 5-scaled Likert. All respondents were required to fill-up the questionnaire in Google Form within one-week. The total of 18 items were divided into three variables: (1) Loyalty (6 items) ; (2) Involvement (6 items) and (3) New values adoption (6 items). Next, all the respondents’ responses were transferred into excel and SPSS. Procedures for data normalization were performed using Factor Analysis. The results showed all data were normal as the followings: (1) Loyalty (KMO and Barlett’s test = 0.869 ; Initial Eigenvalues = 3.463 or 57% ; Correlation is significant at the 0.01 level ; Cronbach’s alpha = 0.850) ; (2) Involvement (KMO and Barlett’s test = 0.857 ; Initial Eigenvalues =3.913 or 65.22% ; Correlation is significant at the 0.01 level ; Cronbach’s alpha = 0.8910 ; and (3) New Vaues Adoption (KMO and Barlett’s test = 0.869 ; Intial Eigenvalues = 3.756 or 62.60% ; Correlation is significant at the 0.01 level ; Cronbach’s alpha = 0.880). Lastly, two statistical methods were employed, namely: (1) Hierarchical Regression Analysis ; and (2) One-Way ANOVA. Specifically, Hierarchical Regression Analysis was used to measure the contribution of consumer's involvement in the dynamic interactivity between business innovativeness and consumer's new values adoption. While, One-Way ANOVA was used to measure the contribution of consumer's demographic (gender and age range) and present values in consumer's involvement.