The dataset for validation of customer inspiration construct in Malaysian context

This study intended to validate customer inspiration (CI)in Malaysian/developing country context. Data were collected from two different respondents for two studies - from Millennial customers of the auto industry and Generation Z customers of the smartphone industry. The survey conducted through a standardized and structured questionnaire. The variables of the both studies were customer-defined market orientation (MO) (customer orientation, competitor orientation, and interfunctional coordination), CI (inspired-by and inspired-to), and customer loyalty (CL). This research strategy, in terms of quantity, is descriptive and correlational. Statistical analysis of the data was carried out, using ADANCO 2.0. The finding of the study suggests all results of data 1 and data 2 were significant, and CI mediates the sub-constructs of MO with CL.


Demographic characteristics of respondents
In order to verify the construct validation of customer inspiration, the data collected from two generations members e 'Millennial' and 'Generation Z' in two survey studies (see Fig. 1). The reason to choose Millennial to get response for the auto industry as they reached the age of job/business, therefore, most of them own the vehicle to commute in Malaysia. On the other hand, Generation Z members getting education and living away from their hometown/parents, hence, all respondent had smartphone to communicate with family and friends. The respondents belonged to 11 states of Malaysia. The data consist of 271 responses of Millennial in data 1, and 252 responses of Generation Z in data 2 [4]. recommended that number of respondents should be at least 100 [5]. argued that the number of respondents should be at least 200, and [6] claimed the minimum desirable number of respondents to be 250 [7] offered a rough rating scale for adequate sample sizes in factor analysis: 100 ¼ poor, 200 ¼ fair, 300 ¼ good, 500 ¼ very good, 1000 or more ¼ excellent.
The data collection took 42 days for both studies. The questionnaire was self administrative and in the English language. Data collection adhere all ethical consideration suggested by prominent studies [8,9]. Tables 1 and 2 illustrate the details of the demographics of respondents of both studies.

Experimental design, materials and methods
All items were adopted from reliable studies measure through reflective scale. Table 3 and Table 4 provide the constructs detail, source, coding, loading values, reliability and convergent validity of both studies. Table 5 and Table 6 show the discriminant validity of data 1 and data 2. Furthermore, all items gauge on five-points Likert scale. A PLS-SEM was applied using ADANCO 2.0. Present study model consists of CuO, CoO, and InF (sub-constructs of CDMO), InB and InT (sub-constructs of CI) and CL. All measures were subjected to check the reliability and validity. We employ J€ oreskog's rho to check reliability [10]. We adopt convergent validity, with average variance extracted (AVE) and discriminant validity, with the Heterotrait-Monotrait ratio of correlation (HTMT) [10]. The minimum threshold of J€ oreskog's rho is more than 0.7, AVE is at most 0.85, and HTMT at least 0.5. All results are delineated evidence for the proposed model constructs, which allow further analysis [11]. For data 1, the J€ oreskog's rho value is between 0.8555 and 0.9259, AVE is between 0.5853 and 0.7958, and HTMT correlation is at least 0.5 between all variables. For data 2, the J€ oreskog's rho value is between 0.8138 and 0.9275, AVE is between 0.6394 and 0.7984, and HTMT correlation is at least 0.5 between all variables. Value of the data This data validates the customer inspiration tool in Malaysian/developing country context. This data could use for comparison of Millennial and Generation X opinions about customer-defined market orientation, customer inspiration, and customer loyalty with other studies in the field and may part of potential meta-analyses.
The datasets provide information about auto industry and the smartphone industry. The paper allows other researchers to extend the statistical analysis i.e. ANOVA.
The all direct and indirect relationships were significant, portray in Tables 7 and 8 for both studies.