M-commerce adoption among youths in Malaysia: Dataset article

The covid-19 pandemic which took the world by storm changed our behaviour towards m-commerce with enforced movement restrictions across the world. This dataset documents the factors of consideration among Malaysian youths (age 15 to 24 years old) in their intention to adopt m-commerce. Collected from October to November 2020, a total of 396 useable responses were finalized. The questionnaire consists of individual demographic variables and factors which influence the intention of youths to adopt m-commerce in Malaysia. The dataset of demographics and m-commerce related variables can be used to further explore the correlations and description of variables. The dataset is valuable for m-commerce service providers and future works of literature in understanding the behaviour of youths and hence increase the adoption rate of m-commerce among youths.


Specification
Business, Management and Decision Sciences Specific subject area Management of Technology and Innovation Type of data

Value of the Data
• The data presented provides insights for stakeholders in understanding the factors which influence the intention of the Malaysian youths to adopt m-commerce. • The data reveals the relationships between perceived usefulness, perceived ubiquity, perceived ease of use, perceived enjoyment and m-commerce adoption intention. Future research may reuse and draw inferences from the data for comparison. • The role of perceived enjoyment as a mediator in the model offers m-commerce service providers an understanding of the importance of perceived enjoyment among youths. • The data offers valuable perspectives for m-commerce service providers in developing product strategies with focus on features and functions which are sought after by the youths. Product development catered for youths can benefit the service providers with an increased m-commerce adoption rate in the youth market. • The dataset can serve as a basis of comparison for future research on the differences between youths in Malaysia and other cultures in their intention to adopt m-commerce.

Data Description
The questionnaire using an online survey format was distributed to youths in Malaysia who are aged between 15 and 24 years and who possess a smartphone with m-commerce capabilities. The questionnaire covers data on individual demographic variables, perceived usefulness, perceived ubiquity, perceived ease of use, perceived enjoyment and intention to adopt mcommerce with 22 items that measure the related intention to adopt m-commerce factors. All measurement items utilised the five-point Likert scale ranging from "1" (strongly disagree) to "5" (strongly agree). The constructs and their measurement items are presented in Table 1 . The data collected using the questionnaires are prepared into the raw data file appended with this main article as a supplementary document. A total of 396 usable responses were collected.

Measurement items Sources
Perceived ubiquity PQ1 I find using m-commerce applications an efficient way to manage my time [ 16 , 17 ] PQ2 I find using m-commerce application fits any location, whenever I go. PQ3 I find using m-commerce application gives me the ability to overcome spatial limitations PQ4 I find using m-commerce application makes my life easier PQ5 I find using m-commerce application enables me to find information in any place PQ6 I find using m-commerce application fits my schedule well.   Table 2 presents the respondents' demographic variables including gender, age, ethnicity, the highest level of education, mobile usage frequency in a day, reasons for using mobile, and m-commerce transaction frequency in a week. The data was analysed using frequencies and percentages.
Results of reliability and validity analysis are shown in Table 3 . Cronbach's Alpha, Dijkstra-Henseler's rho (rho_A), Compositive Reliability, and Average Variance Extracted (AVE) are indicators used to measure the reliability of all constructs [1][2][3] . The independent variables examined are perceived ubiquity (PQ), perceived usefulness (PU), and perceived ease of use (PEOU) while the mediator is perceived enjoyment (PE), and the dependent variable is the intention to adopt m-commerce (INT). The reliability analysis found the constructs to be above the 0.70 thresholds indicating internal consistency reliability [4][5][6][7] while convergent validity is established with AVE value above 0.5 [8][9][10] . Meanwhile, discriminant validity is also examined using the Fornell-Larcker criterion (refer to Table 4 ) which requires the AVE of each construct to be compared to the squared inter-construct correlation of the same and other constructs in the model [11][12][13] . This is also confirmed in Table 5 displaying the individual items of the construct's outer loadings are higher than the cross-loadings of other constructs [ 10 , 14 ]. Issues for collinearity are also checked by examining the variance inflation factor (VIF) where constructs should display a value lower than 5 as recommended [ 5 , 15 ]. All constructs are well below the threshold of 5 indicating no multicollinearity issues.
The analysis of the structural model is presented in Table 6 and Fig. 4 . The results confirm the strength and significance of the correlations between the constructs by path analysis.  The mediation analysis was employed to study the relationships of perceived enjoyment as a mediator connecting perceived ease of use, perceived usefulness and intention to adopt mcommerce. The results are presented in Table 7 with total effect, direct effect and indirect effect. The bias-corrected intervals for 2.5% and 97.5% supports the mediation relationships as the spread of the interval does not contain the value of zero. Findings showed PE to fully mediate between PU and INT while showing partial mediation between PEOU and INT.
Lastly, the structural model is evaluated for its quality with R 2 , Q 2 and f 2 (refer to Table 8 ). The R 2 value indicates the explanatory power of the structural model. As proposed by past literature [ 20 , 21 ], the R 2 value of 0.75 indicates substantial relationships, 0.5 a moderate relationship and 0.25 a weak relationship [ 5 , 22 , 23 ]. The Stone-Geisser's Q 2 indicator reflects the predictive relevance of the model and is assessed using the blindfolding procedure [14] . The threshold for Q 2 as a rule of thumb is 0.025 as small, 0.025 to 0.50 as a medium, and above  0.50 as large [ 5 , 24 ]. Similarly, the f 2 examines the significant effect of an exogenous variable on the endogenous variable. The values are evaluated using Cohen's threshold of 0.02, 0.15 and 0.35 (weak, moderate and strong) to indicate the effect size [ 25 , 26 ]. On predictive accuracy, the model was examined using PLSpredict (PLS prediction algorithm) to assess the PLS-SEM model and the naïve benchmark model (linear model; LM). This is performed by comparing the root mean square error (RMSE), mean absolute error (MAE) and Q 2 predithmct . The findings ( Table 9 ) indicated a majority of Q 2 predict to be mostly positive and both the RMSE are MAE mostly negative. The errors of the proposed model are smaller compared to a linear model. Therefore, PLS-SEM predictions establish a medium predictive power for the model.

Experimental Design, Materials and Methods
The study adopts a cross-sectional quantitative study. The unit of analysis are individuals between the age of 15 and 24 years old classified as youth and they must possess a smartphone with m-commerce functionality. Non-probability sampling technique was employed for the samples. Purposive sampling and snowball sampling was used due to the important predetermined criteria of; 1. age (between 15 and 24 years) and 2. availability of smartphone with m-commerce functionality. An online questionnaire was developed using Google Form and the link was distributed using emails, messaging apps (e.g., Whatsapp, WeChat, etc.) and social networking sites. The Google Forms were disseminated through social media with the purpose that it is better suited for youths' behaviour. The collection period for the data was conducted from October to November 2020 during the Covid-19 pandemic. Due to the lockdown, google Forms was used as physical face-to-face was not a feasible option. As a result, a total of 396 usable responses were gathered for further analysis. The sample size of 396 exceeded the minimum sample size required as suggested by G * Power (v.3.1.9.7). The G * Power sample size calculator parameters were set at the power of 0.95, alpha value 0.05, effect size 0.15 and 4 predictors. The sample respondents are a fair representation of the youth population in Kuala Lumpur. In a study conducted by the Malaysian Communications and Multimedia Commission, the ages of 20-34 years represent the highest age group to adopt smartphones at 87%, followed by those below 20 years at 86.3% [27] .
The questionnaire covers data on individual demographic variables, perceived usefulness, perceived ubiquity, perceived ease of use, perceived enjoyment and intention of adopting mcommerce among Malaysian youths. A total of 22 items adapted from previous studies measure the related intention to adopt m-commerce factors with all items utilising the Likert scale in five-point from "1" (strongly disagree) to "5" (strongly agree).

Ethical Statement
Consent from all respondents to participate in the survey was requested and obtained through an informed consent statement included in the online survey form. In addition, anonymity was assured to the respondents with no personal data that can be identifiable to that individual. The study obtained clearance from the University's Institutional Ethics Committee and data redistribution policies of the social media platforms have been adhered to.

Declaration of Competing Interest
The authors report that there is no conflict of interest to declare. There is no financial conflict of interest or any other opposing interest that may affect the study reported in this paper.