According to the 2015 Digi CyberSAFE survey, more than 90% of schoolchildren i1 lalaysia used the Internet, 83% of whom were susceptible to online dangers owing to poo! ipervision (Digi CyberSAFE, 2015). The Royal Malaysian Police (Sexual, Women and Chilc vestigation Unit) and 2015 Digi CyberSAFE school survey reported that 80% of victim: ped by an Internet acquaintance were children between the ages of 10 and 18 years. Ir idition, 26% of cyberbullied schoolchildren were between the ages of 13 and 15 years (Dig yberSAFE, 2015). An overwhelming 80% of child sexual predators searched for their victim: rough mobile chat applications (e.g., WeChat, Kik, BeeTalk, Facebook Messenger, and sc 1). UNICEF revealed that 40% of Internet users in Malaysia are children and young people ider the age of 24 years (UNICEF, 2017). Predators can approach children throug iprotected social media profiles, chat applications, and online gaming forums. The larges itional survey on the cyber safety of schoolchildren in Malaysia showed that more than 70% ‘children had reported being victims of online harassment, and 26% had been cyberbulliec JNICEF, 2017). In 2008, Richard Huckle, a 30-year-old British man, focused people’: tention toward rural Kuala Lumpur, Malaysia. Pretending to be a Christian community ylunteer, he gained the trust of hundreds of children and their adult guardians. In 2016, Huckle as given 23 life sentences after pleading guilty to 71 counts of child offenses, his younges ctim being only 6 months old. He stated on a pedophilic website that impoverished childrer e easier to seduce than middle class Western children. Actions of Parents to Ensure Child Online Safety (MCMC, 2018) Steps in Research Process Conceptual Framework of Cyber Grooming Deterrence Figure 3 Interview Keywords and Interview Direction Data analysis was carried out using NVivo 12 Plus for Windows to identify common themes. A word frequency test was employed for each question. Initial themes were generated by the word cloud visualization tool in the NVivo software. Resulting themes were scrutinized further through elimination to form meaningful categorizations and interpretation using a tree map diagram. Steps taken to analyze the data was based on six stages of thematic analysis by Braun and Clarke (2006) as presented in the following Figure 6: Data Generation through Interview (adapted based on Goldkuhl, 2019) Thematic Analysis Steps (Braun & Clarke, 2006) Figure 6 Demographics of Participants Overall, most of the respondents were female (83%); 48% of the respondents indicated that least one parent was employed, whereas 35% indicated that both parents were employed. Only 17% of the respondents were single parents, most of whom were divorced. Table 6 presents the gender and working status of the interviewed parents. Key Findings Gender and Working Status of Respondents Word Cloud and Tree Map for Family Time Social Media Figure 4 Word Cloud and Tree Map for Social Media Themes for Social Media Magiswary Dorasamy, Maniam Kaliannan, Manimekalai Jambulinga Iqbal Ramadhan, and Ashok Sivaji Internet Time Word Cloud and Tree Map for Internet Time Ulagiswary Dorasamy, Maniam Kaliannan, Manimekalai Jambulingai qbal Ramadhan, and Ashok Sivaji Magiswary Dorasamy, Maniam Kaliannan, Manimekalai Jambulingar [qgbal Ramadhan, and Ashok Sivaji Sharing Secrets Word Cloud and Tree Map for Sharing Secrets Themes for Sharing Secrets Magiswary Dorasamy, Maniam Kaliannan, Manimekalai Jambulingam Iqbal Ramadhan, and Ashok Sivaji Sex Education The above word cloud generated 55 words. Through elimination, we obtained 23 words relevant to sex education. Figure 9 shows the final tree map for sex education. Figure 8 Figure 9 Tree Map for Sex Education Sex Edu - Word Frequency Ulagiswary Dorasamy, Maniam Kaliannan, Manimekalai Jambulingai qbal Ramadhan, and Ashok Sivaji The above word cloud generated 10 words. Through elimination, we obtained 23 word: relevant to unknown friends. Table 13 presents the themes for unknown friends. Word Cloud and Tree Map for Unknown Friends Figure 10 Unknown Friends Aagiswary Dorasamy, Maniam Kaliannan, Manimekalai Jambulingar qbal Ramadhan, and Ashok Sivaji Themes for Unknown Friends Chat Applications Word Cloud and Tree Map for Chat Applications The above word cloud generated 26 words. Through elimination, we obtained 11 wo relevant to chat applications. Table 14 presents the themes for chat applications. Figure 11 Online Grooming Word Cloud and Tree Map for Online Grooming Figure 12 Themes for Online Grooming Table 15 Mapping Emergent Themes to Theory
