# A Customer-Centric View of E-Commerce Security and Privacy
## 3 Materials and Methods  
The reason for selecting Pakistan as a case setting was based on the huge potential for e-commerce growth due to a large population. We were specifically interested in developing an understanding about how people perceive privacy and security aspects in a developing country, and how these challenges can be addressed by businesses and increase their online dealings. Pakistan is the fifth biggest nation in the world, with a population of more than 225.2 million; by geographical area, it is the 33 rd largest country [84]. Pakistan is the 46 th largest e-commerce market in the world, with annual sales of 4 billion USD in 2020; for the next 4 years, the annual growth rate is expected to increase by another 18% [85]. According to a survey based on data obtained in 2019, the provincial shares of the net e-commerce sales were as follows: the province of Punjab had a 55% share, followed by Sindh with a 36% share, whereas other regions, Khyber Pakhtunkhwa, Balochistan, and Azad Kashmir, contributed to the remaining 9% of total e-commerce sales [86]. Our study is based on quantitative data, which are available in Appendix A, and we used PMT as a theoretical framework [18]; hence, four constructs of PMT, namely, perceived vulnerability, perceived severity, response efficacy, and response cost, were used in our questionnaire. For each of the constructs, we designed closed-ended questions on different aspects of user perception, some of which were rooted in earlier studies [87; 88; 89]. Once the questionnaire was complete, it was reviewed by two colleagues to ensure content validity. After validation, the survey was posted on Google forms. To recruit respondents, we used the nonprobability sampling method and used the snowball sampling technique by contacting our peers; the qualification criterion for the respondent was the use of e-commerce websites for online purchasing. By the end of the survey period, we received 187 responses, of which we discarded one response as the concerned respondent reported not having experience with online shopping. There were 135 male respondents and 51 female respondents, which were divided into four age groups. There were 62 respondents in the age group of 19-25 years, 51 respondents in the age group of 26-35 years, 58 respondents in the age group of 36-50 years, and 15 respondents were above 50 years old. While looking at computer knowledge, among the 186 respondents who participated in the study, 22 were beginner-level users, 63 were middle-level users, and 101 rated themselves as advanced users. The individual questions were mainly based on a five-point Likert scale (strongly disagree, disagree, neutral, disagree, and strongly disagree). Different questions were mapped to relevant constructs. Since SmartPLS only processes numeric data, the complete data file was coded into numeric data and then processed using SmartPLS [90] to identify \begin{table}
\begin{tabular}{c c c} \hline \hline
**PMT Constructs** & **Model Construct** & **Parameters** \\ \hline \multirow{8}{*}{Response efficacy} & \multirow{8}{*}{Motivation factors} & MF1: Data transparency \\  & & MF2: Encryption security \\  & & MF3: Product return policy \\  & & MF4: Dispute guarantee policy \\  & & MF5: An understanding of the terms \\  & & of conditions \\  & & MF6: E-commerce platform’s reputation \\ \hline \multirow{8}{*}{Response cost} & \multirow{8}{*}{Customer worries} & CW1: Concerns over private \\  & & information provision \\  & & CW2: Concerns over information misuse \\ \cline{1-1}  & & CW3: Concerns over lack of control on \\ \cline{1-1}  & & submitted information \\ \cline{1-1}  & & CW4: Concerns over unintended use of \\ \cline{1-1}  & & information by intruders \\ \hline \multirow{2}{*}{Response cost} & \multirow{2}{*}{Customer feelings} & CF1: Users’ feelings about location disclosure \\ \cline{1-1}  & & CF2: Users’ feelings about browsing \\ \cline{1-1}  & & history disclosure \\ \cline{1-1}  & & CF3: Users’ feelings about personal data misuse \\ \hline \hline \end{tabular}
\end{table}