Benefits and Risks of Online Shopping with Consumer’s Perspective: A Case Study of Pakistan

The over-usage of the internet in Pakistan provides a developing prospect regarding online shopping. With the emerging technology and rapid growth in E-markets, it has become necessary to visualize consumer behavior and the factors that influence consumer behavior in online shopping system. The following paper uses the perceived benefits and risks as a measure to study consumer behavior in online shopping. A survey is conducted from students in Punjab province through google forms. A total of 150 responses were collected through convenience sampling and analyzed using SPSS-25 and Pearson correlation method. The findings show that shopping convenience, product selection, and ease of buying positively affect the buying behavior, whilst financial risk, product risk, and security risk are observed to have negative affect on online shopping behavior. However, none of these relations is found to be strong. The study concludes that consumers perceive certain benefits and risks in online shopping.


Introduction
The Internet has become an emerging technology for the past few decades, and the number of internet users is growing day by day. The versatility of the internet has made an amazing attraction for people around the globe. People use it for different purposes like communication, education and research, entertainment, and e-commerce. Online Shopping refers to the purchasing of goods by utilizing the internet. Consumers can select from a wide range of products available on a specific brand's website and can order these products without walking into stores. The ease associated with online shopping is increasing day-by-day due to the advancement in internet technology. For online shopping, internet can be used for many purposes like one can review the prices and quality of products: able to read opinions of other buyers about the products: may order the customized design, and place the order at any time of the day. Many selling companies also give the option to choose between paying via credit cards and pay after the products are received by the consumer. Online shopping has allowed consumers to buy products quickly and at better prices (Koyuncu & Bhattacharya, 2004;Kuhlmeier & Knight, 2005). This research aims to investigate the benefits and risks that consumers perceive in online shopping. The study is based on the respondents' perception concerning the benefit and risks that they face. The trend of shopping is changing throughout the world with more people adopting online shopping systems due to the facilitation and the rapid advancement of internet technology. This has allowed consumers to make online purchases while being in their homes, offices and even out of town or country. Retailers in advanced countries have developed their portals where customers can buy their desirable products while staying at home and also can make online payments. It has become easy for retailers to spread information about discount offers or new products. Consumers can also access information about their favorite brands with just one click. Many Asian countries including Pakistan have also adopted this innovative and effortless way of shopping. Many factors are significant in understanding the buying behavior of consumers affected that can have positive and negative implications. Consumers may prefer online shopping due to convenience, time, discounted deals, and after-sale services. But at the same time, some consumers may argue over the negative factors like security, quality, and reliability that can affect the way they prefer to shop. It is find out that factors like price, identification, convenience, critical information, discount offers are the main components that affect the online buying behavior of students and professionals. (Dost et al. 2015). Figure 1 shows that people do online shopping because they find the following benefits (Delhagen (1997) cited by Khatibi, Haque, and Karim (2006)) . Figure 1: Benefits of online shopping Source: Delhagen (1997) Literature Review Information technology has made it possible for billions of people to access everything through the internet. The rapid and fast development in technologies also eased up the conduct of business. With the consumers changing behavior, more businesses are being shifted to online mode. The competition among retailers is growing day by day and they are trying to introduce new security techniques, quality, ease of buying, and a variety of products to attract customers. Researchers found that ease of access, variety, and advertisements that encourage consumers to buy online are the encouraging factors: while many consumers think that the price fixation, quality, and the delivery time are the major discouraging factors in online shopping (Hussain et al. 2011). Hussain et al. (2011) also finds that the credit card payment system has enhanced the fashion of impulse buying. Shopping Convenience. Convenience in every aspect of life is becoming a human priority in this age of technology and advancement. Customers are always curious to find new ways of attaining convenience. In this regard, Almarashdeh et al. (2019) collected surveys from 143 participants, and data were analyzed by using SPSS-25. He found that consumers feel more convenient in using mobile apps for online shopping as compared to websites for shopping. The author used the following variables in his convenience-based study: search convenience, access convenience, service recovery convenience, and behavioral intention to use. In addition to that, Beauchamp and Ponder (2010) show that people feel more convenient while online shopping and making transactions rather than in-store shopping. The data on four dimensions including assess, search, transaction, and possession, was collected through convenient sampling as well as from a national online panel. However, the author highlighted that people are less aware of the types of convenience that are available to them. Similarly, Jih (2007) discusses the relation between shopping convenience and online shopping intention. The primary data was collected from the students by convenience sampling method on the five dimensions that are place, acquisition, use, execution, and time. By using (ANOVA) canonical, correlation analysis, and regression analysis, it is figured out that convenience has a significant positive impact on shopping intention. H1: Shopping convenience is positively related to consumer online buying behavior Product Selection. Customers usually search for perfect quality products on the internet. Likewise, the brands that display a vast number of products with better quality are considered more successful. Guo surveys from 350 online shoppers to verify the impact of product variety and product quality on consumer satisfaction. He found that consumer satisfaction is positively related to product quality and product selection in online shopping. Previous researchers found different variables that encourage people to shop online, and product characteristics are one of them. Moreover, wider variety attracts consumer attention (Guo et al. 2012). Bin Dost et al. (2015 found that product variety has significant impact on consumer buying behavior. The author illustrated that youth buys more products online if they find more variety.

H2: Product selection is positively related to consumer online buying behavior
Ease/Comfort of Shopping. Researchers are always very eager to find the factors that compel people to shop online. Different people shop for different reasons. Their shopping reasons might be leisure, the pleasure of bargaining, physical activity, and outside experiences (Tauber 1995). Likewise, Ramayah and Ignatius (2005) developed the relation between intention to shop online and perceived enjoyment. He found that perceived enjoyment (β = 0.32, p<0.05) has not only a positive impact but also an important driver of consumer intention to shop. By using Technology Acceptance Model author shows that consumer behavior is not affected by ease of use and usefulness but with the previous online shopping experience and trust. Comfort and ease of shopping usually depends upon trust. Everyone wants comfort in his life. Comfort makes people happy and enables them to work hard and be productive. Sometimes it makes people lazy. Similarly, people want comfort in shopping not just in traditional one but also in E-shopping (Monsuwe et al. 2004). Cheema measured the effect of perceived enjoyment on shopping intention by using the regression analysis. He surveyed 150 people that includes students and professionals. It is found that both ease of use and enjoyment have significant and positive impacts on consumer shopping intentions in Pakistan (Cheema et al. 2013). Akhter (2015) found internet usage comfort by using an ordinal regression model. He shows that internet usage brings comfort which has a significant positive impact on online shopping. Customers enjoy while searching for different products as they feel more comfortable in online shopping. It is no more difficult to try new experiences, products, and custom their designs in online shopping. H3: Ease of buying is positively related to consumer online buying behavior Financial Risk. There is a lot of debate going on about the negative aspects of online shopping and financial risk is one of them. Financial risk plays an important role in consumer decision making. This loss is termed as a net loss of money or money that might be lost. A negative impact of financial risk on consumer online shopping has been proved by previous literature. More often, people stop online shopping due to financial insecurities (Masoud & Management 2013). Similarly, Pi and Sangruang (2011) found the relationship between financial risk and internet shopping. By using the Least Square method he concluded that not just the financial risk but other risks like social, time, and convenience risks are also negatively related to online shopping. H4: Financial risk is negatively related to consumer online buying behavior Product Risk. Sometimes, online shopping websites fail to meet the consumer demand since it is difficult to judge their preferences on limited available information. Similarly, consumers can't trust the displayed product due to limited information. Most people do not buy online because they cannot examine the product physically, test the product, and lack product information (Kaur et al. 2015). In this way, Wai found a positive relation between product risk and consumer online shopping behavior (Wai et al. 2019). On the contrary, Bhatti used Confirmatory factor analysis (CFA) and structural equation modeling (SEM) techniques, and showed that product risk does not has a significant relation with online shopping (Bhatti et al. 2018) H5: Product risk is negatively related to consumer online buying behavior Inconvenience/security Risk. Safety issues and the availability of credit cards are the main barriers in online shopping (Bashir 2013). People in Pakistan are often reluctant to share their confidential information like credit card numbers, email addresses, and identity cards due to various internet scams that have caused financial damage to many people. This has made trust and confidence the main factors that affect the consumer buying behavior in Pakistan (Nazir et al. 2012). It has put a limit on the online shopping trend. The majority of people also lack credit cards due to financial illiteracy. Smith and Rupp (2003) identified that social, cultural, and psychological factors also affect the purchase and post-purchase decisions. The authors find that security and privacy are the main factors that affect online buying behavior. H6: Security Risk is negatively related to consumer online buying behavior

Research Methodology
The study is concerned with the investigation of the benefits and risks that consumers may encounter during online shopping. Seven variables i.e., shopping convenience, product selection, consumer buying behavior, ease, financial risk, product risk, security risk, are retrieved from literature to determine the consumer's perspective for benefits and risks. Consumer buying behavior is taken as a dependent variable that is being affected by six independent variables. A five-point Likert scale was used in which respondents could specify their level of agreement concerning the statement i.e.
(1) Strongly Disagree (2) Disagree (3) Neutral (4) Agree (5) Strongly Agree The data is collected through Convenience sampling from students in academia, in Punjab province. Students are preferred in this study because they have been successfully used for primary research in many Web-related studies, [e.g. (Lee et al. 2005 ;Liu 2003)]. Before its distribution, the questionnaire was pretested to identify possible problems in terms of clarity and accuracy. Based on comments and feedback, several changes were made in order to improve the presentation of the items. The variables and questionnaire for the study are adapted from (Forsythe et al. 2006).

Theoretical Framework
The theoretical framework is given in Figure 2. Consumer buying behavior is taken as the dependent variable, which is being affected by other variables.

Sampling Frame and Data Collection
The data through questionnaires were collected from students belonging to a different gender, age, and education groups. Students were posed with close-ended questions. The questionnaire was uploaded on google forms, from where anyone with the link could open the form and give his/her response. 150 students returned the response with complete answers. Demographic profile.

Consumer
Buying Behavior

Results
To determine the statistical significance between independent variables, a chi-square test is performed using SPSS.
Chi-Square Results Gender*age cross-tabulation H0 = Gender and age are independent variable H1 = Gender and age are significantly associated Result = p<0.05, which means H0 is rejected and therefore gender and age are significantly associated.
Asymptotic Significance (2-sided) Pearson Chi-Square .026 Gender*education cross-tabulation H0 = Gender and education are independent variable H1 = Gender and education are significantly associated Result = p>0.05, which means H1 is rejected, and therefore gender and education are independent. Age*education cross-tabulation H0 = Age and education are independent variable H1 = Age and education are significantly associated Result = p<0.05, which means H0 is rejected and therefore age and education are significantly associated.

Validity Statistics
To check the validity of the questionnaire, the construct validity method is used. The results from the Pearson bivariate correlation show that all items have obtained a p-value greater than the Critical P-value at 0.01 significance level (   Table 2). .000 0.635 For N =150 *Significance at 0.01 level (two-tailed) For N= 150, the degree of freedom is 148 as df = N-2. The obtained p-value for all constructs is greater than the critical value i.e., 0.208, and is highly significant, so all of the items are valid and can be subjected to further analysis. Asymptotic Significance (2-sided) Pearson Chi-Square .066 Asymptotic Significance (2-sided) Pearson Chi-Square .000

Cronbach's Alpha Test
Cronbach's alpha test is used to assess the reliability of test items. The reliability test is applied to all seven sections of the variables. The results from the test ( Table 3) show that all variables have reliability coefficients greater than 0.7. So, according to the thumb rule, reliability of 0.70 or higher is acceptable.  Table 2 provides the results of two tests i.e. KMO and Bartlett's test. To check the adequacy of data for factor analysis, a KMO test is performed. The findings show that the KMO value is greater than 0.5, which means the sample is adequate. The alternate hypothesis is that the variances are not equal for at least one pair or more: H1: σ1 2 ≠ σ2 2 ≠… ≠ σk 2 The results of Bartlett's test show that the value of significance level is less than 0.05, which means there is at least one significant correlation between two of the items. So, it rejects the null hypothesis, and a factor analysis would be useful with the data. .000 a. Based on correlations Hypothesis Testing H1: Shopping convenience is positively related to consumer online buying behavior Since p = 0.000 is less than the benchmark value of 0.05, H1 is accepted. The positive Pearson correlation .521 between shopping convenience and consumer shopping behavior is moderate. The summary of Pearson correlation is given in Table 5.  (2-tailed) .000 N 150 **Correlation is significant at the 0.01 level (2-tailed). H2: Product selection is positively related to consumer online buying behavior Since p = 0.000 is less than the benchmark value of 0.05, H2 is accepted. The positive Pearson correlation .544 between Product selection and consumer shopping behavior is moderate. The summary of Pearson correlation is given in Table 6.  -tailed) .000 N 150 **Correlation is significant at the 0.01 level (2-tailed). H3: Ease is positively related to consumer online buying behavior Since p = 0.000 is less than the benchmark value of 0.05, H3 is accepted. The positive Pearson correlation .527 between Ease and consumer shopping behavior is moderate. The summary of Pearson correlation is given in Table 7.  -tailed) .000 N 150 **Correlation is significant at the 0.01 level (2-tailed). H4: Financial risk is negatively related to consumer online buying behavior Since p = 0.001 is less than the benchmark value of 0.05, H4 is accepted. The positive Pearson correlation .262 between shopping convenience and consumer shopping behavior is not significant. The summary of Pearson correlation is given in Table 8.  -tailed) .001 N 150 **Correlation is significant at the 0.01 level (2-tailed). H5: Product risk is negatively related to consumer online buying behavior Since p = 0.000 is less than the benchmark value of 0.05, H5 is accepted. The positive Pearson correlation .331 between Product Risk and consumer shopping behavior is not significant. The summary of Pearson correlation is given in Table 9.  -tailed) .000 N 150 **Correlation is significant at the 0.01 level (2-tailed). H6: Security Risk is negatively related to consumer online buying behavior Since p = 0.000, which is less than the benchmark value of 0.05, H6 is accepted. The positive Pearson correlation .346 between security and consumer shopping behavior is not significant. The summary of Pearson correlation is given in Table 10.  (2-tailed) .000 N 150 **Correlation is significant at the 0.01 level (2-tailed). So, all the hypothesis are accepted, and Table 11 gives a brief summary of all the results.

Conclusion and Recommendations
This study provides empirical support to the benefits and risks that affect the consumers' online buying behavior. It is found that shopping convenience, product selection, and ease of buying give a positive incentive to consumers to shop online. On the other hand, product risk, financial risk are the major hindrances in the way of online buyers. It is concluded that consumers perceive certain benefits and risks in online shopping, which affects their choice of whether to shop online or in-person. Based on the findings, it is recommended that to enhance customer purchase intentions, online stores should work on developing marketing strategies to address the trustworthiness, reliability, and quality of the products. Online stores can devote valuable corporate resources to the important eservice quality attributes identified by this study.