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International Journal of Information System and Engineering
www.ftms.edu.my/journals/index.php/journals/ijise
Vol. 4 (No.2), November, 2016
ISSN: 2289-7615 DOI: 10.24924/ijise/2016.11/v4.iss2/69.81
This work is licensed under a Creative Commons Attribution 4.0 International License.
Research Paper
INFLUENCE OF CUSTOMERS’ PERCEIVED RISK ON ONLINE SHOPPING INTENTION IN MALAYSIA’S APPAREL INDUSTRY
Tolulope Olaide Folarin [email protected]
Emmanuel Abiodun Ogundare FTMS College, Malaysia
Abstract The main purpose of this research is to examine the influence of customers’ perceived risk on online shopping intention in Malaysia, specifically on Malaysia’s apparel industry.To achieve this purpose, this research deduced its conceptual framework from past researches, using five independent variables which are; information privacy risk, security risk, delivery risk, financial risk and quality risk with online shopping intention as the only dependent variable (Almousa, 2011; Masoud, 2013; Dai, Forsythe and Kwon, 2014). The conceptualized framework was used to develop a multivariate likert-scale questionnaire with a scale of 1 to 5 and the questionnaire was used to conduct a primary research which solicited responses from 307 customers’ who still prefer to shop for apparel in bricks and mortar than shopping online. Thus, convenience sampling which is a type of non-probability sampling that enables easy selection of the target audience was employed (Saunders, Lewis and Thornhill, 2009). The data collected was quantitatively analyzed via SPSS 20.0, the bivariate multiple regression result shows information privacy risk, financial risk and quality risk have significant influence on online shopping intention in Malaysia’s apparel industry, however, security risk and delivery does not have significant influence on online shopping intention in Malaysia’s apparel industry. Conclusively, this research had been able to help businesses to identify the customers’ perceived risk that inhibit Malaysia customers’ from shopping online and it gave recommendations on how businesses can minimize these risks. Finally, future research should employ a longitudinal study approach in order to expand the scope of the research beyond the five variables employed for this study, as such, researcher should combine both questionnaire and interview and make use of a larger sample size which will increase the reliability and generalization of the result to the whole Malaysia customers’ at large.
Key Terms: Customers’ perceived risk, influence, online shopping intention, Malaysia apparel industry
1. Introduction The world’s marketing environment has greatly been impacted by the emergence of internet which had provided businesses the opportunity to expand and enhance their ability to reach
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customers in different locations, both locally and globally through electronic commerce (Masoud, 2013). The availability of internet access and rapid possession of computers by numerous households facilitate product search, product information and extensive acceptance of ecommerce (Ganguly, Dash and Cyr, 2014). According to Moore (2008) the Internet is proving to be a great equalizer, giving small firms a presence comparable to that of the giants in the marketplace. Thus, many businesses operate Business-to-Consumer (B2C) ecommerce which involves selling products or services to the finally consumers via internet (Moore, 2008). The benefits of ecommerce which are 24hours accessibility to products/service, convenience, speedy process, time saving, among others had increased the usage of online shopping among end users (George, 2012). According to the research by Leong and Lee (2014) on the percentage of online shopping in developing countries, it was discovered that online shopping in Philippines had been carried out by 62% of Filipino’s internet users while 61% of Indonesian internet users shop online, 58% of Thailand and Vietnamese internet users make use of online shopping, however, compared to these developing countries, online shopping had been carried out in Malaysia by only 47% of Malaysian Internet users. This percentage of online shopping in Malaysia is relatively low, thus it brought about the purpose of this study which is to identify and examine the influence of perceived risk on shopping intention in Malaysia, specifically in Malaysia’s apparel industry. This research aims to examine the influence of customers’ perceived risk on online shopping intention in Malaysia, specifically on Malaysia’s apparel industry. To achieve this aim, the following objectives are proposed:
i. To investigate if information privacy risk influence online shopping intention ii. To examine if security risk influence online shopping intention
iii. To explore if delivery risk influence online shopping intention iv. To ascertain if financial risk influence online shopping intention v. To examine if quality risk influence online shopping intention
Structure of research paper This paper is subdivided into five sections: section one which is the introduction is discussed above. Session two reviewed current and relevant literatures as regard the influence of perceived risk on online shopping intention. Session three focuses on the methodology employed. Section four quantifies and analyzes participants’ responses. Finally, section five draws conclusion, cite implication for practices, gave recommendation and suggest future research. 2. Literature Review Perceived risk according to Samadi and Yaghoob-Nejadi (2009) is the subjective belief of an individual on possible negative consequences from his/her decision. It was also defined by Schiffman (2007) as the uncertainty customers are liable to encounter if they do not anticipate the consequences of their purchase decisions. According to Schierz, Schilk and Wirtz (2010) perceived risk is the expectations of losses, the larger the expectations of losses are, the higher the degree of risk consumers will perceive. Perceived risk was also defined by Flick (2009) as a two-dimensional construct comprising the uncertainty involved in a purchase decision and the consequences of taking an unfavorable action. Online Shopping intention is defined by Pavlou (2003) as the willingness or intends of customers’ to purchase goods and services through the website. It was also defined by Fygenson and Pavlou (2006) as a state or situation when a customer desires or plans to purchase certain product or service from an e-shop in future.
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Since Bauer (1960) introduced risk-taking behavior in marketing literature as a possible measure of consumer attitude towards online purchase, it had captivated the attention of numerous researchers and many studies had been carried out to examine the influence of perceived risk on shopping intension. In 2011, a research by Almousa (2011) on 300 Saudi Arabian customers investigated perceived risk in apparel online shopping by conducting a web-based survey that measured the perception of customers’ on the six types of risk connected with online apparel shopping and their influence on purchase intention. Thus six variables which are performance risk, financial risk, psychological risk, security risk, time risk and privacy risk were conceptualized, it was found that time risk and performance risk strongly and negatively influenced online shopping intention; it was also found that “privacy risk and security risk have negative effects on online shopping intention” . This research by Almousa (2011) is a relevant study as it specified and encapsulated perceived risk in apparel online shopping however, the reliability and authenticity of the result cannot be ascertained because the research used a non-probability sampling techniques by conducting a web-based survey which according to Saunders, Lewis and Thornhill, (2009) cannot be used as a means of generalization to the population at large. In 2013, a study by Masoud (2013) investigated the effect of perceived risk on online shopping intention in Jordan using a sample size of 395 respondents, majorly customers who are online shoppers, as such, he conceptualized five variables which are; time risk, financial risk, information security risk, delivery risk and product risk. His study revealed that financial risk, product risk, information security risk and delivery risk negatively affect online shopping intention, and he concludes that online merchants should be aware of customers’ perceived risk and strategies adequately to avert these risks. Based on literature, the theoretical framework for this research would be conceptualized based on the above-mentioned findings, using information privacy risk, security risk, delivery risk, financial risk and quality risk because these variables are vastly recognized as the customers perceived risk variables that hinders purchase intention and these variables had been mostly researched by experts in this field, as such, the variables are well suited for this research.
Figure 1.1: Adapted from Masoud (2013, Forsythe and Kwon (2014), Zhang et. al, (2012).
Information privacy risk is the probability of losing personal information which is of great concern to the public (Masoud, 2013). According to Zhang, et al. (2012) information privacy risk usually occur when customers’ ingress websites by providing their personal identifiable information in other to purchase goods or services and the websites operators gather and
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decide to disseminate customers’ information to the public. In Zviran (2008) view, lack of trust in virtual environment makes some privacy conscious shoppers provide incomplete personal information. Smith (2010) stated that improper access to digitally stored personal information, collection of personal information and unauthorized secondary use of personal information are the three main information privacy risk that hinder customers from shopping online. Thus customers are reluctant to embrace online shopping because as compared to brick and mortar, information privacy is difficult to protect in online environment (Smith, 2010). Similarly, Dai, Forsythe and Kwon (2014) revealed that customers that have experienced misuse of personal information by website operators find it difficult to go back to online shopping in future and they are prone to discourage other customers’ online shopping intention. Thus, H1: Information privacy risk has a significant influence on online shopping intention. A study by Thompson and Liu (2011) found that there is a significant relationship between security risk and intention to purchase online. Martin and Camarero (2009) showed that customers avoid online shopping not because it is not convenient but because most customers are scared of losing their credit card information to credit card theft. Thus Martin and Camarero (2009) conclude that security risk has significant influence on online shopping intention. Hence, H2: Security risk has significant influence on online shopping intention. Bauer et al. (2006) showed that customers’ perceived delivery to be risky because some delivery agents hardly meet up the time frame scheduled for delivery and in most cases they deliver incomplete package where some of the products paid for are absent; thus, they conclude that delivery risk has significant influence on online shopping intention. Thus, H3: Delivery risk has significant influence on online shopping intention Masoud (2013) found that any form of financial loss, either through credit card fraud, lesser quality or product that did not perform as expected, deters online shopping and has strong negative effect on online shopping intention. Therefore, H4: Financial risk has significant influence on online shopping intention Zhang, et al. (2012) revealed that quality risk has negative influence on online shopping intention because when products purchased online do not perform to satisfy customers expectation and customers give unsatisfied review, their reviews tend to discourage other intending shoppers but if there were positive reviews, intending shoppers will be encourage to shop online. Also, Garbarino and Strahilevitz (2014) states that quality risk is proportional to performance risk which inhibits intention to purchase online. Thus H5: Quality Risk has significant influence on online shopping intention.
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3. Research Design and Methodology (important for research papers) Study Setting In order to attain the aim of this study and to avoid unbiased data, this research target the right sample, which is, customers’ who still prefer to shop for apparels in brick and mortar; however, due to the large population of apparel customers’ in Malaysia, this research will not be able to get response from all the customers’ in Malaysia (Wilson, 2010). As such, the researcher met with some participants at the heart of Kuala Lumpur; Bukit Bintang which is Kuala Lumpur's trendiest fashion shopping and entertainment district, where shopping malls like Pavilion, Lot 10, Starhill Gallery, Bukit Bintang Plaza and Sungai Wang Plaza are located. Research Methodology Research method refers to the behavior and instruments used in selecting and constructing research techniques (Wilson, 2010). Saunders, Lewis and Thornhill (2012) argued that there are only two basic research method, which are quantitative method and qualitative method. Quantitative method focuses on describing and analyzing phenomenon by using numerical data and empirical models, it is used to quantify attitudes or behaviors, measure variables in which they hinge, compare and point out correlations (Wilson, 2010; Mujis, 2010). On the other hand, qualitative method examines the constitution of meaning in everyday social phenomena, it seeks to preserve the form and consent of human behavior and to analyze its qualities, rather than subject it to mathematical or other formal transformations (Potter, 1996). Thus, in regards to this research, quantitative method is the selected data collection method because it explains phenomena by collecting data in quantitative form which can be subjected to rigorous quantitative analysis in a formal and rigid fashion and it is specifically suitable for testing hypothesis (Kothari, 2004). Similarly, Mujis (2010) argued that “quantitative method is deductive, measurable and it is essential in business research as parameters relating to the population as a whole can be estimated easily by using data from samples. As such, quantitative analysis will enable this research to generalize its findings base on the result from selected sample. Sample size and Sampling Method In order to attain the aim of this study and to avoid unbiased data, this research target the right sample, which is, customers’ who still prefer to shop for apparels in brick and mortar; however, due to the large population of apparel customers’ in Malaysia, this research will not be able to get response from all the customers’ in Malaysia (Wilson, 2010). Hence, probability sampling is not deemed suitable for this research as the researcher could not have access to the whole population and sampling frame that enlist the whole population cannot be accessed because in probability sampling the entire population as to be known, each individual in the population has a specifiable probability of selection and sampling occurs by a random process based on the probabilities (Gravetter and Forzano, 2015). Therefore, convenience sampling which is a type of non-probability sampling is appropriate sampling techniques for this research because it enables easy selection of the target audience as participants can be selected based on their availability and willingness to respond (Saunders, Lewis and Thornhill, 2009). According to Hair et al, (2011) for researchers to limit data error and to avoid unbiased response, the sample size of a research has to be between 300 to 500 participants’. As such, this research solicited responses form 307 participants’ some of which are Asians, Africans and others. To achieve this, the researcher visited Kuala Lumpur’s biggest malls; Pavilion and Berjaya Times Square to distribute questionnaires to customers’ shopping in the mall. Researcher frequents these shopping malls for two weeks before being able to gather 307 responses from 307 different participants.
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According to Wilson (2010) questionnaire is the most suitable for survey strategy and is mostly used for quantitative method. Similarly, previous researches on the influence of perceived risk on online shopping intention made used of questionnaires (Samadi and Yaghoob-Nejadi, 2009; Zhang, et al. 2012). Thus, questionnaire is the collection tool used for this research, though, the questionnaire was not adopted from a particular study, new questionnaire was developed by extracting and modifying questions from prior researches to examine the influence of perceived risk on online shopping intention in Malaysia apparel industry and to ascertain the validity and reliability of the new questionnaire it was pilot-tested (Potter, 1996). This questionnaire which was designed for the purpose of collecting data from participant is divided into three sections, however, to ease the collection of quality data, the questionnaire was made readable by using English language which is the lingual franker, the layout was well structure and the questions were arranged accordingly (Bryman, 2001).
Figure 3.1: Socio-Demographic profile of the participants
Data Analysis Methods IBM SPSS 20.0 version was chosen for the statistical analysis of this research instead of AMOS, Excel and E-VIEWS because with SPSS, researcher can easily import data, have asses to full set of statistical tests, make use of flexible pivot tables that are easy to work with, analyze frequencies and descriptive statistics with ease, produce various graphs and chart automatically, save time and increase productivity with the help of SPSS tutorial which aids better understanding of statistical results (Gravetter and Forzano, 2015). To gather unbiased data from the target population about the influence of perceived risk on online shopping intentions in Malaysia’s apparel industry, that is, the perceived risk that inhibit
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Malaysians from buying fashion apparel online, this research had created a multivariate questionnaire. Hence, the following statistical test would be performed. Regression analysis is a conceptual method for investigating functional relationship among variables (Chatterjee and Hadi, 2015). For the purpose of this research, Multiple-linear regression which is a statistical method for testing and estimating casual effects between variables using a set of statistical data is employed (Howell, 1992). Correlation analysis is used to measure the strength or degree to which two or more variables are related (Dalgaard, 2008). Many statistical techniques can be used to compute correlation coefficients including Pearson’s product-moment, Spearman’s Rho and Kendall’s Tau, however for the purpose of this study, Pearson’s correlation which is a bivariate statistic that measures how strongly two variables are related to one another will be used (Kleinpeter, 2007; John, 2003). Ethical Considerations To carry out a research, several ethical related issues concerning the target population, gathering of unbiased data and how collected data will be analyzed have to be considered. As such, this research considered the following; To avoid deceit, participants’ are entitled to know the purpose of the research before participating, thus, the participant information sheet which is on the first page of the questionnaire educates the participants’ about the aim of this research and it was made known to them that the research is strictly for academic purpose. The participants’ were also assured that the data they provide will only serve as the main data set for this research (Wilson, 2010).
4. Results and Discussion
Reliability
Figure 4.1: Reliability testing
If any issues happen, we will discuss first what we need to do, what is the first step
is, so they give idea, from that we check which idea is good or which idea is bad .
We have meeting or through emails
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Descriptive statistics
Figure 4.2: Descriptive statistics
Correlation In regards to this research, Pearson’s correlation is employed to explore the probability of significant correlation between the independent variables (which include Information privacy risk, security risk, delivery risk, financial risk, and quality risk) and dependent variable (online shopping intention). This will enable the researcher to ascertain the relatedness or relationship between the independent and dependent variables. The Pearson’s correlation coefficient (R) for this study is presented in table 4.9 below, where Information privacy risk’s R = 0.694 indicating that there is a moderate 69% relationship between information privacy risk and online shopping intention, thus there is a significant relationship between information privacy risk and online shopping intention. In terms of security risk, R = 0.636, which signifies that there is a moderate 64% relationship between security risk and online shopping intention, hence, there is a significant relationship between security risk and online shopping intention. For delivery risk, R = 0.668, it shows that there is a moderate 67% relationship between delivery risk and online shopping intention; therefore, there is a significant relationship between delivery risk and online shopping intention. Considering financial risk with its R = 0.722, it signifies that there is a strong 72% relationship between financial risk and online shopping intention, thus, there is a significant relationship between financial risk and online shopping intention. Finally, in terms of quality risk with R = 0.740 it shows that there is a strong 74% relationship between quality risk and online shopping intention, therefore it can be concluded that there is a significant relationship between quality risk and online shopping intention. According to Pett et al. (2003) argument, it is evident that none of the variables has correlation coefficient of ±0.00 to ±0.29; this signifies that none of the independent variables have weak relationship with the dependent variable. However, with all the variables having correlation coefficient between minimum of +0.636 and maximum of +0.740, it implies that the strength of the relationship between the independent variables and dependent variable is from moderate to strong, representing significant relationships.
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Figure 4.3: Correlations Regression analysis According to Jain and Jhunjhunwala (2007) regression analysis is the study of the nature of relationships between variables so that one may be able to predict the unknown value of one variable for a known value of another variable. In Morgan’s (2013) view, regression analysis measures the nature and the extent of the relationship between two or more variables, thus enable one to make predictions. For this research regression analysis is employed to predict online shopping intention using five independent variables of perceived risk which are information privacy risk, security risk, delivery risk, financial risk and quality risk.
Figure 4.4: Regression
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Discussion Hypothesis 1: Information privacy risk has a significant influence on online shopping intention. Information privacy risk is supported by the regression analysis as its t-value of the coefficient of beta and p-value meet its set criteria, it has a t-value of 3.809, which is greater than +1.96 and its p-value of 0.000 is lesser than 0.05. This implies that information privacy risk has a significant influence on online shopping intention. Thus, hypothesis 1 which states that information privacy risk has a significant influence on online shopping intention is accepted. This discovery is consistent with prior discoveries of Tsai and Yeh (2010), Dai, Forsythe and Kwon (2014), Noort et al. (2008) and Chapell (2005) who found that over 69% of United State online shoppers minimized their online purchases because of concerns related toprivacy and safety of their personal information, thus, Chapell concludes that information privacy risk deters online shopping intention. Hypothesis 2: Security risk has significant influence on online shopping intention. Security risk has a t-value of 0.929 which is lesser than +1.96 and a p-value of 0.353, greater than 0.05, this suggests that security risk is not supported by the regression analysis as the t-value and p-value did not meet its set criteria. It also specifies that security risk does not have influence on online shopping intention. Hence, hypothesis 2 which state that security risk has significant influence on online shopping intention is rejected. This result aligns with that of Ling, Chai and Piew (2010) and Mann and Jha (2015) who found that 60% of online shoppers in India have trusted merchant they purchase from without having the fear of identity or credit card theft, thus Mann and Jha (2015) concluded that security risk does not inhibit online shopping intention.
Hypothesis 3: Delivery risk has significant influence on online shopping intention. Delivery risk is not supported by the regression analysis as the t-value and p-value did not meet its set criteria, it has a t-value of 1.432 which is lesser than +1.96 and a p-value of 0.153 which is greater than 0.05. This implies that delivery risk does not have influence on online shopping intention. As such, this research rejects hypothesis 3 that states that delivery risk has significant influence on online shopping intention. This discovery correspond with that of Javadi et al. (2012) who discovered that delivery risk does not have negative effect on online shopping intention because delay in delivery does not mean retailer is not willing to deliver the product customers’ have paid for because some delay might be as a result of custom check which will be made known to customers’ through the tracking details. Hypothesis 4: Financial risk has significant influence on online shopping intention. Financial risk with t-value of 3.522 is greater than +1.96 and its p-value of 0.000 is lesser than 0.05. This signifies that financial risk is supported by the regression analysis as its t-value and p-value meet the set criteria. This shows that financial risk has significant influence on online shopping intention. Hence, hypothesis 4 which imply that financial risk has significant influence on online shopping intention is accepted. This finding aligns with previous findings of Samadi and Yaghoob-Nejadi (2009), Almousa (2011) and that of Chang et al., (2012). Hypothesis 5: Quality Risk has significant influence on online shopping intention. Quality risk is supported by the regression analysis as the t-value and p-value meets its set criteria, where t-value of the coefficient of beta of quality risk which is 6.080 is greater than +1.96 and p-value of 0.000 is less than 0.05, this authenticate the fact that quality risk has significant influence on online shopping intention. Therefore, hypothesis 5 that states that quality risk has significant influence on online shopping intention is accepted. This outcome correspond with preceding research by Zhang, et al. (2012) who revealed that quality risk has negative influence on online shopping intention because when products purchased online do
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not perform to satisfy customers expectation and customers give unsatisfied review, their reviews tend to discourage other intending shoppers but if there were positive reviews, intending shoppers will be encourage to shop online.
Figure 4.5: Hypotheses 5. Conclusion The emergence of internet has impacted the world’s marketing environment in a great way and it has provided businesses the opportunity to expand and enhance their ability to reach customers in different locations, both locally and globally through electronic commerce (Masoud, 2013). Similarly, the internet had brought about great opportunities to small-to-medium enterprises, enabling them to make use of ecommerce and operate Business-to-Consumer (B2C) ecommerce which involves selling productsor services to the finally consumers via internet, thereby, giving them the presence that can be compared to that of market leaders (Moore, 2008) Thus, online shopping had become popular as it is the fifth most popular activity on the internet after email, social networking, accessing online news and web browsing (Neilsen, 2014). However, despite this popularity of online shopping and the myriads of benefits that comes with it, many customers still avert online shopping because of the risk associated with it, as there had been issues of privacy and security breach, credit-card fraud, non-delivery, lack of guarantee of quality goods and services among customers’ (Rao, 2012; Meskaran et al, 2013). This risk associated with online shopping, brought about the essence of this study which had endeavor to research the reasons behind the aversion of online shopping among shoppers in Malaysia apparel industry. Hence, the variables used for this research were deduced and conceptualized from prior researches on the influence of perceived risk on online shopping intention; these variables include information privacy risk, security risk, delivery risk, financial risk, and quality risk (Almousa, 2011; Masoud, 2013; Dai, Forsythe and Kwon, 2014). The Hence, the variables used for this research were deduced and conceptualized from prior researches on the influence of perceived risk on online shopping intention; these variables include information privacy risk, security risk, delivery risk, financial risk, and quality risk (Almousa, 2011; Masoud, 2013; Dai, Forsythe and Kwon, 2014). Furthermore, this research conducted a primary research which solicited responses from 307 customers’ who still prefer to shop for apparel in bricks and mortar than shopping online, using convenience sampling which is a type of non-probability sampling that enables easy selection of the target audience, as participants can be selected based on their availability and willingness to respond (Saunders, Lewis and Thornhill, 2009). The questionnaire used made it possible for the data to be analyzed quantitatively because the questionnaire made use of closed questions which compels participants to choose from the multiple options outlined rather than an open question that enables participants to respond with their own words (Saunders, Lewis and
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Thornhill, 2009). Hence, this enabled this study to fulfill its aim by examining the influence of perceived risk on shopping intention in Malaysia, specifically in Malaysia’s apparel industry. Implication for Practice According to the findings of this research, the three customers’ perceived risk that influence online shopping intention in Malaysia’s apparel industry will continue to hinder online shopping intention, because in terms of information privacy risk, customers’ still lack trust in virtual environment and some privacy conscious shoppers might continue to provide incomplete personal information (Zviran, 2008). Similarly, the three main information privacy risks that hinder customers’ from shopping online which are collection of personal information, improper access to digitally stored personal information and unauthorized secondary use of personal information would still make customers’ reluctant to embrace online shopping because as compared to brick and mortar, information privacy is difficult to protect in online environment (Smith, 2010). In addition, customers’ will continue to avert online shopping because, they are still concerned about the financial loss that they may incur if purchased product did not perform as expected or if the product delivered refused to work or failed to meet their need. Likewise, the no refund policy of most online stores and rigid payment methods will make it more risky for Malaysia customers’ to shop for apparel online (Forsythe and Shi, 2013). Finally, quality risk will continue to deter Malaysia customers’ from shopping for apparel online because the inability of online shoppers to see, touch or feel the desired product limit the information they have about the product, as a result most customers’ will continue to doubt the quality of the product. Also, the facts that most pictures and product information provided on e-shops are insufficient to convince customers’ about the product quality, customers’ might continue to avert shopping online for apparel (Lee, 2009). Recommendation In order to curb the three major customers’ perceived risk that inhibit online shopping intention in Malaysia’s apparel industry and increase the market share of online stores, retailers have to improve the policy guiding the collection of personal information, prevent improper access to digitally stored personal information, prohibit unauthorized secondary use of personal information and severely punish anyone that misuse customers’ personal information. This new policy should be made known to customers’ and any prohibitory act should be made known to the public, implementing this will increase trust in virtual environment and in-turn reduce customers’ perceived risk of information privacy risk. In regards to quality risk and financial risk, retailers should give guarantee that their product texture/color is same as displayed in the e-shop, they could also pledge 100% refund within a specified period if customers’ are not pleased with the product quality. Hence, the refund policy should be boldly written in their terms and condition and they could also use it as slogan which would be displayed at a noticeable part of the website, in order to convince more customers.
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