Factors Affecting the Online Purchasing Behavior for Young Consumers: A Case Study

Abstract

The Internet is widely acknowledged as a platform for international communication and is increasingly used as the most crucial tool for all businesses engaged in international trade. According to estimates, 52.58 million people in Bangladesh are using the internet in the year 2022. In spite of the fact that online shopping is not nearly as common in Bangladesh as it is in other countries, businesses are investing in business-to-consumer (B2C) online shopping. As a result, it is essential to have an understanding of the factors that influence the online purchasing behavior of Bangladeshi consumers. The goal of this study is to create a theoretical research model that will serve as a framework for determining the major variables affecting Bangladeshi consumers’ choice to shop online or not. Specifically, this research focuses on the shopping habits of Bangladeshi consumers. In order to collect data, 331 people in Dhaka, Bangladesh, filled out a questionnaire that they had to administer to themselves. Validity and reliability of a measurement model were evaluated with the help of Structural Equation Modelling (SEM) analyses. The measurement model that best fits the data was approved for use based on the suitability index (fit indices). It has been shown to be a reliable instrument, and it has also satisfied the requirements set forth by psychometrics. The analysis of real-generation Multiple Linear Regression (MLR) data analysis identifies important decision-making considerations such as service quality & responsiveness, price, consumer resource, perceived risk, trialability & generational gap, website factor, payment procedure, and demographic consideration. The adoption of online shopping by Bangladeshi consumers is impacted in various ways by each of these factors. In addition to that, some recommendations and suggestions for decision-making are included in this article for researchers in the future.

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Hasan, I. , Habib, M. and Tewari, V. (2022) Factors Affecting the Online Purchasing Behavior for Young Consumers: A Case Study. Journal of Service Science and Management, 15, 531-550. doi: 10.4236/jssm.2022.155031.

1. Introduction

Online shopping has started to gain popularity in Bangladesh, which is not surprising given the country’s rapidly growing number of internet subscribers. As of January 2022, there were 52.58 million people using the internet in Bangladesh (Kemp, 2022). However, consumer behavior in developed countries and developing countries significantly differs when it comes to online buying. This is mostly attributable to the fact that developing nations have greater access to the web and other tools than developed nations. Numerous earlier researchers have also argued that consumers’ positive attitudes toward adopting new technology play an important role in their motivation to switch to the new shopping platforms, specifically online shopping, and that such attitudes influence consumers’ decisions regarding whether or not they will adopt it. Online shopping is an example of one of these new shopping environments (Sultana et al., 2021). Personality is another factor that plays a significant role in determining whether or not consumers will embrace new technologies, such as online shopping, because different customers have different preferences when it comes to making purchases (Yin, 2022; Wu & Ke, 2020). Seventy percent of users spend on social networking sites more than an hour, according to a recent survey that was carried out in Bangladesh by The Daily Star (Islam, 2020). This indicates that people are more engaged with the internet these days, which increases the likelihood of the expansion of the e-commerce market.

The fundamental purpose of this study is to provide a greater understanding of the decision-making processes that influence the adoption of online shopping in Dhaka, which is a city with an e-commerce business that is rapidly rising in size. It will also help in identifying the areas in which shoppers who do their shopping online should concentrate the majority of their efforts in order to develop. The broad empirical research will be beneficial to future academics who study consumer behavior in the e-commerce industry since it will provide information on decision factors and the relative importance of those criteria.

In today’s society, conducting business over the Internet is becoming an increasingly important activity. The purchasing and selling of products and services over the internet are what we mean when we talk about internet commerce. Online shopping, often known as e-commerce, Internet shopping, electronic buying, or web-based shopping, is a relatively recent innovation in the retail industry. Customers are now able to connect and carry out business transactions online in a way that is not only more convenient, but also less complicated, more inexpensive, and more easily available. Internet shopping provides a lot of advantages that traditional shopping does not have, and these advantages are causing customers to change the way they shop. Many people believe that the Internet poses the greatest danger to conventional shopping centers and retailers (Clemesn, Gan, & Zhang, 2014). Despite the fact that online shopping is gaining popularity in Bangladesh due to the fact that it saves shoppers time, is simple to use, and is handy, it is still not as popular as it ought to be due to the fact that there are obstacles that deter customers from shopping online.

E-Commerce in Bangladesh

E-commerce use is continuing to skyrocket as a direct result of recent developments in Internet connectivity, such as the launch of 4 G on February 19, 2018, as well as the dynamic marketing and sales of mobile devices, particularly smartphones (Kakon, 2022). Businesses have realized that Bangladeshis are ardent fans of technology and voracious customers, particularly during celebrations. This is especially true during Eid. In spite of the many challenges that come with city living in Dhaka, more and more people are turning to online shopping, which has led to an increase in the number of businesses that operate solely online. E-commerce has seen a rapid increase in the number of online shopping websites, particularly in the B2C and C2C categories. This increase has been driven by the demand from buyers & sellers, a wide variety of items, devoted service, as well as convenience, affordable prices, overall improvement in these areas and improving payment security & flexibility. There are many different types of e-commerce in Bangladesh, including:

· Businesses that sell their wares and services to other companies are known as “business to business,” or B2B, for short. These types of business-to-business websites, such as Address bazaar, Bangladesh Business Guide, and Biz Bangladesh, contain business directories, information on trade deals, and details about suppliers.

· Business to Consumer is an industry term for companies who sell their products or services directly to end users. B2C websites have also become highly popular, as evidenced by the expansion and financial success of online-based meal delivery services such as Food Panda and Pthao Food.

· C2C is an abbreviation that stands for “consumer to consumer,” which describes the practice of customers selling goods and services (new or used) to other customers who are also consumers. In this particular industry, the most successful enterprises are Ekhanei and the Facobook marketplace.

2. Problem Statement

Internet use and accessibility have increased dramatically over the past decade, which has contributed to the rapid growth of the online space. It is common knowledge that 52.58 million people make use of the internet for these reasons; to this day, a large number of business owners, both small and large, as well as multinational corporations, have found the internet to be the ideal business platform for expanding their operations on a global scale. In addition, consumers are drawn to the convenience of shopping online because it saves them time. At this time, the most lucrative market is the online one. On the other hand, the circumstance is quite different in Bangladesh. People still have a stronger preference for traditional stores over internet marketplaces (Ahmed et al., 2022). On the flip hand, individuals who were earlier engaged in buying and selling online do not show the same level of enthusiasm as they formerly did.

The objectives of this research are to determine the elements that affect Bangladeshi customers’ acceptance of internet purchasing and identify the elements that may influence Bangladeshi customers’ preference for online buying over traditional shopping.

Global online purchases have become the most common technique of shopping. In recent years, numerous businesses have attempted to start Online Shopping in Bangladesh. Despite the presence of numerous online shopping websites in Bangladesh, shopping online is not as popular as in other nations. Several things contribute to this condition. Therefore, our area of investigation is:

Why has online shopping not been adopted by the majority of Bangladeshis?

Components:

Components can be put into two groups: Independent and Dependent. The only dependent variable is Consumer Online Shopping Behavior of this research, while the other variables are independent. The independent variables include Service quality & Responsiveness, Price, Consumer resource, Perceive risk, Website factor, Payment Process and Trialability & Generation gap.

3. Literature Review and Research Hypotheses

3.1. Price

Internet customers gave great importance to price when making their purchases (Ahmed, 2021). Price has consistently been the most important consideration for purchaser selection. Customers are influenced to shop at online stores because of the availability of competitive pricing (Jadhav & Khanna, 2016). It’s possible to say that price and feedback from other customers are two of the most important components of shopping online. Customers have the expectation that the prices of goods and services that can be found on the internet will be significantly lower than those that can be found in traditional stores. According to empirical evidence, customers assume that costs are the same across all online retailers, but they do not check prices between online and traditional retail outlets. One further significant advantage of shopping online is the availability of comparable prices offered by many online retailers, as this point has been stated. The fact that price is such an important consideration for customers when making purchases online suggests that consumers believe the overhead expenses of online retailers are lower than those of store-based enterprises (Jadhav & Khanna, 2016).

Ha: The association between online merchant prices and consumer online shopping behavior is negative.

3.2. Service Quality & Responsiveness

Customers who buy online have expressed their desire to be able to return an item without having to answer any questions if they are unhappy with their purchase. E-retailers ought to institute refund policies in order to convince online buyers that they can easily return products for which they are dissatisfied and receive refunds or exchange things at no cost within a reasonable amount of time (Ahmed, 2021). Responses to frequently asked questions, as well as procedures for returns, credit, and payments make up customer service (Jadhav & Khanna, 2016) need to provide the best possible delivered value in order for customers to view this as a benefit and continue to show loyalty to the brand. Analyzing consumer satisfaction, which is impacted by factors such as cost, value, and demand from customers, is one way to ascertain this value (Uzun & Poturak, 2017).

The following qualities are those that are included in the category of component three, also known as factor three, and they belong to the product that is being advertised online on the online sites (Hossain et al., 2021):

· The wide range of products that are available,

· The quality of the goods that are being made available online.

The most straightforward strategy for selling things online is to offer customers a “Money Back Guarantee.” It is the mechanism through which consumers are given the assurance that, in the event that they are unhappy with a product, they can return it at any time and without being questioned about their reasoning (Datta & Acharjee, 2018; Khan, 2016).

Hb: The association between Service quality & responsiveness and online purchasing behavior is positive.

3.3. Consumer Resource

Web businesses have reacted to the demand for customer control by incorporating a variety of site features, such as internal search engines and recommender systems, that obtain additional information about it, enable customers to quickly identify what they need, and purchase it. This has been done in response to the demand for customer control. There have been very few attempts to develop a framework or typology of Web-based customer decision-support systems, but this has little effect on the behavior in which people make purchases online because a person’s computer is automatically aware of the existence of the Internet. One of the most important factors that determines whether an individual will experience flow is their level of competence (Koufaris, 2002).

The current technology for integrating machine interactivity in online shopping settings has the ability to offer customers never-before-seen opportunities to locate and evaluate a variety of product options. These skills are especially important when considering the fact that online businesses cannot provide customers with the opportunity to make direct physical contact with their products, do not permit customers to engage in face to face physical interaction with salespeople, and may offer a very large number of alternatives as a result of their lack of physical limits for product presentation (Ferdous et al., 2022). Interactive choice aids come in the form of a large variety of software tools, ranging from search engines with a broad range of applications to complex agent-mediated electronic commerce. These interactive decision aids may not always be helpful for customers who make purchases online. Customers do not require cumbersome equipment and strategies to exert influence when engaging in online shopping (Häubl & Trifts, 2000).

Hc: The association between consumer resources and online shopping behaviour is positive.

3.4. Perceived Risk

According to Rudolph et al. (2008), one of the most significant deciding factors regarding online shopping is the consumer’s perception of the level of risk perceived. They need to be aware of a risk of risks, including those related to quality, security, and so on. In addition, from the perspective of the buyer, the risk associated with purchases made through e-commerce is higher than the risk associated with purchases made at traditional stores (Suki & Suki, 2007). According to Comegys et al. (2006), administrators are able to improve business prospects and plans by gaining a risk of consumer risk and how consumers react to risks linked with products and services. According to research conducted by Miyazaki & Fernandez (2001), customers who are either knowledgeable about online shopping or perceive fewer risks when doing so make more purchases than customers who perceive a greater number of risks. In consumer buying behavior, the anticipation of negative repercussions may be the outcome of perceived risk, as stated by Jain and Kulhar (2019). When discussing the practice of shopping online, there are a lot of different variables to consider. According to Khan et al. (2015), there are five different sub dimensions that make up perceived risk. The performance, economic, personal, social, and private factors are all included in this category. According to Karim (2013), the hazards perceived with online shopping in Bangladesh include the failure of delivery systems, the use of online payment systems, the invasion of personal privacy, and the lack of individualized customer service. In addition to this, he discovered that the cost of delivery was a risk that Bangladeshi buyers perceived while shopping online. According to the findings of these studies, there are a number of distinct dangers connected to online shopping, each of which poses a threat to the consumer’s intention to engage in online shopping. Our survey and other empirical study both came to the conclusion that risk associations had a large and negative impact on attitudes toward online shopping. According to Doolin et al. (2005), customers are more likely to shop at an online business if it offers a high level of protection for their personal information and privacy. As a direct consequence of this, the following theory is proposed:

Hd: The association between perceived risk and online shopping acceptance is negative.

3.5. Trialability & Generation Gap

Compatibility, complexity, and trialability are the three components that make up the requirements for customer learning, as outlined by Gatignon and Robertson (1989). This idea gains further layers of complexity when considered in the context of our modern environment. Who belongs to generation x and who does to generation y, or what is the generational gap? The amount to which a concept may be validated by testing on a smaller scale is referred to as its trialability. There is a positive association between adaptation and trailing, which occurs when a consumer has the opportunity to do so. According to the findings of our empirical research including 331 persons, respondents from the X generation are more concerned about trialability than respondents from the Y generation (Ahmed et al., 2022). As a result, around ninety percent or more of the survey respondents who are part of generation Y engage in online shopping, but only one percent of the survey respondents who are part of generation X do so.

He: The association between trialability & generation gap and online shopping acceptance is negative.

3.6. Website Factors

Clients can benefit from having access to websites because they are, at their core, informational repositories that can aid them in their informational searches. Websites that are considered to be business-to-consumer are those that enable customers to make purchases online. The aesthetic qualities of a website can also have an effect on the decisions that customers make when shopping online (Shergill & Chen, 2015).

The four features that make up B2C websites are the design, the information content, the privacy settings, and the security settings. In a similar vein, the following five aspects of a website have an impact on a consumer’s perception of a retail website: product information, usability, entertainment, currency, and trust. When developing websites of a high quality, online retailers devote great consideration to both the content and the layout of their sites. The information, functions, or services that are offered by a website are referred to as the “content” of the website. On the other hand, “design” refers to the manner in which the material is displayed to users (Alsharief, 2018). A fantastic website should be able to hold the attention of visitors and give them the impression that the company can be trusted, relied on, and depended on. Because of this, the biggest reason customers do not make purchases online is because the websites they visit are poorly designed.

Hf: The association between facets of a well-designed web site and online shopping behaviour is positive.

3.7. Payment Process

When a consumer or buyer makes payment transactions for products or services purchased via the Internet this is considered to be an example of online payment. When a company processes more payments digitally, whether online or offline, the amount of money that it spends on supplies like paper and postage drops accordingly. In addition to this, it helps to boost customer retention because it increases the likelihood that the consumer would return to the same e-commerce website where his or her information has already been captured and is being retained. Because consumers are worried about the security of their credit cards and other personal information, protecting the confidentiality of financial transactions and customer information is the most important issue (Rasheduzzaman et al., 2021).

When it comes to methods of payment, online shopping gives consumers access to a wide range of options, some of which include cash on delivery, internet banking, debit cards, credit cards, and credit card debits and credits. On the other hand, the vast majority of purchasers favored cash on delivery (Rudolph et al., 2008). The vast majority of purchases made online are paid for with credit cards by use of the website paypal.com. Users with a PayPal account are able to make online purchases using a credit card, as indicated by information obtained from customer service for online shopping websites. There are various payment alternatives available, such as paying in advance with cash or using your bank account. Negotiations with customer service representatives might take place over the phone in order to set up the arrangement.

Hg: The association between improper payment process and online shopping acceptance is negative.

3.8. Mathematical Model

O n l i n e S h o p p i n g B e h a v i o r = α + β 1 Price + β 2 Q u a l i t y & Re s p o n s i v e n e s s + β 3 C o n s u m e r Resource + β 4 P e r c e i v e R i s k + β 5 T r i a b i l i t y & G e n e r a t i o n G a p + β 6 W e b s i t e F a c t o r + β 7 P a y m e n t Process

O n l i n e S h o p p i n g B e h a v i o r = 0.839 + 0.337 Price + 0.359 S e r v i c e Q u a l i t y a n d Responsiveness + ( 0.100 ) C o n s u m e r Resource + 0.014 P e r c e i v e R i s k + 0.111 T r i a b i l i t y a n d G e n e r a t i o n G a p + 0.136 W e b s i t e F a c t o r + ( 0.146 ) P a y m e n t Process

3.9. Graphical Model

The graphical representation of the model used in this investigation can be found in Figure 1. The dependent variable in this investigation is Online Shopping Behavior, and there are seven independent variables.

4. Methodology

4.1. Questioner Development

Because there had been no previous research on the topic of online shopping in Bangladesh that had been published, it was required to collect primary data in

Figure 1. The graphical model of online shopping behavior.

order to test hypotheses and accomplish the research objectives of the study. An in-depth analysis of the in-depth literature and the findings of an in-depth interview were the two primary sources of information that were used to structure the questionnaire. A classification of factors that may impact customers’ decisions regarding online shopping was made possible with the assistance of an analysis of the relevant literature and in-depth interviews carried out in Dhaka. The data collected from the in-depth interview served as the basis for the formulation of survey questions that were optimized with respect to their relevance, applicability, and timeliness.

The questionnaire consisted of a number of different measures, such as nominal scales, interval scales, and Likert scales. The respondent’s demographics were discussed in the first part of the servey, which was based on a study piece on their internet buying habits (Moshrefjavadi et al., 2012). The second part of the discussion involved the evaluation of assertions using a Likert scale with five points, ranging from strongly agree (1) to strongly disagree (5) (CR Kothari, 2019; Kumar, 2014). The reliability of the reaction is improved as a result. The questionnaire was evaluated by a seasoned marketing specialist. A pilot study of the questionnaire was also conducted on a random sample of ten Bangladeshi customers who were at least 21 years old and lived outside of a university in the city of Dhaka. Ten customers were polled to determine the reliability and validity of the questionnaire, as well as to explain the questions and comments that were included in it. The questionnaire’s phrasing had some minor tweaks in order to accommodate the new procedure.

4.2. Sample and Data Collection

Because of the ease with which it can be used to communicate with respondents and carry out the poll, we have decided to use “Google Form.” 267 or more measurements/surveys are needed to have a confidence level of 95% that the real value is within ±6% of the measured/surveyed value (Salant & Dillman, 1994). We were successful in locating a representative sample of 331 respondents. In order to make both our analysis and the analysis of the respondent as simple as possible, we used a Likert scale with five points.

4.3. Sample Demography

According to the results of our survey shown in Table 1, male respondents made up 48% of the total, while female respondents made up 52%.

Table 2 age demography of this research, where 74.6 percent of the 331 respondents were between the ages of 18 and 24, 1.8 percent were between the ages of 35 and 44, 23.3 percent were between the ages of 25 and 34, and 0.3 percent were between the ages of 45 and 54.

In terms of the respondents’ level of education, 2.7 percent were in class XII, 78.5 percent were post graduate students, 1.2 percent were in class X, and 17.5 percent had graduated from high school shown in Table 3.

In Table 4, we can also see that there were 331 people who responded. 71 percent of respondents have an annual income that is less than 5 lakhs. 9.4 percent of respondents have an annual income between 5 and 10 lakhs, 2.7 percent of respondents have an annual income between 10.1 and 15 lakhs, 1.8 percent of

Table 1. Gender percentage.

Table 2. Age percentage.

Table 3. Percentage respondent’s education background.

Table 4. Percentage respondent’s education background.

respondents have an annual income between 15.1 lakhs and 20 lakhs, 11.5 percent of respondents have an annual income between 20.1 lakhs and 25 lakhs, 2.1 percent of respondents have an annual income between 25.1 lakhs and 30 lakhs, and 1.5 percent of respondents have an annual income that is greater than 30 lakhs.

To illustrate the relationship between the independent variables and the dependent variable, the Multifactor Linear Regression (MLR) Model (Al Fahham & Alajeeli, 2020) has been used in the section 3.8 of the paper, and the Graphical Model has been used in the section 3.9 of the paper.

5. Analysis and Results

R Square is a calculation that was done so that we could determine the overall validity of the model. In order to calculate the results of the Linear Multiple Regression Analysis, we also calculated the coefficients of the variables and took into account a level of significance of 5%. The IBM SPSS Statistic 25 program was used to do each and every one of these calculations.

We have already highlighted the fact that, in IBM SPSS, we calculated R Square in order to determine the overall strength of our research model, and the result is displayed in Table 5.

If the value of R squared is between 0.5 and 0.7, then this value is often regarded as having a Moderate impact size (Moore & Notz, 2021). Keeping an eye on the table, we are able to determine that our R Square value is 0.502. This translates to the fact that the strength of our research model is moderate, which is, in actuality, fairly good.

The Linear Multiple Regression Analysis was the primary instrument that we used to investigate the hypothesis. In order to conduct this analysis, we have considered a level of significance equal to five percent. In order to analyze the results, the coefficient is provided in Table 6.

5.1. Criteria for Fit Index

According to Kamis et al. (2018), the index of the correspondence is made up of three different types of fits: the parsimonious fit (CMIN/df), the incremental fit (TLI, CFI, and GFI), and the absolute fit (RMSEA). It is sufficient to have at least three or four congruity indices for both absolute fit and incremental fit (Hair et al., 2010). CMIN/DF (Degrees of freedom) < 5.0 (Bentler, 1990), CFI, IFI > 0.90 (Schumacker & Lomax, 2010), and RMSEA (the root mean square error of approximation) < 0.10 (Fabrigar et al., 1999) are the recommended threshold values for the four-factor measurement. This entire process of calculation was carried out by the IBM SPSS Amos 25 program. Figure 2 shows the output of the Structural Equation Modelling (SEM).

After finishing all of the testing for this reflective measurement model, it is abundantly clear in Table 7 that all of the validity and reliability criteria were

Table 5. Result of R square.

a. Predictors: (Constant), Mean_PP, Mean_T_GR, Mean_CR, Mean_Price, Mean_WF, Mean_PR, Mean_SQ_R

Table 6. Result of coefficients.

a. Dependent Variable: Mean_OSB

Figure 2. Measurement model of online shopping behavior in Bangladesh.

Table 7. Cut-off value test.

satisfied. Validity and reliability testing of the reflecting measurement model will benefit from this since it provides further proof and support.

5.2. Result Summary

To test hypotheses, coefficients are utilized. To facilitate comprehension, we can follow Table 8.

The model was compatible with Ha, Hb, Hc, Hd, Hf, and Hg, however did not supported He. Payment Process (Hg), Website Factor (Hf), and Service Quality & Responsiveness (Hb) have a significant positive influence on consumers’

Table 8. Result of model support.

online shopping behavior or adoption of online shopping, as shown in Table 8, Model Support Results. The result also indicates that high prices (Ha), high consumption of consumer resources (Hc), and perceived risk (Hd) have a negative impact on the online shopping behavior of consumers. The Trialability & Generation gap (He) significant level is greater than 5 percent, so (He) is rejected based on Table 8 Model support results.

To formally interpret, we can state that Ha is supported (beta coefficient = 0.337, p-value = 0.006), indicating that there is a significant negative correlation between online retailer prices and consumer online shopping behavior. Hb is also supported (beta coefficient = 0.359, p-value = 0.000), so there is a statistically significant positive correlation between Service quality & Responsiveness and online consumer purchasing decisions. Hc is supported (beta coefficient = −0.100, p-value = 0.000), but the beta coefficient is negative, indicating that there is a negative relationship between consumer resource and online shopping behavior. Hd is also supported (beta coefficient = 0.014, p-value = 0.015), indicating that there is a negative correlation between perceived risk and online shopping adoption. This is the only hypothesis He that is not supported (beta coefficient = 0.111, p-value = 0.806); since this hypothesis is not supported, trialability and generation gap He do not have a significant impact on online shopping behavior. Hf is supported (beta coefficient = 0.136, p-value = 0.006), indicating that a significant positive relationship exists between well-designed website factors and online shopping adoption. Lastly, Hg is also supported (beta coefficient = −0.149, p-value = 0.020), but the beta coefficient is negative, indicating that there is a positive relationship between poor payment process and online shopping adoption.

6. Limitations and Further Research

During the course of our research, we came across a few problems connected to the research that need to be addressed. The following is a list of those:

· When a Market Statistical survey is carried out, all that is measured is what has occurred in the past; it does not, in most cases, predict what will occur in the future. This indicates that a company is unable to take a look at the measurements that have been done on them and know for certain what will take place. The most that can be asked of them is to consider how, if at all, they have developed between the time period in question and the present day.

· The customer is the central focus of the research that is presented. In any event, his intentions regarding purchases are difficult to determine in a definitive and accurate manner. This results in some degree of unpredictability in the conclusions that can be drawn from the exploration action. It is possible that the findings of the investigation work, particularly the results of the shopper research, will not turn out to be accurate.

· The most significant disadvantage of surveying is that the information’s quality is extremely variable. This is the most significant limitation of surveying. The degree to which the investigation will actually be of use to them is directly proportional to the degree to which the information is accurate.

· At the time that this article was being written, the incidence of the novel coronavirus was continuing to rise, and there was no sign that the infection rate associated with this virus would decrease. Our regular lives have been put on hold for a considerable amount of time due to the rapid spread of the virus and the necessity for people to avoid direct contact with one another in order to remain healthy. Which is having a greater impact than it previously had on our behaviour patterns. The covid-19 effect should be included in the researcher’s study of online shopping behaviours.

· Researchers can also concentrate on the question of why major international online marketplaces such as Amazon and eBay are not yet accessible in Bangladesh. This could potentially open up a wide variety of research avenues.

· In addition, we did not conduct an analysis of the demographic factors, such as age, gender, and level of income—that may influence consumer behaviour regarding whether or not they engage in online shopping. These factors may include gender, age, and income level. Therefore, additional research should investigate this topic from the viewpoint of Dhaka, Bangladesh.

In spite of the fact that it has these limitations, the study has provided important and fascinating insights into the online shopping experiences of customers.

7. Innovation and Contribution of the Research

E-commerce is constantly evolving, and Bangladesh is at the forefront of this change. Every person in the country’s urban regions owns a smartphone, which provides a fantastic opportunity for e-commerce companies to launch their operations. This article will assist e-commerce companies in better understanding the psychology of their customers and developing a business model that serves their needs.

This article will also help future academic scholars who want to study how people purchase online in Bangladesh and other South Asian nations. By providing an understanding of the psychological factors that influence customer behavior, this research will contribute to the advancement of knowledge in this field. Additionally, the recommendations for e-commerce businesses will provide valuable insights for entrepreneurs who are looking to enter this rapidly growing market.

8. Discussion and Conclusion

An innovative marketing tool that is increasingly being used globally as a means of communication is the Internet. However, online shopping is not common in Bangladesh, and businesses investing in B2C online shopping must comprehend the factors that affect Bangladeshi consumers’ online purchasing behavior. In order to gather information, a self-administered questionnaire received responses from 331 respondents. The experiential analysis identifies the following important decision factors for online shopping behavior in Bangladesh: Service quality and responsiveness, Price, Consumer resources, Perceive risk, Website factor, Trialability & Generation gap, Payment Process, and Demographic factor. Also included are suggestions and managerial implications.

The primary objective of this research was to investigate the factors that may influence consumer adoption of web transactions. On the basis of the results of the data analysis, it may be possible to conclude that the model’s components collectively possess some predictive ability. After analyzing the model’s results, we were able to conclude that the model’s overall strength is approximately moderate. Among these, consumers’ beliefs regarding high online product prices, service quality & responsiveness, high use of consumer resources, the design of a website, a high perception of risk, as well as a poor payment process have a significant impact on the adoption of online transactions. It is fascinating to observe how consumers’ perception of online products affects their decision to adopt online transactions. In contrast, factor such as trialability & generation gap did not have an impact on the adoption of web transactions.

The findings indicate that Bangladeshis are extremely price sensitive. They prefer to negotiate and purchase the product at the lowest possible price. Therefore, if they perceive the price to be excessive on an online store, they will refrain from making a purchase. Assuring refund policies when consumers are dissatisfied and allowing them to select from a variety of products, this is how service responsiveness and quality influenced consumers’ positive attitude toward online purchasing adoption. Moreover, when consumers find the information they seek quickly and easily on websites, they are pleased. As a result, websites contribute to the behavior of a positive attitude toward online shopping among consumers.

On the other hand, despite the fact that consumers have access to a vast selection of products online, they frequently do not feel compelled to make purchases because they cannot physically touch or inspect the products prior to purchase. Particularly in Bangladesh, consumers prefer to physically touch and check products because online retailers frequently display high-quality images but deliver low-quality products. People prefer brick-and-mortar stores even though they have access to the resources necessary to shop online. As a result, Consumer Resource has little impact on consumers’ adoption of online shopping behavior. Consumers also believe they are taking a bigger risk in terms of the security of their personal data and the quality of the products. Due to the high prevalence of fraud, people in Bangladesh do not feel safe when disclosing their payment information online. Furthermore, we’ve found that elderly generations are less receptive to innovation. And lastly, people are very committed to protecting the privacy of their personal information and do not trust online companies with their personal information when making online purchases. Therefore, additional research should be done to explore these findings.

In this study, we observed a number of factors influencing consumers’ online shopping behavior. Therefore, online business managers should enhance the quality of their products. People will only be able to trust a company if the images displayed on their websites accurately depict the products they ship. In addition, whenever customers pay via cards that contain personal information, the online merchant should safeguard the customers’ personal information. Customers should be able to utilize a safe and secure payment system. People will be more likely to buy products online without touching them if the quality of the products is enhanced, as trust will be established. In addition, 1% of our X-generation respondents engage in online shopping, according to our findings. Managers must therefore keep this in mind and plan their strategies accordingly in order to target the X generation in addition to the Y generation, thereby increasing their sales and fostering the growth of their businesses.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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