Online Spending: The Role of Demographic Variables in Emerging Economy with Special Reference to Bangladesh

Online Spending: The Role of Demographic Variables in Emerging Economy with Special Reference to Bangladesh

Dr. Mohammed Javed Hossain¹, Dr. Robaka Shamsher², Srabonti Das Chayti³

¹Professor, Department of Marketing, University of Chittagong

²Associate Professor, School of Business, Independent University Bangladesh

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8100097

Received: 28 September 2024; Accepted: 08 October 2024; Published: 06 November 2024

ABSTRACT

In recent years, online shopping has boomed a lot around the world towards convenient and pleasurable shopping experience and it has brought a dramatic change in the shopping behavioral pattern of Bangladeshi consumers as well. A gradually increasing number of Bangladeshi customers are leaning toward online shopping. Online spending by Bangladeshi customers has increased too. This paper has made an attempt to explore an association between online customers’ demographic profile and their level of online spending. Data used in this study were obtained using a structured questionnaire on 98 respondents selected randomly. After the associational analyses through Chi-square, the demographic variables including age, occupation, and monthly income exhibited an association with online spending. However, two variables, namely gender and education did not confirm any association with the volume of online spending. At the end of the paper, keeping in mind the limitations of the study some agenda for future research have been proposed.

Keywords: Online spending, Demographic variables, Bangladesh

INTRODUCTION

The advent of smart phones, digital, and internet technology has brought a significant change in the way people do shopping around the world. The gradual shift from physical to online shopping has been phenomenal over the last decade. In 2019, the online market size was estimated at USD 9.09 trillion and it reached USD 10.36 trillion in 2020 at a growth rate of 13.97 percent (www.grandviewresearch.com). The revenue forecast in 2027 stands at USD 27.15 trillion at a growth rate of 14.7 percent. Such a boom in online shopping can be attributed to the convenience, ease of use, wide product variety, discounts, and quick delivery of merchandise associated with online shopping. People from different age groups are on the rise in using online platform for their purchases. Online marketers are adding new features and services for online shoppers, with the intent of providing them the same support and comfort that they would otherwise have during an in-person shopping environment. Like many other countries of the world, Bangladesh has also observed a tremendous growth in online shopping; especially after the pandemic of Covid-19. The sector is rising and becoming more popular among the youths of Bangladesh. Online shopping, thus, has become an easy solution to busy lives.

The term ‘Online’ means ‘On the Internet’. Online shopping is a form of electronic commerce that allows consumers to directly buy goods or services from a seller over the Internet using a web browser or a mobile app. At present, customers can shop online using a range of devices like desktop computers, laptops, tablet computers, smart phones, and smart speakers. A typical online store enables the customer to browse its range of products and services, view photos or images of the products along with information on product specifications, features, and prices. The online customer must have access to the Internet and a valid method of payment in order to complete a transaction; such as a credit card, a debit card, or a service such as PayPal.

The e-commerce industry in Bangladesh is steadily growing and attaining competitiveness. Though in its early stage, the e-commerce industry in Bangladesh is demonstrating progress at a rapid pace. According to a Dublin-based trade research institution ResearchAndMarkets.com, the e-commerce market size in Bangladesh in 2021 was about Tk. 56870 crore and it is expected to rise to Tk. 1.5 lakh crores by 2026 (www.tbsnews.net). Mostly urban, about 80 percent of the online shoppers in Bangladesh live in metropolitan cities like Dhaka, Gazipur, and Chittagong. The other two major cities for online shopping are Narayanganj and Sylhet. However, Suhan (2015) discovered that Bangladeshi people are still more prone to traditional shopping. Like other countries around the globe, Bangladesh has also observed a rapid increase in online shopping during the COVID-19 pandemic. Research findings indicate that the online sales in Bangladesh has increased by 70 to 80 percent in recent years and have drawn escalated attention and engagement amongst the city-dwellers of the country (Hasan, 2021; Tabassum, Khan & Farhana, 2017; Nesha, Rashed, & Raihan, 2018; Datta & Acharjee, 2018; Saha, Zhuang, & Li, 2020; Alam, 2020).

The online shopping behavior largely varies across different groups of consumers, which is somehow related to the shoppers’ demographic profiles, such as age, gender, household income, educational status, and the like. Studies indicate that the changing demographic profile has a major influence on consumers’ purchasing power, shopping behavior, and shopping preferences. Many researchers have emphasized on the significant influence of consumer demographics on shoppers’ preference of online store visit (Phang, Kankanhalli, Ramakrishnan, & Raman, 2010), online shopping frequency (Pradhana & Sastiono, 2019), online purchase intentions (Kanchan & Kumar, 2015; Sethi & Sethi, 2018), and customer perception of online shopping (Singh & Rana, 2018). Researchers have paid explicit attention to the dramatic shift in consumer demographic characteristics affecting online buying behavior (Padmaja & Mohan, 2015; Nampoothiri (2021). Today’s consumers’ are more diverse with respect to age, gender, income, education, occupation, marital status etc. Understanding the demographic profiles of the online consumers is pivotal to the marketers to figure out which are the fastest-growing segments and which segments buy more than the average. Retailers need to pay close attention to consumers’ demographic trends to reevaluate and reprioritize their target audiences and redesign marketing strategies accordingly.

Having stated this, the present study accentuates on exploring the discriminating roles of demographic variables shaping diverse shopping experience among the Bangladeshi online customers. The results of the study are equally expected to grow awareness among the Bangladeshi online marketers in realizing the differential roles of demographic variables shaping the diverse customer segments and help them (online marketers) improve their ability to obtain sustainable competitive advantage and future growth potentials by capturing the most attractive and profitable segments of shoppers and strategizing their respective businesses accordingly. The following sections of the paper embody literature review, followed by the rationale of the study, research objectives and hypotheses, research methodology, findings, and relevant discussion. The last part represents the conclusion, limitations, and some future research agenda.

LITERATURE REVIEW

Studies on Online Shopping Behavior – The International Context

Online shopping is the activity or action of buying products or services over the Internet. It means going online, landing on a seller’s website, selecting something, and arranging for its delivery. The buyer either pays for the good or service online with a credit or debit card or cash on delivery. There have been numerous studies on online shopping behavior in many different parts of the world (Nilsson & Wall, 2017; Pandey & Parmar, 2019; Devi, Das, & Baruah, 2019; Jaiswal & Singh, 2020; Niroula & Gyanwali, 2020; Mustikasari & Astuti, 2021; Meriç & Yıldırım, 2021). In the following literatures different areas of online shopping are covered and empirical findings are discussed below:

In a study, Nilsson and Wall (2017) identified six factors of Swedish online customer experience for clothing retailing where ease of use, security, fulfillment, reliability, customer service and store offerings were important in determining satisfaction, that showed positive impact on repurchase intention. In another study by Pandey and Parmar (2019) in Kanpur, India revealed that perceived ease of use, perceived risk, perceived usefulness, effect of website design, economic factor, availability of products, and customer satisfaction affect consumer’s online shopping buying behavior. A similar study by Devi, Das and Baruah (2019) unearthed that cash on delivery, website feature, knowledge on internet, offers and discounts, price, convenience, and lifestyle are pertinent to online shopping among the Indian customers.

Jaiswal and Singh (2020) revealed that economic value; followed by customer service, customization, and post-purchase experience- have significant impact on customer satisfaction with online shopping. Niroula and Gyanwali (2020) established that online shopping has positive impact on customer’s satisfaction in Kathmandu valley of Nepal.

A recent study by Mustikasari and Astuti, (2021) validate that customer service, brand experience, delivery experience, and economic value are critical in creating positive shopping experience and have significant influence on repurchase intention among the online grocery shoppers in Indonesia. A similar study conducted in Istanbul, Turkey (Meriç and Yıldırım, 2021) reveals that online shopping experience has significant effects on building purchase intention and re-visit intention to the same site.

The role and impact of consumer demographics have prompted an emergent attention among scholars and researchers around the world to how consumers are shopping through online (Singh and Rana, 2018; Nampoothiri, 2021). The effect of demographic variables on online shopping experience has been executed in Indian retail market (Richa, 2012), Malaysian retail market (Mee et al, 2019), Indonesian retail market (Pradhana and Sastiono, 2019), and Nigerian retail market (Mbah, Odike, and Akpan, 2019). Some of these studies have been found to have contradictory results and conclusions on whether or not consumer demographics are related to online shopping behavior (Padmaja and Mohan, 2015; Gazi, 2016; Datta and Acharjee, 2018).

Studies on Online Shopping Behavior – The Bangladesh Context

Physically visiting retail stores was the only predominant way to shop in Bangladesh. However, after the breakout of Covid-19 pandemic, the shopping behavior of consumers has changed a lot in favor of online shopping. Moreover, improvement in standard of living, rising purchasing power, tendency toward physical distancing, wide assortment of merchandise, convenience in product selection, cashback or discount offers, easy payment methods, and timely delivery of merchandise- have witnessed the recent rise in online shopping in Bangladesh. The gradual shift from physical to online shopping by Bangladeshi consumers in recent years has drawn the attention of Bangladeshi researchers and academicians. The following section highlights some studies conducted in online shopping in the context of Bangladesh.

A study by Tabassum, Khan, and Farhana (2017) reveals that attitude, followed by price, is most significant in making decision regarding online shopping by urban Bangladeshi youths. Nesha, Rashed, and Raihan (2018) have established that price, user friendliness, and perceived web quality have positive association with building customer attitude toward online shopping. The study further confirms that customer attitude is positively related to online shopping intention. Datta and Acharjee (2018), in their study conducted in Dhaka, Bangladesh, observed that security, after sales service, time saving, return policy, website design, product quality, previous experience, and reputation of the online vendor- positively impact young consumer’s attitude toward online shopping. A similar study by Hossain, Rahman, and Hasan (2018) explores that security, delivery, product availability, and product variety have positive and significant influence on Bangladeshi consumers’ online shopping decision of fashion apparel. However, Islam et al (2020) argued that Bangladeshi consumers consider price as the most significant factor while making decision regarding online shopping. Another study by Ali, Ahmed, and Absar (2020) exhibit that the products most frequently purchased online by Bangladeshi customers are clothes, food & beverage, mobile accessories, electronic goods, and beauty products.

Some authors tried to explore the level of satisfaction among the online shoppers in Bangladesh. In a study Das (2017) showed that customers in Bangladesh are moderately satisfied with their online shopping practice.  However, Mahmud, Imtiaz, and Ahmed (2019) revealed that satisfaction with online shopping is greatly dependent on price, convenience, time saving, and variety of products. Alam (2021), in his research, validated that adequate product description (both visual and written), delivery of the right product as demonstrated, better price as compared to the off-line price have huge impact on determining the satisfaction/dissatisfaction level of online shoppers in Bangladesh. The study further concluded that female customers are more satisfied than the male customers with online shopping in Bangladesh. In another study Hossain, Jamil, and Rahman (2018) revealed that security, personal hobby, payment method, appropriate pricing, privacy, social media, and reference groups are the factors that have significant influence on developing consumers’ intention toward online purchase. The study further validated that online purchase intention, product quality, and brand familiarity are key to influencing online consumer satisfaction. Finally, the study put forward that significant relationship exists between online consumer satisfaction and the resulting loyalty.

Some authors conducted studies on online shopping behavior during COVID-19 pandemic in Bangladesh. For instance, Neger and Uddin (2020) revealed a number of factors including product, time saving, payment, administrative, and psychology – have positive roles in determining Bangladeshi consumers’ internet-enabled shopping behavior. Another study by Alam (2020) validated that health, price, product, and place – show positive association with online buying, while another aspect ‘trust’ seems to have insignificant relationship with online buying behavior in Bangladesh.

Some studies have also been conducted in Bangladesh online retail industry to identify the impact of demography on online shopping behavior. Datta, Hossain, and Rouf (2015) found that age exhibit statistically insignificant impact on online shopping behavior of customers; whereas, income and occupation reveal statistical significance toward such behavior. Gazi (2016) found statistically no significant discriminating powers of demographic variables (gender, age, income, and education of consumers) in determining online repurchase intention among the consumers of Chaldal.com in Dhaka city. Similar results were found in another study in Chittagong by Alam (2018), who employed regression analysis to reveal that gender has no significant impact on developing Bangladeshi consumers’ attitude toward online behavior. However, these results were voided by Datta and Acharjee (2018), who validated that six socio demographic factors; namely, family income, personal income, educational level, member of current residence, and daily use of internet – have statistically significant influence on Bangladeshi young consumers’ attitude towards online shopping in Dhaka city. Likewise, Akman and Rehan (2014) identified that age, income, and education have a significant impact on online buying by professionals.

RATIONALE OF THE STUDY

Bangladesh retail industry has recently witnessed a dramatic transformation by realizing the importance of digital technology and adopting various digital tools for customer attention, acquisition, and interaction. The demographic profile of new-generation customers of Bangladesh has probably been a major factor stimulating such a shift. Moreover, the previous research studies do not confirm, rather contradict over the exact role of demographic variables in determining online shopping spending. Therefore, the existing paper aims at exploring the discriminatory power of demographic variables, if there is any, that shape a typical online shopping spending pattern in Bangladesh.

RESEARCH OBJECTIVES AND HYPOTHESES

This paper attempts to empirically examine the extent to which the variation in demographic profile of online customers (gender, age, monthly income, education, and occupation) can be attributed to the online spending by Bangladeshi customers.

Gender

A number of studies confirm that gender has a relationship with online shopping behavior. Zhang and Prybutok (2003) showed that gender is an important moderating variable in online shopping. Similar results were found by Sethi and Sethi (2018), who discovered that gender has a significant effect on online purchase intention from an Indian customers’ perspective. They observed that males have a stronger online purchase intention compared to the females in India. Similar results were unearthed by Mee et al (2019) in a Malaysian context, in which male internet shoppers had shown a more favorable attitude towards online shopping and web advertising than the female online customers. However, the study reveals that female shoppers perceive online shopping to be more enjoyable than the male shoppers. Rafiq (2018), who conducted a study at a university, found that there exists a significant difference for online shopping attitude between males and females students, but he established that female students have more tendency towards online shopping compared to their male counterparts. But, Awan and Ho (2017) established that female consumers’ confidence regarding online payment methods is lower than the male consumers. In another study, Pradhana and Sastiono (2019) exhibited that online shopping frequency is more for women than for men, though the study does not establish that there is a significant difference in terms of online shopping expenditure between men and women in Indonesia.

On the other hand, some other studies conclude that gender has no effect on online shopping behavior or experience. Baldevbhai (2015) established that no significant difference exists between the shoppers’ gender and their online shopping behavior. Singh and Rana (2018) found no significant differences in consumer perception from a gender perspective. Another study by Alam (2018) in Chittagong, Bangladesh reveals that gender has no significant impact on consumer attitude towards online buying behavior. This study result is also in line with the one conducted by Nampoothiri (2021), who validated that gender shows insignificant relationship with online buying behavior in Kerala, India. Similarly, Mehrotra, Elias, Al–Alawi, and Al–Bassam (2020) established that insignificant differences exist regarding the perception of male and female online shoppers.

Since there exists contradictory result with respect to the role of gender shaping online customer spending, this study finds it interesting to verify the fact by testing the following hypothesis.

Hypothesis 1: H_0: Online spending does not differ with respect to male and female shoppers

Age

Baldevbhai (2015) revealed that significant differences exist in the behavior of online shoppers for various age groups. Padmaja & Mohan (2015) also revealed the same asserting that age positively affects online buying behavior among the customers of Bengaluru City, India. In a recent study in Kerala, India, Nampoothiri (2021) observed significant correlations between various age groups and online buying behavior. Conversely, Jusoh and Ling (2012) established that there exists no significant difference in attitude towards online shopping from the perspective of age conducted in Taman Tawas Permai, Ipoh of Malaysia. Reddy and Srinivas (2015) also found the same that age doesn’t impact online shopping in India. This result is equally consistent with the one by Datta, Hossain, and Rouf (2015), where age was proved statistically insignificant showing no impact on online shopping behavior of customers. Similarly, Singh & Rana (2018) validated that there exists no significant difference in consumers’ perception of online shopping pertaining to various age groups.

The present study has, thus, found it interesting to examine if age has any role in discriminating online shopping spending. As such, the following hypothesis has been developed for testing.

Hypothesis 2: H_0: Online spending does not differ with respect to higher and lower age group of customers

Education

Reddy and Srinivas (2015) found that education doesn’t impact online shopping in Indian context, which is also reflected in a study by Mbah, Odike, and Akpan (2019) in Nigeria. Yet, Singh and Rana (2018) validated that educational qualification show significant impact on online shopping selection by customers. A previous study by Baldevbhai (2015) and a recent study by Nampoothiri (2021) in Kerala, India affirm the same. Yahya and Sugiyanto (2020) found that the online shoppers are usually more educated compared to the less educated shoppers who usually shop by visiting stores physically. Mehrotra at el (2020) reveal that more educated individuals have better knowledge regarding online shopping.

The conflicting results of the previous research studies make the authors of the present paper examine the role of education in discriminating online shopping spending. As such, the following hypothesis has been developed.

Hypothesis 3: H_0: Online spending does not differ with respect to higher and lower educated group of customers

Occupation

Datta, Hossain, and Rouf (2015) originated that occupation show statistically insignificant impacts on online shopping behavior of customers. Similar results were revealed by Jusoh and Ling (2012) in a study where different occupational groups exert no influence on attitude toward online shopping in Taman Tawas Permai, Ipoh of Malaysia. However, these revelations were rejected by Padmaja and Mohan (2015) who established that occupation positively affects online buying behavior among the customers of Bengaluru City, India.

Such differences in the concluding remarks by the previous researchers regarding the association of occupation and online shopping have drawn attention in the present study. Thus, the following hypothesis has been proposed to investigate the significance of relationship between these two parameters.

Hypothesis 4: H_0: Online spending does not differ with respect to different occupational groups

Monthly Income

Jusoh and Ling (2012) established that significant differences exist in attitude towards online shopping among the customers of different income groups in Taman Tawas Permai, Malaysia. Such discriminating power of varied income groups of customers affecting online shopping behavior was also found by Baldevbhai (2015). Yahya and Sugiyanto (2020) came up with similar findings that the higher income group shops more online than does the lower income group. Mehrotra at el (2020) indicated that income significantly impacts the online shopping of the university students. Likewise, Nampoothiri (2021) validated significant correlation between annual income of people and the online shopping behavior in Kerala, India. On the contrary, Reddy and Srinivas (2015) found that income doesn’t impact online shopping in the Indian context. This result is in line with the results of Singh and Rana (2018) who established that annual income of respondents display insignificant influence on customer perception of online shopping. Sethi and Sethi (2018) revealed the same picture that there exists no significant relationship between customers’ annual income and their perception of online shopping.

These bi-polar research findings draw the attention of the authors of this present study to investigate if there is any role of income of customers shaping their varied online shopping spending. Thus, the following hypothesis has been developed for testing purpose.

Hypothesis 5: H_0: Online spending does not differ with respect to higher and lower income group of customers

RESEARCH METHODOLOGY

The study is empirical in nature where both primary and secondary data were used. The population for the study was online customers of Bangladesh. Secondary data were collected from various published sources including relevant books, online and printed journals, websites, newspapers, and reports. A self-administered structured-questionnaire was used to collect primary data. All the data were collected between July and August of 2023. The study aimed at collecting data from 200 respondents from four randomly chosen wards of Chittagong metropolitan city, the commercial capital of Bangladesh. A total of 50 respondents were randomly selected from each of the chosen wards by making unscheduled visits to their residences. However, 79 of the randomly selected people were found to have never used online shopping. The remaining 121 people responded to the questionnaire, out of which 23 were found incomplete. Thus, the final size of the respondents used for analyses came to 98.

The questionnaire of the study had two sections. The first section of the questionnaire (Customer Profile) comprised of five demographic information of the respondents including their gender, age, occupation, education, and monthly income. These questions were either multiple-choice type or dichotomous type. The second section of the questionnaire comprised of only one question regarding the amount of money the respondents spent online per month. It is simply because the study was highly confined to examining if more or less spending was somehow associated with variation in demographic variables. This question was categorical in nature and the respondents were requested to select either of two options (‘I spend Tk. 10000 per month on an average online’/ ‘I spend more than Tk. 10000 per month on an average online’).

The collected information was analyzed by using some simple descriptive statistics including frequencies, percentages, mean, and standard deviation. Similarly, the association between usage rate and demographic variables was examined by employing chi-square test. All the calculations were carried out using SPSS, Version 25.0 (Leech, Barrett, and Morgan, 2005). The publication manual of APA (American Psychological Association, 2001) was used for citation of the sources of references that have been used in the study.

FINDINGS OF THE STUDY

Demographic Profile of the Respondents

The demographic profile of the sample respondents includes their gender, age, education, occupation, and monthly income. The following table shows the summary of the respondents’ demographic profile:

Table 1: Demographic Profile of Respondents

Source: Survey

Table 1 shows that out of 98 sample respondents, 52 percent (N= 51) are male and the rest 48 percent (N= 47) are female. As far as age is concerned, 51 percent (N= 50) of the respondents compose the age of 30 years and above; whereas 49 percent (N= 48) of the respondents fall below the age group of ‘up to 30 years’. Pertaining to educational qualification, the study has revealed that majority of the respondents (67.3 percent; N= 66) have higher educational qualification; whereas the remaining 32.7 percent (N= 32) have obtained the academic certification of ‘up to SSC/ HSC level’. The table also shows that the majority of the respondents (65.3 percent; N= 64) are service holders/ businessmen with the remaining 34.7 percent (N= 34) being students/ housewives. With respect to monthly income, it has been observed that the majority of the respondents (64.3 percent; N= 63) have a monthly income of Tk. 50000 or above; whereas the remaining 35.7 percent (N= 35) make a monthly income of ‘up to Tk. 50000’.

User Status of the Respondents

The study required to know the user status of online shoppers based on their monthly spending. In this regard, the respondents were given an option to choose from either of the monthly spending on online shopping below Tk. 10000 or above Tk. 10000. Table 2 reveals the results as found:

Table 2: User Status of Respondents by Monthly Spending

Source: Survey

From the table above, it can be seen that majority of the respondents (73.5 percent, N= 72) are heavy users, who spend more than Tk. 10000 per month for online shopping; whereas, only 26.5 percent (N= 26) of the respondents are found to have spent below Tk. 10000 for online shopping per month.

Test of hypotheses

All the hypotheses in the study needed to be examined if there is any association between the demographic variables (gender, age, education, occupation, and monthly income) of online shoppers and their status of usage (heavy user/ light user) based on their monthly online spending. Chi-square test was performed in this regard. The following tables (Table 3 and Table 4) depict the results derived from the test:

Table 3: Crosstab between Demographic Variables and Monthly Online Spending

Source: Survey

Table 4: Chi-Square Test Results;

Source: Survey

a. 0 cells have expected count less than 5.

b. Computed only for a 2×2 table.

Table 3 exhibits the differences in the status of online shoppers (light users or heavy users) based on an average monthly spending (below Tk. 10000 and above Tk. 10000) by the sample respondents corresponding to their different demographic variables. From the table, it has been observed that there is no major difference between the male and female shoppers with respect to online spending. For example, as the table shows majority of the male shoppers (76.47 percent; N= 39) are heavy users and the rest 23.53 percent (N= 12) are light users. Similar picture is prevalent among the females with 70.21 percent of them (N= 33) being heavy users and only 29.79 percent (N= 14) being the light users. The table also shows very a minimum difference between the ‘less educated group’ (71.87 percent, N= 23) and the ‘higher educated group’ (74.24 percent, N= 49) with respect to online spending.

However, with respect to age of the respondents, some differences are noticed. It is seen from the table that ‘within the age group of 30 years’ 60.41 percent (N= 29) of the respondents are ‘heavy users’; whereas, in the ‘above 30 years age group’ 86 percent (N= 43) are ‘heavy users’. So, there is a clear gap between the two age groups regarding online spending. As far as occupation of the respondents is concerned, some differences have also been observed. Service holders/ businessmen/ professional (81.25 percent, N= 52) tend to exhibit higher volume of online spending compared to students/ housewives (58.82 percent, N= 20). Finally, pertaining to monthly income of respondents some variations have equally been identified. The respondents with ‘more monthly income’ exhibit higher spending (88.88 percent, N= 56) compared with those with ‘less monthly income’ (45.71 percent, N= 16).

The differences, so far, observed in Table 3 needed validation and as such Chi-Square tests were run. The results of the Chi-Square tests are shown in Table 4. The first assumption (hypothesis 1) that ‘online spending does not differ with respect to male and female shoppers’ was tested and the researchers came with the following results (λ = .491, d.f. = 1, p < .483). The results invalidated such an assumption and concluded by failing to reject the null hypothesis referring to the fact that there is no significant difference between genders of the respondents and their online spending. This outcome is very much consistent with the previous studies conducted by Baldevbhai (2015); Singh and Rana (2018); Alam (2018); Mehrotra, Elias, Al–Alawi, and Al–Bassam (2020); and Nampoothiri (2021). Yet, the present outcome conflicts with that of Zhang and Prybutok (2003); Awan and Ho (2017); Sethi and Sethi (2018); Rafiq (2018); Mee et al (2019); and Pradhana and Sastiono (2019).

With regard to hypothesis 2 (online spending does not differ with respect to higher and lower age group of customers), table 4 exhibited the following results (λ = 8.223, d.f. = 1, p < .004) rejecting the null hypothesis by indicating that there is a significant association between different age groups and online spending. These results validate the previous studies by Baldevbhai (2015); Padmaja & Mohan (2015); and Nampoothiri (2021). However, this existing results differ from those of Jusoh and Ling (2012); Reddy and Srinivas (2015); Datta, Hossain, and Rouf (2015); and Singh and Rana (2018).

The Chi-Square test run for hypothesis 3 (online spending does not differ with respect to higher and lower educated group of customers) explored the following results (λ = .062, d.f. = 1, p < .803), which failed to reject the null hypothesis implying that online spending does not differ by the variation in education of customers. This result is similar to the ones previously observed by Reddy and Srinivas (2015) in an Indian context and Mbah, Odike, and Akpan (2019) in a Nigerian context. Nevertheless, the current result differs from the ones by a large number of researchers including Baldevbhai (2015); Singh and Rana (2018); Yahya and Sugiyanto (2020); Mehrotra at el (2020); and Nampoothiri (2021).

Table 4 also exhibits the results (λ = 5.729, d.f. = 1, p < .017) for hypothesis 4 (online spending does not differ with respect to different occupational groups) that led the authors to reject the null hypothesis by establishing the fact that the amount spent in online shopping significantly differs between occupational groups. This outcome seems to be consistent with the one by Padmaja and Mohan (2015) in an Indian perspective. Although the result contradicts with those by Jusoh and Ling (2012); and Datta, Hossain, and Rouf (2015).

Finally, the Chi-Square test run for hypothesis 5 (online spending does not differ with respect to higher and lower income group of customers) explored the following results (λ = 21.517, d.f. = 1, p < .000) and failed to reject the null hypothesis validating that online spending significantly differs between lower and higher income groups of customers. Such a result is consistent with those of Jusoh and Ling (2012); Baldevbhai (2015); Yahya and Sugiyanto (2020); Mehrotra at el (2020); and Nampoothiri (2021). However, some previous studies including those of Reddy and Srinivas (2015); Singh and Rana (2018); and Sethi and Sethi (2018) seem to be conflicting with the current one undertaken.

CONCLUSION, LIMITATION AND FUTURE RESEARCH AGENDA

Online shopping provides a virtual platform where both buyers and sellers can interact at their convenience regardless of the time and place. Rapid urbanization, technological advancement, availability of Internet services, favorable government policies, higher literacy rate, growing popularity of social media (like Facebook or Instragram), Internet-freak youngsters, reliable online payment platforms (like bKash, Rocket, Nogod), gradual involvement of banking sector, dual-couple career etcetera have brought huge changes among the Bangladeshi customers toward online shopping in recent years. The present study is a simple attempt to exploring the association of customer demographic variables with higher volume or lower volume of online spending. The results have discovered significant relationships between customers’ online shopping and their age; occupation, and monthly income; although customers’ gender and education did not show any significance validating the association with spending behavior.

These findings, in addition to its contribution to the marketing literature, have important implications for the online marketers in terms of adopting strategic marketing decisions by expanding the product portfolio and global foot prints. As the domestic FMCG companies are facing intense competition from the new as well as the existing players therefore they should aggressively focus on branding, sales promotion, product development, and innovation techniques to grab the untapped rural and semi urban market of Bangladesh.

However, the study suffers from some limitations. First, the sample size of the study is only 98, which may not represent the population. A larger size of sample could have portrayed the online spending of Bangladeshi customers much better. Second, the study considered only five demographic variables in finding the association between consumer demographic profile and online shopping behavior. However, some additional demographic variables like marital status, living status, religious affiliation, life-cycle stages etcetera could have been added. Addition of such variables would have displayed a far clearer picture of online spending of Bangladeshi customers. Third, the present study suffers from another limitation in that it did not consider the role of product categories (convenience goods, shopping goods, luxurious goods, hedonic goods, utilitarian goods etc.) while examining the consumption or spending by customers online. Customers have different ways of responding to different product categories- which has not been covered in this study. All the limitations described above may be considered for future researches. Despite these limitations, the authors strongly believe that the results of the study deserve consideration for policy formulation by online companies as a way to improving the online customer spending in Bangladesh.

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