Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2020 Vol: 19 Issue: 5

Evaluating the Factors Influencing Customer Perception on Online Buying Behavior of Sub Urban People of Bangladesh

Md. Al Amin, Bangabandhu Sheikh Mujibur Rahman Science and Technology University

Sabiha Matin, Daffodil International University

Md. Rakibul Islam, Bangabandhu Sheikh Mujibur Rahman Science and Technology University

Israt Jahan, Bangabandhu Sheikh Mujibur Rahman Science and Technology University

Md. Hafizur Rahman, Bangabandhu Sheikh Mujibur Rahman Science and Technology University


The research aims at analyzing the consumer online buying behavior of Sub Urban People of Bangladesh regarding the social media and online marketing platforms. The study, conclusive in nature, uses quantitative research methods and collected data through survey questionnaires by five Likert scales through online-offline basis from 285 respondents and in-depth interviews from 10 experts in this field having snowball sampling techniques. The relationship among the series of variables was done by sophisticated statistical tools including factor Analysis, regression, correlation, and hypothesis testing by t-statistics overcoming the limitations in the availability of data, financial problems, and time.

The researchers have found nine factors (Psychological factor, Personal Factors, Peer Influence, Web Influence, Brand Personality, Promotion, Environmental forces, Convenience, Process) which influence the Online Buying Behavior of Sub Urban People of Bangladesh. The relationship among the internal factors and processing factors are related; the external factors have also the correlated with processing factors (security, privacy or Trust issues) and positive Consumer Perception Or Consumer Buying Behavior is the results of this process factor which means psychological factor, personal factors, peer influence, web influence, brand personality, promotion, environmental forces, convenience factors generate Consumer perception for online buying behavior being loyal. The identified factors have significant relationship creating positive or negative consumer perception and convincing customers to purchase the products through internal factors & external factors which influence on security, privacy, and trust disposition attitude of the consumers. The organizations should not be focusing on ‘technologically’ only; instead, they need proper judgment on how consumers perceive their efforts in the process. The use of online marketing platforms has a positive perception in consumers’ minds.

The implications are to attract sub-urban people to use social media and other online platforms. Also, using online marketing platforms, a positive perception of the consumers can be understood by the companies. The research conforms to developing a new theory called Input Processing output (IPO) Model, methodological improvement, and practical implications of how organizations can capture the sub-urban as their customer through online.


Sub-Urban People, Online Buying Behavior, Customer perception, Social Media Marketing, Motivations.


With the advent of technology, social media, as well as other online marketing platforms, have proven to be very effective for the marketing efforts of the companies (Ngai et al., 2015). Although they are overwhelmingly popular among the city dwellers, there are vast implications of these platforms in the urban areas (Misirlis & Vlachopoulou, 2018). This study aims to evaluate the factors influencing consumer perception on the online marketing platforms for the Sub Urban people in Bangladesh. In doing this study, it has been assessed whether these platforms are appropriate for the promotion of products to the Sub Urban people of Bangladesh.

Problem Statement

The companies are continuously targeting urban people to conduct their marketing activities-besides, plenty of customers in the online market who are dwelling in the Sub Urban region. Therefore, the promotion of products to these people can be a massive success since companies are primarily entering in the online industry. Besides, the companies can target these people for a better result in the market, which plays a significant role in providing them with a competitive advantage (Amatulli et al., 2018). Online marketing platforms can make a difference because Sub Urban people are firmly enthusiastic about online marketing platforms. This study is needed to understand factors attracting the Sub Urban people and tapping into this vast market to gain a high profit.

Rationale Statement

Many types of research have been conducted on consumers' perceptions of online marketing. Several types of research have been conducted on online marketing platforms and their prospects for companies (Stephen, 2016). However, few of these researches fundamentally focused on measuring the influence on customer perception on the online buying behavior of Sub Urban People of Bangladesh. Since many people are newly using social media and other online platforms, they can be attracted by the companies more effectively. It is a vast scope for the companies that these factors can be positively used in the decision making of the companies (Amatulli et al., 2018; Al Amin et al., 2020). Therefore, this research will have a greater significance in the academic field and pave new possibilities. Furthermore, it will also be a new dimension for the companies to focus on. That is why this research bears great significance for the companies and researchers in this field.

Research Aims

The major aim of the research is to analyze the perception of the customers dwelling in the Sub Urban region regarding the online marketing platforms. To reach these objectivs, some specific objectives are needed.

There are some specific objectives of this research which are provided below:

• To find out the factors that influence consumers for purchasing from different online platforms in the Sub Urban people of Bangladesh;
• To evaluate how the online platforms are used by the companies directed towards the Sub Urban people of Bangladesh;
• To assess the effectiveness of the use of online marketing platforms in the Sub Urban people of Bangladesh.

Literature Review

Online Marketing

Online Marketing Platforms have drastically altered the orthodox notions of marketing practices, and there are abundant works of literature to bolster this proclamation. According to Reddy (2013), online marketing platforms can craft a profound and yet lucrative relationship with the customers in sales, marketing, and customer support. Dlodlo & Mafini (2014) stated that the use of online marketing could amend the marketing and operations efficiency of a firm. Furthermore, following the statement of Boyles (2011), it is grasped that online marketing platforms offer an assortment of prospects for firms that support it to attain marketing and operational proficiency by sinking costs. Moreover, Sharma (2011) states that the usage of online platforms such as websites, e-mails, online advertising, viral marketing, social media, etc. allows the companies to escalate the responsiveness of the customers about their brand and sustain their loyalty with professed quality and brand associations. Besides, Morgan-Thomas & Veloutsou (2013) depict that because of the virtual nature of the World Wide Web, possessing an established and trusted brand perceived in the cognizance of the customers, which provide acquaintance and reduce the perceived risk. However, various factors of online marketing are presumed to influence the perception of consumers. According to Schivinski & Dabrowski (2015), large companies like Coca-Cola have used Facebook to effectively connect with their customers and created a positive change in the perception of these customers about their products. However, Burke & Kraut (2016) stated that companies need a robust online marketing plan along with the usage of effective online marketing platforms to connect effectively with the consumers. Hence, it can be argued that the use of online marketing platforms can be effective for the positive perception of consumers. Still, it should be done according to a robust plan and effective implementation of the plan (Jung, 2017).

Consumer Buying Behavior

Consumer Buying Behaviour is coordinated towards understanding the path in which the people, associations, and gatherings pick, buy, and discard the products (Cruz-Cárdenas & Arévalo-Chávez, 2018). As per the examination led by Kotler & Keller (2012), different elements impact the buying behavior of the purchasers, including past involvement, taste, value, marking, and so on (Al Amin & Bhuiyan, 2019). The general execution of a business is fundamentally dictated by the comprehension of the consumer buying behavior by the organizations in the market (Bae & Lee, 2011) like different wares made by the organizations, comprehension of customer purchasing conduct for the stock is additionally vital (Haque & Faruquee, 2013). For this, they need data about share. Since there is no use for utilization, the purchasers of offers consider whether they will get adequate come back from their venture (Stephen, 2016). They might want to produce better benefits from their venture of offers (Raunaque et al., 2016).

For this reason, they consider the organizations with a better brand picture and remarkable notoriety of making a critical measure of benefit in the business (Pushpa Bhatt & Sumangala, 2012). Additionally, word to mouth preparation is essential for urging individuals to buy securities exchange partakes in the nation. If a few buyers can pick up benefits with their interests in the offer market even, they may suggest the offers of a similar organization (Bae & Lee, 2011). And the offer of that organization might be extremely famous among the customers, which they have closer and better associations with and tend to pull in a more considerable amount of offer for them (Haque & Faruquee, 2013).

The Theory of Reasoned Action can be considered here, which was created by Martin Fishbein & Ajzen in 1967 (Fishben and Ajzen, 1975). This theory depicts that consumers carry out actions based on their intention to create or attain a specific outcome (Lamb et al., 2011). Consumers only take particular actions when a specific result is expected. The consumers will purchase a product when they are convinced that their desires and needs will be fulfilled by that product (Kapoor & Kulshrestha, 2014). When all the factors of consumer perception are in favor of the decision making, the consumers are likely to purchase the products.

Factors of Consumer Perception

To contemplate the consumer perception of the online marketing efforts, the researcher has discovered some factors which have a closer connection with it. These factors are two types,' i.e., internal factors and external factors.

Some psychological factors like motivation, learning, attitude, beliefs, ego, and super-ego are also included among internal factors. Motivation is a factor that encourages a person to purchase a specific product. Attitude and beliefs also shape the potential consumer's perception regarding a product. These three factors can create positive customer perception (Dlodlo & Mafini, 2014 and ). Some psychosocial factors, which include ID, ego, and super-ego, may also be linked to the problem. ID comprises the unconscious and subconscious minds and the instincts of the individuals (Haque & Faruquee, 2013).

Superego is primarily associated with all levels of consciousness, and consumers tend to be attracted to socially acceptable commodities. Finally, the ego is the part of the whole personality of an individual, which compels one to decide on the purchase of the product (Singh, 2016). Then there are the personal factors which include age, income level, lifestyle, and personality (Hemsley-Brown & Oplatka, 2016). People of different ages tend to perceive their needs and the product's capacity to fulfill those needs differently (von Helversen et al., 2018). Furthermore, peer influence includes reference groups, family, roles and status, social values (Kapoor & Kulshrestha, 2014). Reference groups are the ones who have been satisfied after using the product of the company (Lamb et al., 2011). Also, social roles and status can shape their needs and demands of particular products (Singh, 2016). Lastly, social values can also promote these people to purchase the products.

Web influence includes factors like easy to read, easy to navigate, comfortably viewed, and quick to download (Lee, 2016). If the website is easy to read and easy to navigate, the potential customers will be satisfied with their encounter. The speed of the website should also be up to the market. It should not turn slow when there is high traffic. Any items should be downloaded quickly (Boyles, 2011).

Regarding the external factors, four categories have been selected for this research. They are brand personality, promotion, PESTEL, and convenience. Brand personality includes sincerity, excitement, competence, sophistication, and ruggedness (Bairrada et al., 2019). The company can flourish its brand image by doing marketing functions effectively and ensuring that the products are promoted honestly and sincerely (Güse, 2011). They want to ensure that the products that they bought will fulfill their needs. Also, the products have to be rugged as in sturdy and durable (Kapoor & Kulshrestha, 2014).

In the promotion category, various online promotional platforms are included, which can provide the company with a competitive edge. The factors that are included are Facebook, YouTube, LinkedIn, Twitter, Google+, etc. Among these, Facebook is the most prominent one, with more than 2 billion monthly active users (Lee, 2016 and Al Amin, 2018). There is a considerable scope for companies to reach out to more and more customers. Also, YouTube has gained much popularity in recent years (Singh, 2016). LinkedIn is a social media for professionals who are also used to promote various kinds of products. Twitter is used to provide public posts called tweets, which can be seen by the followers along with others. Lastly, Google+ has benefits that have other Google accounts such as Gmail (Lamb et al., 2011).

PEST/environmental factors are also crucial external factors. Firstly, political factors are essential, which include political turbulence, election, protests, terrorism, etc. When the political condition of a country is unstable, the consumers tend to avoid going to shops to purchase goods (Reic, 2016). The economic factors include economic policies, tax policies, income levels, unemployment rates, etc. The social factors include social values, ideologies, culture, religion, etc. At present, technological factors are more important than ever (Lamb et al., 2011). They include modern innovation, communication platforms, online devices and websites, databases, etc. Ecological factors are also important; the companies must ensure that environmental pollution and degradation is not accelerated with the manufacturing process (Reddy, 2013). Lastly, the legal factors indicate various kinds of legislation that are imposed by the government.

Convenience factors are also significant to consider. In this category, the major factors include distribution channels, distribution points, delivery time, and placement of order (Güse, 2011). The consumers like those companies which have convenient arrangements for order placement (Kapoor & Kulshrestha, 2014). After placing the order, the goods also need to be delivered timely.

Three significant factors are there, which are reinforced by the input factors. They consist of security, privacy, and trust disposition attitude (Al Amin, 2017). Security refers to be free from any threat, danger, or loss, which can incur from the transactions (Singh, 2016). There is also a risk of hacking the data, which can negatively influence consumer perceptions (Raunaque et al., 2016). Moreover, privacy refers to the situation where the private information of the consumers, such as ID number, bank account number, passwords, etc. is protected (Usmani, 2012). The consumers only decide to purchase items from a company that offers a higher level of privacy. Lastly, trust refers to the sense of reliance, conviction, and faith that an individual has on a particular company or a product (Coelho et al., 2018; and Al Amin & Mozid, 2017).

Conceptual Framework of the Study

The conceptual framework provides an idea about the overall perception and standpoint of the researcher and the pathway for deriving information. Using the existing knowledge and the information from different sources, the researcher has created the following conceptual framework in this research. There are three major dimensions of this conceptual framework which is related to the research. These three dimensions are Input, processing, and output of the model.

In the conceptual framework psychological factors have been adpapted from (ref) while the authors have adapted peer influence (social) from (ref), personal factors from (ref), and web influence from (ref). In accordance with (ref) brand personality was extracted for the research model while environmental factors (pest analysis) from (ref). Moreover, promotional factors from (ref), convenience from (ref), security from (ref), privacy from (ref), trust disposition from (ref); consumer perception from (ref). and consumer buying behavior from (ref) were taken.

Input factors affecting online consumer perception

Firstly, psychological factors such as motivation and learning create a greater sense of trust in the company (Jeong & Kim, 2017). Also, the attitude and beliefs of the consumers determine how secure they feel while using the website. Secondly, personal factors, such as the age and personality of the consumers, delineate their trust in the company. Besides, income level and lifestyle determine how much privacy they need while seeking the product. Also, peer influence such as reference groups and families etc. encourages consumers to try new products in the market (Kapoor & Kulshrestha, 2014). Therefore, Hypothesis 1: The internal factors are positively related to security, privacy, or Trust of Consumer perception.

Again, the external factors such as brand personality consisting of sincerity, excitement, competence, sophistication, and ruggedness may influence the trusting attitude of the consumers. For instance, consumers are willing to trust the companies that are sincere to them and show competence in delivering sophisticated services to them (Martín-Consuegra et al., 2019). Besides, the promotion also plays a pivotal role in this case as the social media platforms can be frequently used to reach to the consumers conveniently. Consequently, the PESTEL factors also influence the consumers. Lastly, distribution channels, distribution points, and the delivery time as part of the convenience factors influence the degree of trust of the consumers (Lamb et al., 2011). Thus Hypothesis 2: The external factors (Brand Personality, Promotional, PEST Factors, and Convenience) are positively related to security, privacy or Trust issues of Consumer perception.

Processing elements of affecting online consumer perception

The three processing elements are security, privacy, and trust disposal attitude, which are presumed to affect the consumer perception of online products. Firstly, security is a fundamental component of any transaction between consumers and businesses. It involves providing better products, reasonable prices, avoiding fraudulent actions, using secure means of transaction, etc. (Misirlis & Vlachopoulou, 2018; and Al Amin et al., 2020). The companies, which are able to provide these benefits to the consumers, are primarily ahead in the competition of the business. Secondly, privacy is another major factor affecting consumer perception since a substantial number of deceitful actions are seen in the era of online business (Obar & Oeldorf-Hirsch, 2020). The companies that can provide a significant amount of privacy are, therefore, preferred by the consumers. Lastly, the trust disposal attitude of the consumers is indispensable in the process of better consumer perception and purchase. Consumers frequently purchase from the companies that they trust, and they often stick with the same company if they are satisfied with the deal (Choi et al., 2017).

Final output

As per the conceptual framework, the input and processes interact to deliver a final output, which is a positive perception and purchasing of the product. When all the pieces of the framework fall in the appropriate places, the consumer may make a favorable decision to purchase the product (Yerasani et al., 2019). Emanating from security, privacy, and trust as part of the process, the positive consumer perception is the prime desire of the companies working in the online business sector. As all the companies are working to increase their consumer base, they would like to ensure that a large number of consumers purchase their products. To do so, their perceptions regarding the brand need to be positively changed (Shiau et al., 2017). Thus the Hypothesis 3: There is an impact of Security, privacy or Trust on customer for buying goods and services through online (Figure 1).

Figure 1: Proposed Research Model: Ipo Model Affecting Online Consumer Perception

Thus, it is evident from the literature the consumer perception of online shopping differs between individuals with a feeling that the quality of appropriate access and accessibility is limited to some degree in online shopping. However, evaluating each hypothesis statement, several techniques are required, which has been done to a later portion of the study (Misirlis & Vlachopoulou, 2018). To be sure, the researcher mentioned all the relevant crucial information to strengthen the significance of the conceptual framework concerning online shopping behavior.

Research Methodology

In the quantitative method, both the primary and secondary data has been used in this conclusive research. For collecting primary data, the researchers chose the convenience sampling method (Hossain & Al Amin, 2016). Using the method, the researcher selected 285 respondents as a sample based on the preference of the researchers who they thought would be suitable for the study (Shanahan et al., 2019; and Al Amin, 2020).). These respondents were dwellers of the Sub Urban areas in Bangladesh with different occupations and ages. The secondary information was collected from different kinds of research papers, journals, articles, magazines, newspapers, brochures, websites, blogs, and social media. Besides, the author utilized SPSS software for data analysis & Microsoft word application for data presentation (Liu et al., 2018). For identifying Sub Urban people of Bangladesh, the researcher considered some criteria which are i) the area where the internet facilities (Broadband and Wi-Fi) are not readily available, but people are using the internet for online shopping either through Mobile internet or by coming near Upazila ICT centers (Kabir & Roy, 2015); ii) the area where people as consumers have significantly grown usage interest and moderate knowledge of social media advertising. The authors have selected these 2nd criteria of the respondents as the city people, or urban people are losing faith in social media advertising. In contrast, the first criteria were chosen so that researchers could understand the real suburban people’s perceptions as there have already been a lot of studies have been conducted on urban population. On the other hand they Researchers primarily selected areas are i) Cumurdi Union, Bhanga, Faridpur, ii) Gava Union, Baniripara, Barisal, iii) Tujerpur, Bhanga, Faridpur. These are were convenient for researchers.

The authors used both metric (nominal and ordinal level data) and non-metric (Interval and ratio level data) data for the analysis of the research project (Mishra et al., 2018 and Al Amin, 2020). Besides, an in-depth interview with ten experts was considered. After that, the researcher analyzed the collected data employing the following statistical Models:

Regression Equation-1

Y= β01X12X23X34X4 + β5X5+ β6X6 + β7X7+ β8X8 + β9X9 + β10X10+ β10X10 + β11X11+ β12X12 + β13X13 + β14X14 + β15X15 + β16X16 + β17X17 + β18X18 + e

Here, Y = Security Process Factors, X1= Motivation, X2= Learning, X3 = Attitude & beliefs, X4= ID, X5 = ego, X6 = super-ego, X7= Age, X8 = Income Level, X9= Lifestyle, X10 = Personality, X11 = Reference Groups, X12 = Family, X13 = Roles and Status, X14= Social values, X15 = Easy to read, X16 = Easy to navigate, X17= Comfortably viewed, X18 = Quick to download.

Regression Equation-2:

Y= β019X1920X2021X2122X22+ β23X23+ β24X24+ β25X25+ β26X26+ β27X27+ β28X28+ β29X29 + β30X30+ β31X31+ β32X32 + β33X33 + β34X34 + β35X35+ β36X36 + β37X37+e

Here, Y = Security Process Factors, X19= Sincerity, X20 = Excitement, X21 = Competence, X22= Sophistication, X23= Ruggedness, X24= Facebook, X25= YouTube, X26= LinkedIn, X27 = Twitter, X28 = Google+, X29 = Political issues, X30= Economic, X31 = Social, Technological, X32 = Ecological, X33 = Legal, X34= Distribution Channel, X35 = Distribution Points, X36 = Delivery time, X37 = Placement of order.

Regression Equation-3

Y= β038X3839X3940X40+ e

Here, Y = Consumer perception, X38= Security, X39 = Privacy, X40= Trust e = error associated

Data Analysis and Presentation


In this chapter, the researcher presents analyses and interprets data that were collected from the primary source, questionnaires. Statistical methods have been used to analyze and interpret quantitative data and have also tested hypotheses to prove the arguments brought in this chapter.

Reliability and validity

The reliability and validity of the study have ensured through AVE, CR and Cronbach's Alpha given in Table 1.

Table 1: Average Variance Extracted (AVE) and CR
  Average Variance Extracted
Composite Reliability(CR) Cronbach's alpha
Psychological factor (PSF) 0.609 0.823 0.715
Personal Factors (PF) 0.604 0.820 0.790
Peer Influence (PI) 0.700 0.823 0.912
Web Influence (WI) 0.505 0.750 0.772
Brand Personality (BP) 0.580 0.805 0.791
Promotion (PRO) 0.513 0.758 0.701
Environmental forces (EF) 0.627 0.834 0.769
Convenience (CON) 0.791 0.883 0.727
Process Factors (PRS) 0.668 0.857 0.735

The AVE for each construct must be greater than 0.5 which stands that the construct explains more than 50% of the variance of the items in the research model while Hair et al. (2014) and (Malhotra & Das, 2016) Composite reliability (CR) must be more than 0.7 which explains 70% of the variance in the items. Both criteria are fulfilled for all of our variables. According to Malhotra & Das, (2016) the cut of value for Cronbach's alpha 0.7 while 0.6 is acceptable by the rule of thumb, which matched with the study of (Cronbach, 1951 and Cho, 2016). This study also matched the required cut value for Cronbach's alpha given in the Table 1.

Factor Analysis

The author has organized an exploratory factor analysis by considering the 37 variables. Initially, the PCA), a principal component analysis, was performed, where all the 37 variables were extracted to the maximum possible extent. The extraction score of more than (0.5) was only considered for the calculation. The value below 0.5 is eliminated during the principle component analysis (PCA).

Extraction Method: Principal Component Analysis

Ref to the above Table 2, the variables with loading factor less than 0.5 is eliminated during the EFA. The iterative process able isolates almost 40 components or variables. Ref to Table 4 total of eight components or factor was identified under different constructs. Out of 40 components, only 9 Components has able to deliver the Eigenvalue score >1. The source of cumulative variance has captured almost (71.15) % (Table 3). So consideration of these nine components based on Eigenvalue is justified (Schweingruber & McPhail, 1999). The component matrix is given in the Appendix-Table A1.

Table 2: Total Variance Explained
Dimension Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 9.608 27.451 27.451 9.608 27.451 27.451
2 3.461 9.889 37.340 3.461 9.889 37.340
3 2.870 8.199 45.538 2.870 8.199 45.538
4 2.000 5.715 51.254 2.000 5.715 51.254
5 1.688 4.822 56.076 1.688 4.822 56.076
6 1.496 4.275 60.351 1.496 4.275 60.351
7 1.398 3.994 64.345 1.398 3.994 64.345
8 1.198 3.421 67.767 1.198 3.421 67.767
9 1.184 3.382 71.149 1.184 3.382 71.149
10 0.964 2.753 73.902      
11 0.947 2.705 76.606      
38 0.300 0.856 6.017      
39 3.723E-16 1.064E-15 100.000      
40 -2.526E-16 -7.216E-16 100.000      


Table 3: Communalities
  Initial Extraction   Initial Extraction
Motivation 1.000 0.688 Competence 1.000 0.705
Learning 1.000 0.606 Sophistication 1.000 0.711
Attitude & beliefs 1.000 0.708 Ruggedness 1.000 0.667
ID 1.000 0.723 Facebook 1.000 0.789
Ego 1.000 0.625 YouTube 1.000 0.710
super-ego 1.000 0.665 LinkedIn 1.000 0.760
Age 1.000 0.656 Twitter 1.000 0.628
Income Level 1.000 0.686 Google+ 1.000 0.689
Lifestyle 1.000 0.733 Political issues 1.000 0.968
Personality 1.000 0.694 Economic 1.000 0.762
Reference Groups 1.000 0.579 Social Media 1.000 0.720
Family 1.000 0.550 Ecological 1.000 0.745
Roles and Status 1.000 0.682 Legal 1.000 0.690
Social values 1.000 0.685 Distribution Channel 1.000 0.968
Easy to read 1.000 0.826 Distribution Points 1.000 0.968
Easy to navigate 1.000 0.613 Delivery time 1.000 0.620
Comfortably viewed 1.000 0.649 Placement of order 1.000 0.665
Quick to download 1.000 0.693 Security 1.000 0.690
Sincerity 1.000 0.548 Privacy 1.000 0.680
Excitement 1.000 0.813 Trust 1.000 0.687

Results and Discussion on Factor Analysis

Ref to Appendix-Table A1, the result of the exploratory factor analysis revealed that for each of the 37 factors. The first component for consideration is the Psychological factor extracted from Maslow's (1958) hierarchy of needs theory and Freud’s psychoanalytic theory (1920). The significance value of the factor’s variables ‘motivation’ (0.792), ‘learning’ (0.670), ‘attitude and beliefs’ (0.406), and ‘id’ (0.482), ego and super-ego’ (0.261). So, consumers have to be motivated by informing more about the product that will lead to more purchases. The second component to consider in this case is Personal Factorsextracted from Kotler’s (2004) buyer black-box model, where the significance value of variables ‘income’ (0.691), ‘age’ (0.556), ‘lifestyle’ (0.535), and ‘personality’ (0.513). From the result, it is seen that high-income persons in society tend to be intrigued more easily using online marketing, and online attractions vary from age to age. Besides, the third component is Peer Influenceextracted from Sociological model by Brim (1968), where the significance value of variables are ‘Social roles and statuses’ (0.711), ‘social values’ (0.628), ‘reference groups’ (0.625), and ‘family’ (0.458). Consequently, the result illustrates that individuals with higher social status tend to be attracted more by the online for being convenient. After that, the fourth factor is Web Influenceextracted from the model of online consumer behavior by Kumar & Dange (2012), where the significance value of variables ‘speed of the website’ (0.639), ‘easy to read’ (0.636), and ‘user friendly’ (0.603). So, the consumers are satisfied with the company if its website provides a steady speed, and they can surf it conveniently. The fifth factor to be taken into consideration is Brand Personalityextracted from Aaker's (1997) Dimensions of Brand personality, where the significance value of variables ‘sincerity’ (0.691), ‘reliability’ (0.618), ‘exciting’ (0.590), ‘competence’ (0.516) and ‘sophistication’ (0.184). The result exhibits that consumers always like to associate themselves with the companies that show sincerity to them. The sixth factor is Promotion extracted from social media marketing communication model by Castronovo & Huang (2012), where the significance value of variables ‘Facebook’ (0.766), ‘Google+’ (0.208), ‘YouTube’ (0.205), ‘Twitter’ (0.104), and LinkedIn (-0.035). The result shows that Facebook’s rapidly growing popularity is an excellent opportunity for online promotion. The seventh-factor is Aguilar's (1967) Environmental forces (PEST Forces) that include ‘political, economic, social, and technological’ (Ramya & Ali, 2016). The significance values of these variables are ‘technological’ (0.651), ‘economic’ (0.615), ‘social’ (0.461), and ‘political’ (0.372). The eighth factor is Convenienceextracted fromLauterborn's (1990) 4C model, where the significance value of ‘convenient arrangements’ is 0.629, and ‘distribution channel and point’ is 0.481, which means it has a moderate level of connection with the eighth factor. Lastly, the ninth factor is Process extracted from the Privacy-trust-behavioral intention model by Liu et al. (2005). In this case, ‘security,’ ‘privacy,’ and ‘trust disposition’ have a significance value of 0.667, 0.461, and 0.461, respectively, and have the most connection level with this component. So, these three factors have a substantial amount of significance with the ninth factor.

Correlation Analysis (CA)

Pearson correlation (r) is 0.900; which means that there is a positive and very strong relationship between internal factors and process factors (Table 4). It means that environmental forces (internal) have an influence on process factors (security, privacy and trust) to buy their desired products on these platforms. However, the second Pearson correlation (r) is .858; which means that there is a positive and very strong relationship between external factors and processing factors. It means that environmental forces (external) influence process factors (security, privacy, and trust) to enhance customers to makes a transaction for making a loyal customer. Moreover, the third Pearson correlation (r) is 0.662, which means that there is a positive and strong relationship between process factors and consumer perception. This results also mean that process factors (security, privacy and trust) changes the perceptions or buying attitudes of the customers.

Table 4: Correlation Analysis (CA)
Variables r Strength of association
Internal Factors and Process Factors 0.900 Very strong
External factors and processing factors 0.858 Very Strong
Process factors and Consumer perception 0.662 Strong

Coefficient of Determination (R2): Results and Discussion

From equation – 1, the value of R2 is 0.810, which means that 81% variance of dependent variables can be changed concerning the independent variables. The value of adjusted R2 also denotes is.780, in which the dependent variables can be caused 78% variance due to additional independent variables. Whereas the equation – 2 shows that the value of R2 is 0.722, which means that 72% variance of dependent variables can be changed concerning the independent variables. The value of adjusted R2also denotes is 0.717, in which the dependent variables can be caused 71% variance due to additional independent variables. So, this study explains that a customer’s perceived value influences confirmation.

Moreover, from the equation- 3, the value of R2 is 0.930, which means that 93% variance of dependent variables can be changed concerning the independent variables. At the same time, the value of adjusted R2 also denotes 0.921, in which the dependent variables can be caused by 921% variance due to additional independent variables (Table 5).

Table 5: Model Summary
Equation No. R Square Adjusted R Square Standard error Independent and dependent variables
11 0.810 0.780 0.732 Internal Factors and Process Factors
22 0.722 0.717 0.649 External factors and processing factors
33 0.930 0.921 0.709 Process factors and Consumer perception

Hypothesis Testing: Results and Discussion

From the above Table 6, the value of b is 0.830 t statistic value is 6.883, critical value is 1.976, at the level of significance of 0.05, df value is 283, which means that t-statistic value is > critical value which is (6.883> 1.976). However, it proves that the hypothesis (H1) is supported. Therefore, it can be expected that psychological factors, peer influence (social), personal factors from and web influence have a significant impact on processing factor (security privacy and trust) that matches literature provided by (ref)

Table 6: t Statistic For Hypothesis Testing
t- statistic: Hypothesis
Hypotheses b t Value Critical value Sig Result Remarks
H1 0.830 6.883 1.976 000 6.883> 1.976 Supported
H2 0.643 4.548 1.976 000 4.548> 1.976 Supported
H3 0.562 3.876 1.976 000 3.876> 1.976 Supported

In hypothesis (H2), the value of b is 0.643, t statistic value is 4.548, critical value is 1.976, at the level of significance of 0.05, df value is 283 which means that the t statistic value is more than critical value which is (4.548> 1.976) that supports H2. Therefore, brand personality, environmental factors, promotional factors, and convenience have a significant impact on processing factor (security privacy and trust) that matches literature provided by found by (ref).

The Table 6 explains that the value of b is 0.562 in H3, t statistic value is 3.876 critical value is 1.976, at the level of significance of 0.05, df value is 283 which means that the t statistic value is > critical value which is (3.876> 1.976) which proves the acceptance of the hypothesis. It stands that processing factor (security privacy and trust) leads to Customer perceptions, which is explained by (ref).

Limitations and Implications

Limitation of the Study

The author faced budget limitations since the study required traveling to collect data from Sub-Urban locations in Bangladesh. There is some degree of difficulty in finding the respondents as they are not always available. Besides, it could not be identified whether there are some other factors influencing consumer perception. Then, valid generalization is well-practiced to make sure the collected pieces of information are well appreciated by the researcher. Besides, it is expected from other researchers to conduct further study on additional factors of online marketing that can affect consumer perception of the product and ensuring that they purchase the product. By conducting further research, it will be understood which factors are most significant in affecting consumer perception (Jacobson et al., 2020).

Manegerial Implication

Online marketing platforms drastically improve the effectiveness of the marketing process (Boateng & Okoe, 2015). According to the research finding, the consumers of the sub-urban need to have the appropriate motivation and learning to make positive decisions to purchase the. Also, the results showed that online marketing is an essential part of executing the organizational marketing strategy to raise brand awareness since the manufactured products and services are available for a very long time to a more focused global consumer. The desire to purchase those products and services will remain escalated to the sub-urban consumer as long as the website still speedily functions and remains relevant. The outcomes of the marketing tools that are used online for targeting specific clients, carrying out more tasks, and saving a considerable resource for the company are essential findings of the study. So the companies need to enrich consumers providing reliable product information continuously. Moreover, the effectiveness of the online marketing process also depends on the consumer’s trust level. Peer influence incensement through online marketing strategies not only depends on the goods to roll in but also creates positive vibes to purchase. Each of the factors has a decent level of relationship with consumer perception, which is why the marketing managers can use these factors in their decision-making product (Chen & Lin, 2019; Arora & Sanni, 2019). It can improve the brand image of the company and gain it more profitability in the business (Kapoor et al., 2018).

Theoretical Contribution

The prior researchers do mention various factors that influence consumers’ perception and motivates their behavior through the Consumer behavior model. However, the author of this study illustrates specifically how consumers in the sub-urban influenced to purchase and re-purchase step by step through merging the IPO model in which Input, process, and output function as marketing efforts, purchase process, and consumer response to the marketing efforts. As consumers increasingly use social media and other online platforms, businesses can be more drawn to them. It is a comprehensive opportunity for companies to make effective use of these factors in company decision-making.

Conclusions and Recommendations

The conclusion was reached that the identified factors have a significant relationship with the processes that create positive or negative consumer perceptions and compelling them to purchase the company’s product (Algharabat et al., 2020). The internal factors, including psychological, personal, peer influence, and web influence, as well as the external factors involving brand personality, promotion, PEST, and convenience, significantly influence the security, privacy, and trust disposal attitude of the consumers. So, the companies have to emphasize these factors and provide better services so that consumers are encouraged to purchase from the companies frequently (Alalwan, 2018). They need to be technically savvy with the way they perceive their efforts in the process. For attracting the consumers, the following recommendations can be provided: Provide sufficient information of company background and product qualities to the consumers so that their learning is flourished and they have the motivation to purchase the products. The provision and promotion of product need to determine by age, income, and lifestyle of consumers to play a crucial role in their satisfaction.Companies should emphasize word of mouth in order to use a group of peers, one of the most effective methods of publicity. A robust brand personality has to be created with continual effectiveness in preserving. Promotion mode should be appropriate for the consumers in order to make the most of their cultural and attitudinal factors. The company website is convenient to distribute process should be efficient (Gucciardi & Jackson, 2015). This work will, therefore, be more important in the academic field and will pave the way for new opportunities. In addition, businesses will also concentrate on a new dimension.


1 Y = β0 + β1 X1 + β2 X2+ β3 X3 + e; 0.530 +0.314X1 + 0.505 X2 + 0.083 X3 + 301.735

2 Y = β0 + β4 X4+ β5 X5+ β6 X6 + e; 0.543 + 0.014 X4 + 0.000 X5 + 0.872 X6 +0 .24267

3 Y = β0+ β7 X7+ β8 X8+ β9 X9+ e; 0.662+ 0.810 X7 + 0.063 X8 + 1.502 X9 + 0.23894


Table A1
Component Matrixa
1 2 3 4 5 6 7 8 9
Motivation 0.892 -0.21 0.196 -0.256 0.056 -0.281 0.085 0.212 0.502
Learning 0.67 -0.207 0.599 -0.05 0.083 0.131 -0.039 -0.007 -0.202
Attitude 0.406 -0.1 0.365 0.115 0.342 -0.153 -0.404 -0.174 0.228
ID 0.482 0.073 0.43 0.169 0.104 -0.428 -0.063 0.022 -0.272
EGO and Super EGO 0.61 -0.007 0.52 -0.229 0.012 0.44 0.021 0.179 -0.091
Age -0.214 0.556 0.447 -0.129 0.197 0.016 -0.054 -0.226 -0.029
Personality -0.108 0.513 0.319 -0.043 0.276 -0.016 0.364 -0.244 -0.093
lifestyle -0.87 0.535 0.322 -0.022 -0.443 -0.194 0.101 0.19 -0.091
Income -0.189 0.691 0.013 -0.006 -0.418 -0.194 0.048 0.027 0.062
Reference Groups -0.137 -0.201 0.625 -0.329 -0.058 0.114 0.154 0.002 0.309
Family -0.75 0.231 0.458 0.195 -0.078 -0.113 0.134 0.224 0.279
Social roles and status -0.29 0.006 0.711 -0.005 0.134 -0.054 -0.14 -0.005 -0.057
Social values -0.132 0.235 0.628 0.132 0.341 0.149 -0.109 -0.216 -0.011
easy to read -0.2 -0.299 0.81 0.636 -0.093 0.368 0.079 -0.167 -0.111
user friendly 0.273 -0.14 0.715 0.603 -0.088 0.528 0.062 0.086 -0.155
The speed of the website 0.194 -0.168 0.102 0.639 -0.203 -0.017 0.09 -0.048 -0.277
competence -0.153 -0.203 0.143 0.65 0.516 0.129 -0.111 -0.289 0.428
Sincerity -0.188 -0.058 -0.276 0.268 0.691 0.013 0.036 0.074 -0.146
Exciting 0.145 -0.075 -0.025 0.035 0.59 0.205 0.088 -0.264 0.229
sophistication -0.228 0.056 0.607 -0.219 0.884 0.126 -0.267 0.468 -0.049
Reliability -0.118 -0.043 -0.474 0.267 0.618 -0.054 -0.051 -0.029 0.071
Facebook -0.89 -0.239 -0.102 0.057 -0.1 0.766 -0.112 -0.131 -0.082
YouTube. -0.304 0.416 0.26 0.362 0.192 0.605 0.323 -0.131 0.041
LinkedIn 0.209 0.468 0.457 0.404 0.223 0.735 0.295 0.057 0.114
Twitter -0.128 0.468 0.24 0.542 -0.296 0.704 0.055 0.089 0.119
Google 0.295 0.515 -0.081 0.415 0.126 0.708 0.065 0.328 0.24
Political 0.563 0.12 -0.129 -0.319 0.14 -0.071 0.72 -0.013 -0.125
Economical 0.328 0.158 -0.309 -0.201 0.037 -0.029 0.615 0.198 -0.032
Social -0.136 0.778 -0.01 -0.303 -0.16 0.017 0.861 -0.117 0.001
Technological -0.048 0.108 -0.299 -0.259 0.262 -0.221 0.651 0.22 0.032
Distribution channel and point. 0.335 -0.054 -0.13 -0.107 0.539 0.174 -0.145 0.481 0.059
Convenient arrangements 0.078 0.016 -0.278 0.296 0.096 -0.061 -0.402 0.629 -0.053
The Security, -0.251 -0.079 -0.208 0.021 0.224 -0.208 -0.103 0.168 0.667
Privacy 0.001 0.078 -0.01 -0.303 -0.16 0.017 -0.136 -0.117 0.461
Trust Disposition 0.001 0.078 -0.01 -0.303 -0.16 0.017 -0.136 -0.117 0.461


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