# What Factors Determine the Online Consumer Behavior in This Digitalized World? A Systematic Literature

Authors: Muluken Ayalew; Shimelis Zewdie
Journal: Human Behavior and Emerging Technologies
 (2022)

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## Abstract

This review study envisioned to address the basic objective that is to investigate determinants of online consumer behavior. A conventional review strategy was used to address the objective raised above, i.e., systematic strategy, and also, the obtained data were analyzed via content analysis. In addition to the above, the study also employed descriptive research design to present the obtained result descriptively. According to the generated findings, purchase intention is the most studied area, which is followed by adoption, and conversely, continuance or repurchase stage of online consumer behavior is the most underresearched area. Perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience are the most influential factors significantly affecting online consumer behavior at large. Therefore, it is advised that industries those are experienced or newcomers in the market to work on the identified factors determining the online consumer behavior, to sustain and achieve success in this dynamic world.


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## Body

## 1. Introduction

The society that we live in is in a continuous state of change [1]. The changed lifestyle of individual consumers, for instance, has changed their way of doing things such as consumption patterns [2–4]. Such patterns have changed numerous times in history [5–7]. For example, approximately 100 years ago, consumption occurred in the form of bartering or purchases made by traveling merchants [8, 9]. Since then, purchases have been made by means of catalogs, retail or boutique stores, and convenience stores like supermarkets and department stores [9–11].These days, the developments in the field of communication, technology, information, and marketing have created new shifts in the way consumers inform and buy certain products and services [12]. In particular, in the last two decades, consumers have become more and more accustomed to using the internet as means to inform themselves about products or services and about other information in general [13]. It has become ever so important for the companies of the 21st century to work on the integration of new technologies and to understand how consumers use the internet [12]; while this day, everything is linked with the World Wide Web, whether it is social interaction or shopping [2]. Companies become aware of the advantages of working on such technologies can bring in order to provide better consumer services [14]. Therefore, in a market environment that is constantly changing, understanding customer buying behavior is decisive for companies to operate successfully and efficiently [15]. And also, understanding consumer behavior is considered the cornerstone for successful marketing, reliable production management, and the success of research and development activities [16].Different scholars have given their own definitions concerning the concept of consumer behavior from various corners of view but with similar understanding. For instance, Kotler [17] defined as it the study of how people buy, what they buy, when they buy, and why they buy. According to Jacoby [18], consumer behavior refers to the acquisition, consumption, and disposition of products, services, time, and ideas by decision-making units. It is the study of consumers and the processes they use to choose, use (consume), and dispose of products and services, including consumers’ emotional, mental, and behavioral responses [19]. As defined by Schiffman and Kanuk [20], consumer behavior is the behavior that consumers display in searching for, purchasing, using, evaluating, and disposing of products and services that they expect will satisfy their needs.The American Marketing Association on the other hand, defined consumer behavior as the dynamic interaction of affect and cognition, behavior, and the environment by which human beings conduct the exchange aspects of their lives [21]. In this definition, consumer behavior is viewed as a relationship between the environment and the consumer’s psychological and emotional state [22]. The environment includes all the things that influence the thoughts, feelings, and actions of consumers [23]. These include advice from other consumers, advertisements, price information, packaging, product appearance, blogs, and many others [24]. It is important to recognize from this definition that consumer behavior is dynamic, involves interactions, and exchange decisions [25, 26]. From the above definitions, therefore, it can be understood that acquisition, consumption, and disposition issues are the main pillar points that have been used to explain the concept of consumer behavior by scholars.In the modern world, the popularity of the internet is increasing rapidly [27–29] as a result, companies have started investing hundreds of millions of dollars in efforts to establish an electronic presence on the internet [30]. Accordingly, this vital emphasis on the rapid development of the internet has drastically modified the lives of consumers around the world and played an essential role in globalizing and changing the consumer buying process [28, 31]. For example, the expansion of online shopping has provided customers the option to easily compare product characteristics and prices, making it the most flexible way of purchasing [32]. Online shopping also adds great convenience to the life of the people, and they do not have to spend their time going to a store or driving to retail stores [33, 34]. At large, buying online is always a more beneficial deal than visiting retail stores in terms of more various options and time efficiency [35, 36].In the current global scenario, the growth of online commercial retail transactions has remarkably been obtaining the utmost emphasis; businesses have been undergoing a huge digital transformation [37–39]. For instance, the integration of new digital technologies, digital business models, including platform-based multisided market places, access to information, a global vision, and changes in computation and mobile shopping [40–43]. All of these digital transformations have led to changes in peoples’ purchase behavior and consequently, that of the consumer [44]. This day, therefore, consumers, not only those from well-developed countries, but also, those from developing countries are getting used to the new shopping channel [45].Over the last decade, the number of online shopping portals, the breadth of products available online, and access to fast internet has continuously grown [46]. This development has led to both a maturing of online shopping as a retail channel and profound changes in people’s shopping behavior [47, 48]. In 2019/2020, online sales were estimated to account for $3.36 trillion or 13.6% of retail sales globally, representing a 20.2% increase over the previous year. This includes, for instance, 34.1% of total retail sales in China, 21.8% in the UK, and 11% in the US (Ibid.). The sales predicted a growth of up to $4.8 trillion by 2021/2022 [49]. However, online shopping is still in the infancy stage in most developing countries. Statistics indicate that only 15% of African households made an online purchase in 2018 [50]. This situation makes understanding of the determinants of online consumer behavior extremely essential for industries’ online business success [51–53].According to various sources, e.g., Elhoushy and Lanzini [54]; Reed et al. [55]; Tanrikulu [56]; and Zhang et al. [57], online consumer behavior research is a young and dynamic academic domain, which is described by a diverse set of variables studied from multiple theoretical perspectives [58–60]. The Theory of Reasoned Action [61], the Technology Acceptance Model [62], the Theory of Planned Behavior [63], Innovation Diffusion Theory [64, 65], and Flow Theory [66] are some of the theories that have been used by most researchers for investigating online consumer behavior [67, 68].Online consumer behavior has been the subject of considerable research in the last few years [69], and scholars continue to explain online consumers’ behavior from a diverse perspective [70]. Such as website use, future use, purchase, future purchase, unplanned purchase, channel preference, and satisfaction [71]. This hopefully contributes to describing the relationships between key variables that predict and determine consumer behavior in electronic channels (Ibid.). The recent failure of a large number of companies operating online epitomizes the challenges of working via virtual channels and underscores the requirement to better understand key drivers of online consumer behavior [72]. One reason might be that of limited scholarly attention has been devoted to understanding potential factors that influence online consumer behavior [69].Despite the increasing attention and interest surrounding online consumer behavior in the last decade [73], the studies appear relatively still immature and fragmented with contradictory findings, which exhibits an important research potential. For example, there is a paucity of documented studies that attempt to integrate research findings across studies from a theoretical marketing and consumer behavior perspective [73]. Besides, prior studies have investigated online consumer behavior (e.g. [74–79]), nonetheless, without considering variability often exists among online consumers; let us say, in terms of their a priori preferences or attitude regarding a technology [69].In particular, online consumer purchase stages are intention, adoption, and continuance, and various studies were carried out these days. For instance, factors affecting consumer’s intention (e.g. [80–86]), relationships of consumers’ intention (e.g. [87]), and consumer’s adoption (e.g. [28, 88–91]). There are also studies conducted on consumer’s continuance, for example, Gidey [34]. However, there is an insignificant number of studies were conducted on the determinants of purchase intention to continuance with regard to online consumer behavior [92–95].In conclusion, there are shortages of studies in the existing literature on understanding the determinants of online consumer behavior in respect of purchase intention, adoption, and continuance stages in a comprehensive manner in this digitalized world; though there are significant numbers of studies predominantly conducted in relation to the two online purchase stages (purchase intention and adoption). Therefore, this study will investigate determinants of purchase intention to continuance in respect of online consumer behavior, using the Cheung and Chan model.The main reason for selecting the Cheung and Chan model specific to this study is that it is an integrative model that was developed by Cheung and Chan, which apparently stated the potential online consumer behavior determinants into five major domain areas (individual/consumer characteristics, environmental influences, product/service characteristics, medium characteristics, and online merchant and intermediary characteristics) with major online consumer purchase categories (intention, adoption, and continuance).


### 1.1. Research Question

The following research question is addressed:(i)
What are the factors determining online consumer behavior using the Cheung and Chan model?


### 1.2. Objective of the Study

This study was focused on the following objective:(i)
To investigate determinants of online consumer behavior using the Cheung and Chan model


## 1.1. Research Question

The following research question is addressed:(i)
What are the factors determining online consumer behavior using the Cheung and Chan model?


## 1.2. Objective of the Study

This study was focused on the following objective:(i)
To investigate determinants of online consumer behavior using the Cheung and Chan model


## 2. Research Methods

### 2.1. Review Method

This study follows a systematic literature review method to analyze, summarize, and draw inferences [96, 97] from the accessible literature on online consumer behavior. The intention of the current study is to analyze and categorize the available literature on online consumer behavior into different focus areas such as based on the purchase stages (intention, adoption, and continuance) and to identify avenues for future research. This review approach comprises three steps and discusses time horizon, database selection, and article selection.


### 2.2. Time Horizon for the Selection of Articles

For the review and assessment process, the date of publication of the journal articles considered was between the beginning of 2010 and the beginning of 2022. The year 2010 was chosen as the starting point for collecting the relevant data because extended time gives chance to get rich evidence to answer the raised research questions. As Meline [98] suggests, whatever time period is selected, reviewers are expected to provide sufficient justification for their choice. Likewise, the beginning of 2022 was selected as the endpoint to include the most recent academic journal publications in light of the increase in articles that have addressed this highly significant topic.


### 2.3. Selection of Databases

This study used a number of online databases to identify current and pertaining literature on online consumer behavior. The studies were carried out in the English language and the sources of online databases were Wiley Online Library, SpringerLink, Emerald Insight, Taylor and Francis, PubMed, Google Scholar, and ScienceDirect. While an attempt was made to include the most articles possible, the present study does not claim that the databases are either complete or exhaustive in nature.


### 2.4. Article Selection

The current study followed a systematic review procedure as precisely indicated in Figure1 and described in the following manner. Firstly, keywords were defined as search criteria in online databases. The keywords encompassed “Consumer Behavior,” “Internet Shopping,” “Online Consumer Behavior,” “Online Consumer Purchasing Behavior,” “Online Shopping,” “E-Tail,” “E-Commerce,” “E-Shopping,” and “Online Consumer Purchasing” in the title of the above-mentioned online databases and contained in all text. Next, every article in the leading academic journals from 2010 to 2022 was considered. 981 articles were selected and the preliminary result included 115 articles.Figure 1
Summary of article selection process. Source: Adapted from Yong et al. [147].Then, the abstracts were read to evaluate the relevance of journal articles in online consumer behavior. In this regard, articles that seemed nonrelevant to this study were eliminated to ensure consistent focus and to reduce bias. Further, duplications of articles were eliminated to avoid counting a paper twice in our analysis [99]. This process resulted in 50 articles for review—and that were chosen based on their originality, clearly stated aims, and relevance. Figure 1 presents a summary of the article selection process used by this study.Concerning the nature of the studies considered under this systematic review, as can be observed in Figure2, the large majority of the included studies are quantitative in nature, which covers 88% of the total selected studies, followed by mixed (8%), systematic (2%), and narrative (2%) studies.Figure 2
Nature of the studies.


### 2.5. Data Analysis

At first, all the online consumer behavior data were collected from the involved studies in this review study. The collected online consumer behavior data were processed iteratively in order to answer the questions raised in the introduction part of this study. In this regard, this study employed a content analysis method to analyze the collected data. According to Elo et al. [100], content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given data, such as qualitative or quantitative. It is a systematic and quantitative approach to analyzing the content or meaning and describing the phenomenon of an organization [101]. Thus, using this analysis method, researchers and scholars can quantify and analyze the presence, meanings, and relationships of certain words, themes, or concepts, for instance, online consumer behavior [100].


## 2.1. Review Method

This study follows a systematic literature review method to analyze, summarize, and draw inferences [96, 97] from the accessible literature on online consumer behavior. The intention of the current study is to analyze and categorize the available literature on online consumer behavior into different focus areas such as based on the purchase stages (intention, adoption, and continuance) and to identify avenues for future research. This review approach comprises three steps and discusses time horizon, database selection, and article selection.


## 2.2. Time Horizon for the Selection of Articles

For the review and assessment process, the date of publication of the journal articles considered was between the beginning of 2010 and the beginning of 2022. The year 2010 was chosen as the starting point for collecting the relevant data because extended time gives chance to get rich evidence to answer the raised research questions. As Meline [98] suggests, whatever time period is selected, reviewers are expected to provide sufficient justification for their choice. Likewise, the beginning of 2022 was selected as the endpoint to include the most recent academic journal publications in light of the increase in articles that have addressed this highly significant topic.


## 2.3. Selection of Databases

This study used a number of online databases to identify current and pertaining literature on online consumer behavior. The studies were carried out in the English language and the sources of online databases were Wiley Online Library, SpringerLink, Emerald Insight, Taylor and Francis, PubMed, Google Scholar, and ScienceDirect. While an attempt was made to include the most articles possible, the present study does not claim that the databases are either complete or exhaustive in nature.


## 2.4. Article Selection

The current study followed a systematic review procedure as precisely indicated in Figure1 and described in the following manner. Firstly, keywords were defined as search criteria in online databases. The keywords encompassed “Consumer Behavior,” “Internet Shopping,” “Online Consumer Behavior,” “Online Consumer Purchasing Behavior,” “Online Shopping,” “E-Tail,” “E-Commerce,” “E-Shopping,” and “Online Consumer Purchasing” in the title of the above-mentioned online databases and contained in all text. Next, every article in the leading academic journals from 2010 to 2022 was considered. 981 articles were selected and the preliminary result included 115 articles.Figure 1
Summary of article selection process. Source: Adapted from Yong et al. [147].Then, the abstracts were read to evaluate the relevance of journal articles in online consumer behavior. In this regard, articles that seemed nonrelevant to this study were eliminated to ensure consistent focus and to reduce bias. Further, duplications of articles were eliminated to avoid counting a paper twice in our analysis [99]. This process resulted in 50 articles for review—and that were chosen based on their originality, clearly stated aims, and relevance. Figure 1 presents a summary of the article selection process used by this study.Concerning the nature of the studies considered under this systematic review, as can be observed in Figure2, the large majority of the included studies are quantitative in nature, which covers 88% of the total selected studies, followed by mixed (8%), systematic (2%), and narrative (2%) studies.Figure 2
Nature of the studies.


## 2.5. Data Analysis

At first, all the online consumer behavior data were collected from the involved studies in this review study. The collected online consumer behavior data were processed iteratively in order to answer the questions raised in the introduction part of this study. In this regard, this study employed a content analysis method to analyze the collected data. According to Elo et al. [100], content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given data, such as qualitative or quantitative. It is a systematic and quantitative approach to analyzing the content or meaning and describing the phenomenon of an organization [101]. Thus, using this analysis method, researchers and scholars can quantify and analyze the presence, meanings, and relationships of certain words, themes, or concepts, for instance, online consumer behavior [100].


## 3. Discussions of Findings

This part of the study focused on discussing findings obtained from relevant studies exclusively conducted on online consumer behavior in relation to its determinants of online consumer purchase. This study considered three basic domains such as presenting general results about online consumer behavior, then, discussing and addressing the specific objective presented in the introduction part of chapter one will follow. This objective is to investigate determinants of online consumer behavior. Through this, this study will apparently discuss the center of focus, level of analysis, models/theories employed, target group, and method of analysis, of the selected studies, to draw gaps, which can open up a juncture for future research to be conducted based on the gaps.To address the research objective, i.e., to investigate determinants of online consumer behavior, 50 studies were considered, which comprised empirical and conceptual studies.


### 3.1. General Results

In advance, addressing the specific objective, some general issues are presented and discussed in the following sections such as online consumer behavior studies by a list of published journals, nature of industries, sector, country and region (where online consumer behavior studies were conducted), methods of data analysis, research design, theory/model employed, sample source, and also, discusses online consumer behavior studies based on the center of focus. The following Table1 presents online consumer behavior studies by a list of publishing journals.Table 1
List of online consumer behavior studies published journals.
Journal nameNumberPercentageAsia Pacific Journal of Marketing and Logistics (Emerald)24Cogent business & management24International Journal of Marketing Studies24Journal of Business Research (Elsevier)24Journal of Computer Information Systems24Journal of Retailing and Consumer Services (Elsevier)24Journal of Theoretical and Applied Electronic Commerce Research24Sustainability (MDPI)24Others3468Total50100Source: own survey (2022).From 50 eligible studies found in 50 articles in 42 journals, the majority of journals had published just one (N=34) or two or more (N=8) articles. Table 1 shows the most prolific journals with at least two published online consumer behavior studies. The most prolific journals are Asia Pacific Journal of Marketing and Logistics (Emerald), Cogent Business and Management, International Journal of Marketing Studies, Journal of Business Research (Elsevier), Journal of Computer Information Systems, Journal of Retailing and Consumer Services (Elsevier), Journal of Theoretical and Applied Electronic Commerce Research, and Sustainability (MDPI) published two articles, respectively. Figure 3 presents online consumer behavior studies by nature of the organization.Figure 3
Online consumer behavior studies by nature of organization.According to Figure3, almost equivalent share of online consumer behavior studies conducted in public, private, and mixed (i.e., studies conducted using both natures of organizations such as publicly based organizations and private ones together) natures; accordingly, their share out of the total studies are presented as 38% (19), 32% (16), and 28% (14) for public, private, and mixed, and the remaining 2% (1) is not applicable studies, respectively. Therefore, as can be understood from the result above, there are still shortages of studies conducted at the global level by taking into account both natures of organizations (public and private) together in the extant studies, which predominantly emphasized online consumer behavior, while currently almost equal emphasis is being given by researchers and scholars carrying out studies that having either public or private nature in the extant literature. This situation may result in a problem to identify determining factors affecting online consumer behavior that are equally applicable to all organizations. The next section presents online consumer behavior studies by sector.Figure4 presented online consumer behavior studies sector-wise; accordingly, a large number of studies 50% (25) were conducted in the service sector, and online consumer behavior studies were done by taking into consideration both sectors, i.e., service and manufacturing, covering the second-largest number that is 34% (17) and manufacturing 14% (7), and the remaining 2% (1) is not applicable to either of the case. From the studies conducted in the service sector, education takes the leading position of 52% (13) particularly studies focused on tertiary level, i.e., university, and the next place is owned by other services such as bank, finance and insurance, hotel, health, and administration cities. Therefore, the finding indicated that there is a lack of online consumer behavior studies in some sectors like the manufacturing sector, and also the available studies conducted on the sector are only limited to apparel, fashion, pharmaceutical, and wood furniture industries. Similarly, there is also a shortage of online consumer behavior studies on primary and secondary level education. Figure 5 presents online consumer behavior studies by country of origin.Figure 4
Online consumer behavior studies by sector.Figure 5
Online consumer behavior studies by country.Figure5 described about online consumer behavior studies by their countries of origin, therefore, the finding revealed that online consumer behavior is a concept with a global reach from all corners. However, there is a concentration of online consumer behavior studies in specific countries, for instance, USA, which have the biggest share in the extant literature. Based on region, North America, Asia, and the Middle East are taking the leading positions in the number of online consumer behavior studies. On the other hand, online consumer behavior studies are less represented in some content, for example, Africa. This can expose a trend that raises questions about the aspirations of generalizable knowledge on online consumer behavior. Therefore, from the finding obtained, African countries are the place where a shortage of online consumer behavior studies is exclusively seen. And also, a number of online consumer behavior studies are insufficiently conducted across countries or by considering two or more countries or conducted at a multinational level, the existing studies. Figure 6 portrays data analysis methods employed by online consumer behavior studies.Figure 6
Data analysis methods employed by online consumer behavior studies.As can be understood from Figure6, regarding data analysis methods used, 58% (29) of the online consumer behavior-focused studies were analyzed using structural equation modeling (SEM), 14% (7) factor analysis (FA), 10% (5) descriptive statistics (such as frequency, mean, median, and standard deviation), 8% (4) mixed (data analysis methods used particularly to qualitative and quantitative data analysis methods, e.g., FDG, interview, SEM, MLRM, and correlation test together), 6% (3) of the studies were analyzed using multiple linear regression model (MLRM), and very limited number of the studies were used in correlation (Pearson’s correlation test) and ANOVA tests, which cover 2% (1) and 2% (1), respectively, of the selected studies. Therefore, this shows that almost more than half of the studies focused on online consumer behavior employed inferential statistics. Similarly, 92% of online consumer behavior studies used a cross-sectional research design and were self-reported. Furthermore, a large majority of online consumer behavior studies are surveys which accounts for 84%, and the rest 8% goes for studies conducted using a mixed approach. The following section describes the model/theory employed by online consumer behavior studies.Figure7 has presented model/theory employed by online consumer behavior studies; accordingly, only 42% of the studies were conducted using clear and conventional model/theory such as expectation-confirmation model (ECM) with task-technology fit (TTF) model and the trust factor (ECM, TTF, and TF) 2% (1), technology acceptance model and the theory of planned behavior 10% (5), technology acceptance model (TAM) 6% (3), the theory of planned behavior (TPB) and the protection motivation theory (PMT) 2% (1), unified theory of acceptance and use of technology (UTAUT) and innovation resistance theory (IRT) 2% (1), unified theory of acceptance and use of technology (UTAUT) 6% (3), theory of planned behavior (TPB) 4% (2), theory of reasoned action (TRA) 2% (1), theory of reasoned action (TRA) and theory of planned behavior (TPB) 2% (1), flow theory 2% (1), and stimulus-organism-response model (SORM) 2% (1); 12% (6) of the studies focused on model development; 2% (1) of the studies categorized under “others” while employed various frameworks for instance, Cattell’s sixteen personality factor (16PF), and the remaining 44% (22) were carried out without a model or unclear. Therefore, the majority of the past studies were not used apparent models/theories to investigate online consumer behavior, and there is also a contradiction.Figure 7
Model/theory employed by online consumer behavior studies.For example, the studies that apparently used technology acceptance model (TAM) and the theory of planned behavior (TPB): Wu and Song [102], Ha et al. [82], Mengistie and Worku [103], and Teka [104]; technology acceptance model (TAM): Karaveg [105], Changchit et al. [106], and Lai and Wang [107]; unified theory of acceptance and use of technology (UTAUT): Mengesha and Garfield [108], Mäntymäkia and Salo [109], and Escobar-Rodríguez and Carvajal-Trujillo [80]; flow theory: Richard et al. [110]; the theory of reasoned action (TRA): Andrews and Bianchi [111]; the theory of reasoned action (TRA) and theory of planned behavior (TPB): Yusmita et al. [112]; the theory of planned behavior (TPB): Lee and Chen [113] and Moon et al. [9]; the theory of reasoned action (TRA), domain specific innovativeness (DSI), and the theory of planned behavior (TPB): Javadi et al. [114]; unified theory of acceptance and use of technology (UTAUT) and innovation resistance theory (IRT): Soh et al. [115]. Figure 8 describes the sample sources used by online consumer behavior studies, in the existing literature.Figure 8
Sample source of online consumer behavior studies.As can be clearly seen in Figure8, regarding sample sources employed by online consumer behavior studies, therefore, four basic categories of sample sources are used by the studies such as consumers (in general) 60% (30), student consumers 30% (15), staff/employees consumers 4% (2), and user and nonusers 2% (1), and the rest studies, i.e., 4%(2) are categorized as “not applicable”, which conducted in systematic and narrative approaches or while these are review studies. Therefore, the finding revealed that the largest majority of the online consumer studies have “consumers” sample source in general, followed by student consumers in the existing literature. On the contrary, a very insignificant number of studies or only one study considered users and nonusers as a sample source to investigate a company’s online consumer behavior. This study is the one which is conducted by Javadi et al. [114], with a research title “An Analysis of Factors Affecting on Online Shopping Behavior of Consumers,” using two main groups, i.e., consumers who are purchasing and individuals who do not start purchasing company’s products via online. This indicated that there are serious shortages of studies conducted considering users and nonusers type of sample source in extant online consumer behavior studies.Regarding online consumer behavior studies conducted using consumers in general as a sample source, a considerable number of studies have used consumers with various statuses and groups such as age, income, and education levels. For instance, studies conducted using consumers’ age groups: older adults [102, 115], adult individuals [116, 117], young consumers [33, 118]; consumers’ income groups: middle and higher income [119]; based on education levels of consumers: highly educated [110, 111]. As can be understood from the findings, the studies are more focused on and used a sample source of adults/young individuals those highly educated. This shows us there is a lack of studies investigating factors affecting older and illiterate consumers’ online purchase behavior.Figure8 also indicated that not a few studies were conducted using student consumers as a sample source to investigate online consumer behavior, in the existing literature. According to the findings, the studies have used university students studying at undergraduate and graduate levels. To list some of these studies’ authors: Lixandroiu et al. [120], Daroch et al. [2], Ozdemir and Naserinia [121], Tran [122], Changchit et al. [106], Lee and Lee [123], Bucko et al. [124], Kim and Ammeter [69], Moody et al. [125], Rahnamaee and Berger [126], Yusmita et al. [112], Mazaheri et al. [127], Richard [128], and Lee and Chen [113]. This study also revealed that primary and secondary level student consumers are the most neglected groups in the existing online consumer behavior studies. The following section presents descriptions regarding the center of focus of online consumer behavior studies, in the extant literature.Figure9 presents online consumer purchase stages, thus, as can be understood from the result, the online consumer behavior studies were conducted at intention 58% (29), adoption 34% (17), and continuance 8% (4) stages. As the finding indicated that the majority of the existing online consumer behavior studies were carried out to investigate purchase intentions of consumers, followed by studies focused on adoption, which has taken the second largest numbers, and conversely, a very insignificant number of studies carried out to investigate determinants of online consumers repurchase or continuance behavior. For instance, product and service-providing industries/businesses such as education (e.g. [2, 69, 120–122]), banks (e.g. [103]), fashion (e.g. [129]), city administrations (e.g. [130]), and apparel (e.g. [131]) are more emphasized on online purchase intention, according to the shown researchers.Figure 9
Online consumer behavior studies by center of focus.Some of the studies focused on investigating factors affecting online consumer purchase/adoption as follows: banks (e.g. [104, 132]), apparel (e.g. [105]), wood furniture (e.g. [15]), CyberPharma (e.g. [133]), health (e.g. [108]), education (e.g. [106, 123, 124]), and city administrations (e.g. [134]). In contrast, very few studies were conducted on repurchase/continuance intention of online consumers, in the existing studies, for instance, to list some: retailing business [116, 117] and education (e.g. [126]). Therefore, it can be concluded that continuance stage is the most forgotten and underresearched area in online consumer behavior studies in the extant literature.


### 3.2. Determinants of Online Consumer Behavior Using Cheung and Chan Model

As proposed by Cheung and Chan model, extant studies on the potential determinants of online consumer behavior are separated into five major domain areas: individual/consumer characteristics, environmental influences, product/service characteristics, medium characteristics, and online merchant and intermediary characteristics with major online consumer purchase categories such as intention, adoption, and continuance. In this regard, Table2 presented determinants of online consumer behavior from purchase intention perspective.Table 2
Determinants of online consumer behavior (intention).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Trust(ii) Attitude(iii) Experience(iv) Degree of knowledge(v) Degree of participation(vi) Perceived risks(vii) Value(viii) Motivation(ix) Perceived satisfaction(x) Demographic factors(xi) Psychological factors(xii) Perceived enjoyment(xiii) InnovativenessAl-Hattami [127]; Lixandroiu et al. [120]; Moon et al. [9]; Ha et al. [82]; Daroch et al. [2]; Mengistie and Worku [103]; Ozdemir and Naserinia [121]; Soh et al. [115]; Koch et al. [129]; Tran [122]; Chetioui et al. [32]; Cruz-Cárdenas et al. [130]; Hashim et al. [118]; Kim and Ammeter [69]; Duarte et al. [33]; Singh et al. [131]; Adnan [119]; Moody et al. [125]; Mäntymäkia and Salo [109]; Escobar-Rodríguez and Carvajal-Trujillo [80]; Andrews and Bianchi [111]; Lai and Wang [107]; Yeh et al. [86]; D’Alessandro et al. [136]; Mazaheri et al. [127]; Javadi et al. [114]; Richard [128]; Richard et al. [110]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Vulnerability(ii) Cognition of government policy(iii) Subjective norm(iv) Perceived behavioral control(v) Facilitating conditions(vi) Social influence(vii) Cultural values(viii) Image(ix) StructureMoon et al. [9]; Ha et al. [82]; Mengistie and Worku [103]; Ozdemir and Naserinia [121]; Soh et al. [115]; Koch et al. [129]; Cruz-Cárdenas et al. [130]; Hashim et al. [118]; Han et al. [83]; Mäntymäkia and Salo [109]; Escobar-Rodríguez and Carvajal-Trujillo [80]; Andrews and Bianchi [111]; Javadi et al. [114]; Richard et al. [110]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) Product information(ii) Product variety(iii) PriceDaroch et al. [2]; Kim and Ammeter [69]; Escobar-Rodríguez and Carvajal-Trujillo [80]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Perceived usefulness(ii) Confirmation(iii) Security(iv) Compatibility(v) Perceived ease of use(vi) Convenience(vii) Website designAl-Hattami [135]; Lixandroiu et al. [120]; Ha et al. [82]; Daroch et al. [2]; Mengistie and Worku[103]; Ozdemir and Naserinia [121]; Soh et al. [115]; Koch et al. [129]; Tran [122]; Chetioui et al. [32]; Duarte et al. [33]; Singh et al. [131]; Adnan [119]; Mäntymäkia and Salo [109]; Andrews and Bianchi [111]; Lai and Wang [107]; Mazaheri et al. [127]; Javadi et al. [114]; Richard [128]; Richard et al. [110]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Merchandise(ii) Retailer brand(iii) Performance expectation(iv) Service quality(v) Privacy and security(vi) Time riskDaroch et al. [2]; Soh et al. [115]; Tran [122]; Han et al. [83]; Escobar-Rodríguez and Carvajal-Trujillo [80]; Yeh et al. [86]; D’Alessandro et al. [136]; Javadi et al. [114]; Hashim et al. [118]Source: own survey (2022).


#### 3.2.1. Determinants of Online Consumer Behavior (Intention)

As can be seen in Table2, regarding investigating determinants of online consumer behavior from individual/consumer characteristics, accordingly, various factors were addressed by the existing studies such as trust, attitude, experience, degree of knowledge, degree of participation, perceived risks (function, finance, product, and physical), value, motivation, perceived satisfaction, demographic factors (gender), psychological factors (perceived advantage, perceived information asymmetry, and emotions), perceived enjoyment, and innovativeness are factors that affecting online consumer behavior (e.g. [2, 9, 32, 82, 103, 115, 118, 120–122, 129, 130, 135]). For example, according to Al-Hattami [135], trust has a positive impact on consumers’ intention to continue the usage of online shopping. By the same token, the finding of Ha et al. [82] revealed that the shopping intention of online consumers is positively affected by their attitude. Though, there are also consumer characteristic-related factors that do not yet get emphasis in the extant online consumer studies such as consumer lifestyle, flow, and related to demographic factors such as age and income level.Purchase intention of online consumers is also affected by various environmental factors, as can clearly be understood from Table2, factors such as vulnerability, cognition of government policy, subjective norm, perceived behavioral control, facilitating conditions, social influence, cultural values, image, and structure (e.g. [9, 82, 83, 103, 109, 115, 118, 121, 129, 130]). For instance, subjective norms significantly affect online shopping behavior [9, 114]. Similarly, the shopping intention of online consumers is significantly affected by their perception of behavioral control [82]. However, there are shortages of online consumer behavior studies investigating environmental factors such as exposure and attention.From the same table (Table2), factors affecting relation to product/service characteristics, medium characteristics, and merchants and intermediate characteristics, on online consumer behavior, has presented. Product/service characteristics: product information, product variety, and price are factors obtained emphasis by the existing online consumer behavior studies [2, 69, 80]. For example, according to Daroch et al. [2], product information has a significant impact on consumers buying from online sites. However, there are lack of studies in the extant literature on the factors: product knowledge and type, layout, frequency of purchase, tangibility, and differentiation factors on online consumer purchase intention.Medium characteristic-related factors that have obtained focus in the online consumer behavior studies are perceived usefulness, confirmation, security, compatibility, perceived ease of use, convenience, and website design (e.g. [2, 32, 33, 82, 103, 115, 119–122, 129, 131, 135]). According to the findings of Mengistie and Worku [103] and Lai and Wang [107], perceived usefulness and perceived ease of use were significant in affecting customer’s online purchase intention. Likewise, security practices influence a buyer’s perceived risk to purchase gemstones online [136]. On the other hand, navigation, interface, and reliability are factors to those have not yet been investigated in the extant literature.According to Table2, with regard to merchants and intermediate characteristics, merchandise, retailer brand, performance expectation, service quality, privacy and security, and time risk are factors that affect online consumer behavior [2, 80, 83, 86, 114, 115, 118, 122, 136]. For example, security and privacy risk are significant factors that prevent consumers from online apparel products shopping in Ethiopia [131]. However, delivery/logistic, after-sale service, and incentive are factors that shortages of studies seriously observed in the existing online consumer behavior literature.


#### 3.2.2. Determinants of Online Consumer Behavior (Adoption)

Table3 presented determinants of online consumer behavior from adoption stage, accordingly, individual characteristic-related factors that affect consumers’ online purchases are the following: orientation, awareness, experience, demographic (age, gender, occupation, education, and income), attitude, perceived risk, perceived value, perceived enjoyment, and concentration factors (e.g. [15, 104–106, 134, 137–139]). According to the research findings of Gu et al. [137], the impact of consumer awareness and experience has increased in affecting online consumer buying behavior. Similarly, the findings of the study [134] revealed that online shopping among selected respondents is strongly influenced by the demographic profile of the purchaser which included factors like age, gender, education, occupation, and income. And also, the same result was obtained regarding perceived risk, and it has a significant effect on online consumer purchases [104]. Nonetheless, individual factors such as lifestyle, motivation, knowledge, innovativeness, involvement, flow, and satisfaction factors and their impact on online consumer purchases have not yet been investigated in the existing studies.Table 3
Determinants of online consumer behavior (adoption).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Orientation(ii) Awareness(iii) Experience(iv) Demographic factors(v) Attitude(vi) Perceived risk(vii) Perceived value(viii) Perceived enjoyment(ix) ConcentrationHuyen et al. [138]; Karaveg [105]; Gu et al. [137]; Barcic et al. [15]; Teka [104]; Changchit et al. [106]; Jindal et al. [134]; Ling and Yazdanifard [139]; Tate [140]; Zhang et al. [141]; Lee and Chen [113]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Perceived behavioral control(ii) Subjective norms(iii) Facilitating conditions(iv) Social influence(v) HabitTeka [104]; Mengesha and Garfield[108]; Bucko et al. [124]; Tate [140]; Zhang et al. [141]; Yusmita et al. [112]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) Product involvement(ii) Price(iii) Scarcity(iv) Product detailsKaraveg [105]; Bucko et al. [124]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Feasibility and readiness(ii) Usefulness(iii) Ease of use(iv) Security(v) Constancy(vi) Adaptiveness(vii) Introversion(viii) Compatibility(ix) Web design qualitySingh and Sahu[132]; Karaveg [105]; Gu et al. [137]; Guthrie et al. [133]; Teka [104]; Mengesha and Garfield [108]; Changchit et al. [106]; Lee and Lee [123]; Bucko et al. [124]; Yusmita et al. [112]; Lee and Chen [113]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Performance expectancy(ii) Effort expectancy(iii) Perceived quality(iv) Time pressure(v) Perceived delivery(vi) Perceived after-sale(vii) Perceived privacyKaraveg [105]; Mengesha and Garfield[108]; Tate [140]; Zhang et al. [141]; Lee and Chen [113]Source: own survey (2022).The same table (Table3) also described environmental and product-related factors affecting online consumer purchase in the extant literature. In this regard, environmental factors that affect online purchase are perceived behavioral control, subjective norms, facilitating conditions, social influence, and habit (e.g. [104, 108, 124, 140]). As Teka [104] revealed that perceived behavioral control as well as subjective norms have a significant positive impact on users’ e-banking usage practice in Ethiopia. Exposure, attention, and image are factors that shortages of studies clearly observed in the extant online consumer behavior literature. Besides, product involvement, price, scarcity, and product details are identified as a product characteristic factors that influence online consumer purchase [105, 124]. For instance, the factor of price explained the largest part and is especially important for university students which critically affect their online purchase in the Slovak Republic, according to Bucko et al. [124]. Conversely, product type, layout, frequency of purchase, tangibility, and differentiation are product characteristics factors not yet getting emphasis in the existing online consumer studies on their impact on online consumers.Feasibility and readiness, usefulness, ease of use, security, constancy, adaptiveness, introversion, compatibility, and web design quality are medium characteristics factors affecting online consumer purchase [104–106, 108, 132, 133, 137], as indicated in Table 3. A study by Karaveg [105] revealed that usefulness and ease of use factors have positively significant influences on consumers online purchasing behavior, especially those who have apparel product experience and who enjoy shopping process, in apparel industry, in Thailand. In this respect, our results support the usefulness of the multiperspective react-cope-adapt framework of constrained consumer behavior in an online environment [133].Regarding merchants and intermediate characteristic-related factors, Table3 shows that performance expectancy, effort expectancy, perceived quality risk, time pressure, perceived delivery risk, perceived after-sale risk, and perceived privacy risk are factors affecting online consumer purchase, in the existing literature [105, 108, 113, 140, 141]. According to the findings of Zhang et al. [141], for instance, perceived quality risk, perceived time risk, perceived delivery risk, and perceived after-sale risk affect significantly customers’ purchasing behavior in the overall process of B2C in China. Nevertheless, brand reputation and incentive factors are not yet apparently investigated with regard to their impacts by the extant online consumer behavior studies.


#### 3.2.3. Determinants of Online Consumer Behavior (Continuance)

Table4 portrays factors that affect online consumer repurchase/continuance behavior in particular from consumers’ characteristics and environmental influence perspectives. Consumer characteristic factors affecting online repurchase behavior of consumers are attitudes, adults’ perceptions, customer satisfaction, customer trust, and perceived value (e.g. [116, 117]). For example, according to the study by Pham et al. [116], perceived value significantly influences repurchase intention of online consumers. However, there are significant number of possible factors that have not yet been obtained, especially, focus on the existing online consumer behavior studies, for instance, consumer lifestyle, motivation, knowledge, innovativeness, involvement, demographic factors (e.g. gender, occupation, education, and income), flow, and experience factors.Table 4
Determinants of online consumer behavior (continuance).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Attitudes(ii) Adults’ perceptions(iii) Customer satisfaction(iv) Customer trust(v) Perceived valueWu and Song [102]; Rita et al. [117]; Pham et al. [116]; Rahnamaee and Berger [126]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Perceived social isolation(ii) Subjective norms(iii) Perceived behavioral controlWu and Song [102]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) PriceRahnamaee and Berger [122]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Perceived usefulness(ii) Perceived ease of use(iii) Website design(iv) Convenience(v) Website security(vi) Website privacyWu and Song [102]; Rita et al. [117]; Pham et al. [116]; Rahnamaee and Berger [126]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Perceived lack of shopping mobility(ii) Service quality(iii) Brand prestigeWu and Song [102]; Rita et al. [117]; Rahnamaee and Berger [126]Source: own survey (2022).Besides, perceived social isolation, subjective norms, and perceived behavioral control are environmental factors influence online consumers repurchase [102]; and the finding of Wu and Song [102] revealed that these factors significantly impacting consumers’ intentions to continue online shopping. In contrast, environmental factors such as exposure, attention, and image are factors not yet received attention from researchers and scholars to investigate their impact on online consumer repurchase. Moreover, price factor is the only factor addressed by the existing studies—its impact on consumers’ online repurchases under product/service characteristic-related factors, and as per Rahnamaee and Berger [126] finding indicated that, price has a significant impact on intention to repurchase. In this regard, product/service characteristic factors that are most neglected or not apparently investigated, to list some: product knowledge, product type, layout, frequency of purchase, tangibility, and differentiation factors.The same table (Table4) shows factors investigated in relation to medium characteristics and merchants and intermediate characteristics. Medium characteristic factors influencing consumer online continuance/repurchase are perceived usefulness, perceived ease of use, website design, convenience (access, search, evaluation, transaction, and possession/postpurchase), and website security and privacy (e.g. [102, 108]). And as stated by Pham et al. [116], for example, perceived usefulness and perceived ease of use factors are significantly influencing consumers’ online continuance. Interface, reliability, and navigation factors are not investigated sufficiently in the extant literature.Moreover, perceived lack of shopping mobility, customer service quality, and brand prestige are merchants and intermediate characteristics-related factors influencing consumer online continuance (e.g. [117, 126]), and Rita et al. [117] indicated that e-service quality significantly influencing consumers online repurchase intention. However, there is a shortage of studies in relation to privacy and security control, delivery/logistic, after-sale service, and incentive factors in the extant literature. In a general format, very limited studies were conducted on consumers’ online repurchase intention in comparison to purchase intention and adoption stages.


#### 3.2.4. The Most Influential Factors of Online Consumer Behavior

According to Figure10, the most influential factors affecting online consumer behavior in the extant online consumer behavior studies are presented. Accordingly, the top nineteen factors significantly affecting online consumers are identified based on their level of influence on consumers’ intention, adoption, and repurchase in the following manner: perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience. For example, perceived usefulness and ease of use are factors influencing consumers’ internet shopping [142–144].Figure 10
The most influential factors of online consumer behavior.


## 3.1. General Results

In advance, addressing the specific objective, some general issues are presented and discussed in the following sections such as online consumer behavior studies by a list of published journals, nature of industries, sector, country and region (where online consumer behavior studies were conducted), methods of data analysis, research design, theory/model employed, sample source, and also, discusses online consumer behavior studies based on the center of focus. The following Table1 presents online consumer behavior studies by a list of publishing journals.Table 1
List of online consumer behavior studies published journals.
Journal nameNumberPercentageAsia Pacific Journal of Marketing and Logistics (Emerald)24Cogent business & management24International Journal of Marketing Studies24Journal of Business Research (Elsevier)24Journal of Computer Information Systems24Journal of Retailing and Consumer Services (Elsevier)24Journal of Theoretical and Applied Electronic Commerce Research24Sustainability (MDPI)24Others3468Total50100Source: own survey (2022).From 50 eligible studies found in 50 articles in 42 journals, the majority of journals had published just one (N=34) or two or more (N=8) articles. Table 1 shows the most prolific journals with at least two published online consumer behavior studies. The most prolific journals are Asia Pacific Journal of Marketing and Logistics (Emerald), Cogent Business and Management, International Journal of Marketing Studies, Journal of Business Research (Elsevier), Journal of Computer Information Systems, Journal of Retailing and Consumer Services (Elsevier), Journal of Theoretical and Applied Electronic Commerce Research, and Sustainability (MDPI) published two articles, respectively. Figure 3 presents online consumer behavior studies by nature of the organization.Figure 3
Online consumer behavior studies by nature of organization.According to Figure3, almost equivalent share of online consumer behavior studies conducted in public, private, and mixed (i.e., studies conducted using both natures of organizations such as publicly based organizations and private ones together) natures; accordingly, their share out of the total studies are presented as 38% (19), 32% (16), and 28% (14) for public, private, and mixed, and the remaining 2% (1) is not applicable studies, respectively. Therefore, as can be understood from the result above, there are still shortages of studies conducted at the global level by taking into account both natures of organizations (public and private) together in the extant studies, which predominantly emphasized online consumer behavior, while currently almost equal emphasis is being given by researchers and scholars carrying out studies that having either public or private nature in the extant literature. This situation may result in a problem to identify determining factors affecting online consumer behavior that are equally applicable to all organizations. The next section presents online consumer behavior studies by sector.Figure4 presented online consumer behavior studies sector-wise; accordingly, a large number of studies 50% (25) were conducted in the service sector, and online consumer behavior studies were done by taking into consideration both sectors, i.e., service and manufacturing, covering the second-largest number that is 34% (17) and manufacturing 14% (7), and the remaining 2% (1) is not applicable to either of the case. From the studies conducted in the service sector, education takes the leading position of 52% (13) particularly studies focused on tertiary level, i.e., university, and the next place is owned by other services such as bank, finance and insurance, hotel, health, and administration cities. Therefore, the finding indicated that there is a lack of online consumer behavior studies in some sectors like the manufacturing sector, and also the available studies conducted on the sector are only limited to apparel, fashion, pharmaceutical, and wood furniture industries. Similarly, there is also a shortage of online consumer behavior studies on primary and secondary level education. Figure 5 presents online consumer behavior studies by country of origin.Figure 4
Online consumer behavior studies by sector.Figure 5
Online consumer behavior studies by country.Figure5 described about online consumer behavior studies by their countries of origin, therefore, the finding revealed that online consumer behavior is a concept with a global reach from all corners. However, there is a concentration of online consumer behavior studies in specific countries, for instance, USA, which have the biggest share in the extant literature. Based on region, North America, Asia, and the Middle East are taking the leading positions in the number of online consumer behavior studies. On the other hand, online consumer behavior studies are less represented in some content, for example, Africa. This can expose a trend that raises questions about the aspirations of generalizable knowledge on online consumer behavior. Therefore, from the finding obtained, African countries are the place where a shortage of online consumer behavior studies is exclusively seen. And also, a number of online consumer behavior studies are insufficiently conducted across countries or by considering two or more countries or conducted at a multinational level, the existing studies. Figure 6 portrays data analysis methods employed by online consumer behavior studies.Figure 6
Data analysis methods employed by online consumer behavior studies.As can be understood from Figure6, regarding data analysis methods used, 58% (29) of the online consumer behavior-focused studies were analyzed using structural equation modeling (SEM), 14% (7) factor analysis (FA), 10% (5) descriptive statistics (such as frequency, mean, median, and standard deviation), 8% (4) mixed (data analysis methods used particularly to qualitative and quantitative data analysis methods, e.g., FDG, interview, SEM, MLRM, and correlation test together), 6% (3) of the studies were analyzed using multiple linear regression model (MLRM), and very limited number of the studies were used in correlation (Pearson’s correlation test) and ANOVA tests, which cover 2% (1) and 2% (1), respectively, of the selected studies. Therefore, this shows that almost more than half of the studies focused on online consumer behavior employed inferential statistics. Similarly, 92% of online consumer behavior studies used a cross-sectional research design and were self-reported. Furthermore, a large majority of online consumer behavior studies are surveys which accounts for 84%, and the rest 8% goes for studies conducted using a mixed approach. The following section describes the model/theory employed by online consumer behavior studies.Figure7 has presented model/theory employed by online consumer behavior studies; accordingly, only 42% of the studies were conducted using clear and conventional model/theory such as expectation-confirmation model (ECM) with task-technology fit (TTF) model and the trust factor (ECM, TTF, and TF) 2% (1), technology acceptance model and the theory of planned behavior 10% (5), technology acceptance model (TAM) 6% (3), the theory of planned behavior (TPB) and the protection motivation theory (PMT) 2% (1), unified theory of acceptance and use of technology (UTAUT) and innovation resistance theory (IRT) 2% (1), unified theory of acceptance and use of technology (UTAUT) 6% (3), theory of planned behavior (TPB) 4% (2), theory of reasoned action (TRA) 2% (1), theory of reasoned action (TRA) and theory of planned behavior (TPB) 2% (1), flow theory 2% (1), and stimulus-organism-response model (SORM) 2% (1); 12% (6) of the studies focused on model development; 2% (1) of the studies categorized under “others” while employed various frameworks for instance, Cattell’s sixteen personality factor (16PF), and the remaining 44% (22) were carried out without a model or unclear. Therefore, the majority of the past studies were not used apparent models/theories to investigate online consumer behavior, and there is also a contradiction.Figure 7
Model/theory employed by online consumer behavior studies.For example, the studies that apparently used technology acceptance model (TAM) and the theory of planned behavior (TPB): Wu and Song [102], Ha et al. [82], Mengistie and Worku [103], and Teka [104]; technology acceptance model (TAM): Karaveg [105], Changchit et al. [106], and Lai and Wang [107]; unified theory of acceptance and use of technology (UTAUT): Mengesha and Garfield [108], Mäntymäkia and Salo [109], and Escobar-Rodríguez and Carvajal-Trujillo [80]; flow theory: Richard et al. [110]; the theory of reasoned action (TRA): Andrews and Bianchi [111]; the theory of reasoned action (TRA) and theory of planned behavior (TPB): Yusmita et al. [112]; the theory of planned behavior (TPB): Lee and Chen [113] and Moon et al. [9]; the theory of reasoned action (TRA), domain specific innovativeness (DSI), and the theory of planned behavior (TPB): Javadi et al. [114]; unified theory of acceptance and use of technology (UTAUT) and innovation resistance theory (IRT): Soh et al. [115]. Figure 8 describes the sample sources used by online consumer behavior studies, in the existing literature.Figure 8
Sample source of online consumer behavior studies.As can be clearly seen in Figure8, regarding sample sources employed by online consumer behavior studies, therefore, four basic categories of sample sources are used by the studies such as consumers (in general) 60% (30), student consumers 30% (15), staff/employees consumers 4% (2), and user and nonusers 2% (1), and the rest studies, i.e., 4%(2) are categorized as “not applicable”, which conducted in systematic and narrative approaches or while these are review studies. Therefore, the finding revealed that the largest majority of the online consumer studies have “consumers” sample source in general, followed by student consumers in the existing literature. On the contrary, a very insignificant number of studies or only one study considered users and nonusers as a sample source to investigate a company’s online consumer behavior. This study is the one which is conducted by Javadi et al. [114], with a research title “An Analysis of Factors Affecting on Online Shopping Behavior of Consumers,” using two main groups, i.e., consumers who are purchasing and individuals who do not start purchasing company’s products via online. This indicated that there are serious shortages of studies conducted considering users and nonusers type of sample source in extant online consumer behavior studies.Regarding online consumer behavior studies conducted using consumers in general as a sample source, a considerable number of studies have used consumers with various statuses and groups such as age, income, and education levels. For instance, studies conducted using consumers’ age groups: older adults [102, 115], adult individuals [116, 117], young consumers [33, 118]; consumers’ income groups: middle and higher income [119]; based on education levels of consumers: highly educated [110, 111]. As can be understood from the findings, the studies are more focused on and used a sample source of adults/young individuals those highly educated. This shows us there is a lack of studies investigating factors affecting older and illiterate consumers’ online purchase behavior.Figure8 also indicated that not a few studies were conducted using student consumers as a sample source to investigate online consumer behavior, in the existing literature. According to the findings, the studies have used university students studying at undergraduate and graduate levels. To list some of these studies’ authors: Lixandroiu et al. [120], Daroch et al. [2], Ozdemir and Naserinia [121], Tran [122], Changchit et al. [106], Lee and Lee [123], Bucko et al. [124], Kim and Ammeter [69], Moody et al. [125], Rahnamaee and Berger [126], Yusmita et al. [112], Mazaheri et al. [127], Richard [128], and Lee and Chen [113]. This study also revealed that primary and secondary level student consumers are the most neglected groups in the existing online consumer behavior studies. The following section presents descriptions regarding the center of focus of online consumer behavior studies, in the extant literature.Figure9 presents online consumer purchase stages, thus, as can be understood from the result, the online consumer behavior studies were conducted at intention 58% (29), adoption 34% (17), and continuance 8% (4) stages. As the finding indicated that the majority of the existing online consumer behavior studies were carried out to investigate purchase intentions of consumers, followed by studies focused on adoption, which has taken the second largest numbers, and conversely, a very insignificant number of studies carried out to investigate determinants of online consumers repurchase or continuance behavior. For instance, product and service-providing industries/businesses such as education (e.g. [2, 69, 120–122]), banks (e.g. [103]), fashion (e.g. [129]), city administrations (e.g. [130]), and apparel (e.g. [131]) are more emphasized on online purchase intention, according to the shown researchers.Figure 9
Online consumer behavior studies by center of focus.Some of the studies focused on investigating factors affecting online consumer purchase/adoption as follows: banks (e.g. [104, 132]), apparel (e.g. [105]), wood furniture (e.g. [15]), CyberPharma (e.g. [133]), health (e.g. [108]), education (e.g. [106, 123, 124]), and city administrations (e.g. [134]). In contrast, very few studies were conducted on repurchase/continuance intention of online consumers, in the existing studies, for instance, to list some: retailing business [116, 117] and education (e.g. [126]). Therefore, it can be concluded that continuance stage is the most forgotten and underresearched area in online consumer behavior studies in the extant literature.


## 3.2. Determinants of Online Consumer Behavior Using Cheung and Chan Model

As proposed by Cheung and Chan model, extant studies on the potential determinants of online consumer behavior are separated into five major domain areas: individual/consumer characteristics, environmental influences, product/service characteristics, medium characteristics, and online merchant and intermediary characteristics with major online consumer purchase categories such as intention, adoption, and continuance. In this regard, Table2 presented determinants of online consumer behavior from purchase intention perspective.Table 2
Determinants of online consumer behavior (intention).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Trust(ii) Attitude(iii) Experience(iv) Degree of knowledge(v) Degree of participation(vi) Perceived risks(vii) Value(viii) Motivation(ix) Perceived satisfaction(x) Demographic factors(xi) Psychological factors(xii) Perceived enjoyment(xiii) InnovativenessAl-Hattami [127]; Lixandroiu et al. [120]; Moon et al. [9]; Ha et al. [82]; Daroch et al. [2]; Mengistie and Worku [103]; Ozdemir and Naserinia [121]; Soh et al. [115]; Koch et al. [129]; Tran [122]; Chetioui et al. [32]; Cruz-Cárdenas et al. [130]; Hashim et al. [118]; Kim and Ammeter [69]; Duarte et al. [33]; Singh et al. [131]; Adnan [119]; Moody et al. [125]; Mäntymäkia and Salo [109]; Escobar-Rodríguez and Carvajal-Trujillo [80]; Andrews and Bianchi [111]; Lai and Wang [107]; Yeh et al. [86]; D’Alessandro et al. [136]; Mazaheri et al. [127]; Javadi et al. [114]; Richard [128]; Richard et al. [110]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Vulnerability(ii) Cognition of government policy(iii) Subjective norm(iv) Perceived behavioral control(v) Facilitating conditions(vi) Social influence(vii) Cultural values(viii) Image(ix) StructureMoon et al. [9]; Ha et al. [82]; Mengistie and Worku [103]; Ozdemir and Naserinia [121]; Soh et al. [115]; Koch et al. [129]; Cruz-Cárdenas et al. [130]; Hashim et al. [118]; Han et al. [83]; Mäntymäkia and Salo [109]; Escobar-Rodríguez and Carvajal-Trujillo [80]; Andrews and Bianchi [111]; Javadi et al. [114]; Richard et al. [110]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) Product information(ii) Product variety(iii) PriceDaroch et al. [2]; Kim and Ammeter [69]; Escobar-Rodríguez and Carvajal-Trujillo [80]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Perceived usefulness(ii) Confirmation(iii) Security(iv) Compatibility(v) Perceived ease of use(vi) Convenience(vii) Website designAl-Hattami [135]; Lixandroiu et al. [120]; Ha et al. [82]; Daroch et al. [2]; Mengistie and Worku[103]; Ozdemir and Naserinia [121]; Soh et al. [115]; Koch et al. [129]; Tran [122]; Chetioui et al. [32]; Duarte et al. [33]; Singh et al. [131]; Adnan [119]; Mäntymäkia and Salo [109]; Andrews and Bianchi [111]; Lai and Wang [107]; Mazaheri et al. [127]; Javadi et al. [114]; Richard [128]; Richard et al. [110]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Merchandise(ii) Retailer brand(iii) Performance expectation(iv) Service quality(v) Privacy and security(vi) Time riskDaroch et al. [2]; Soh et al. [115]; Tran [122]; Han et al. [83]; Escobar-Rodríguez and Carvajal-Trujillo [80]; Yeh et al. [86]; D’Alessandro et al. [136]; Javadi et al. [114]; Hashim et al. [118]Source: own survey (2022).


### 3.2.1. Determinants of Online Consumer Behavior (Intention)

As can be seen in Table2, regarding investigating determinants of online consumer behavior from individual/consumer characteristics, accordingly, various factors were addressed by the existing studies such as trust, attitude, experience, degree of knowledge, degree of participation, perceived risks (function, finance, product, and physical), value, motivation, perceived satisfaction, demographic factors (gender), psychological factors (perceived advantage, perceived information asymmetry, and emotions), perceived enjoyment, and innovativeness are factors that affecting online consumer behavior (e.g. [2, 9, 32, 82, 103, 115, 118, 120–122, 129, 130, 135]). For example, according to Al-Hattami [135], trust has a positive impact on consumers’ intention to continue the usage of online shopping. By the same token, the finding of Ha et al. [82] revealed that the shopping intention of online consumers is positively affected by their attitude. Though, there are also consumer characteristic-related factors that do not yet get emphasis in the extant online consumer studies such as consumer lifestyle, flow, and related to demographic factors such as age and income level.Purchase intention of online consumers is also affected by various environmental factors, as can clearly be understood from Table2, factors such as vulnerability, cognition of government policy, subjective norm, perceived behavioral control, facilitating conditions, social influence, cultural values, image, and structure (e.g. [9, 82, 83, 103, 109, 115, 118, 121, 129, 130]). For instance, subjective norms significantly affect online shopping behavior [9, 114]. Similarly, the shopping intention of online consumers is significantly affected by their perception of behavioral control [82]. However, there are shortages of online consumer behavior studies investigating environmental factors such as exposure and attention.From the same table (Table2), factors affecting relation to product/service characteristics, medium characteristics, and merchants and intermediate characteristics, on online consumer behavior, has presented. Product/service characteristics: product information, product variety, and price are factors obtained emphasis by the existing online consumer behavior studies [2, 69, 80]. For example, according to Daroch et al. [2], product information has a significant impact on consumers buying from online sites. However, there are lack of studies in the extant literature on the factors: product knowledge and type, layout, frequency of purchase, tangibility, and differentiation factors on online consumer purchase intention.Medium characteristic-related factors that have obtained focus in the online consumer behavior studies are perceived usefulness, confirmation, security, compatibility, perceived ease of use, convenience, and website design (e.g. [2, 32, 33, 82, 103, 115, 119–122, 129, 131, 135]). According to the findings of Mengistie and Worku [103] and Lai and Wang [107], perceived usefulness and perceived ease of use were significant in affecting customer’s online purchase intention. Likewise, security practices influence a buyer’s perceived risk to purchase gemstones online [136]. On the other hand, navigation, interface, and reliability are factors to those have not yet been investigated in the extant literature.According to Table2, with regard to merchants and intermediate characteristics, merchandise, retailer brand, performance expectation, service quality, privacy and security, and time risk are factors that affect online consumer behavior [2, 80, 83, 86, 114, 115, 118, 122, 136]. For example, security and privacy risk are significant factors that prevent consumers from online apparel products shopping in Ethiopia [131]. However, delivery/logistic, after-sale service, and incentive are factors that shortages of studies seriously observed in the existing online consumer behavior literature.


### 3.2.2. Determinants of Online Consumer Behavior (Adoption)

Table3 presented determinants of online consumer behavior from adoption stage, accordingly, individual characteristic-related factors that affect consumers’ online purchases are the following: orientation, awareness, experience, demographic (age, gender, occupation, education, and income), attitude, perceived risk, perceived value, perceived enjoyment, and concentration factors (e.g. [15, 104–106, 134, 137–139]). According to the research findings of Gu et al. [137], the impact of consumer awareness and experience has increased in affecting online consumer buying behavior. Similarly, the findings of the study [134] revealed that online shopping among selected respondents is strongly influenced by the demographic profile of the purchaser which included factors like age, gender, education, occupation, and income. And also, the same result was obtained regarding perceived risk, and it has a significant effect on online consumer purchases [104]. Nonetheless, individual factors such as lifestyle, motivation, knowledge, innovativeness, involvement, flow, and satisfaction factors and their impact on online consumer purchases have not yet been investigated in the existing studies.Table 3
Determinants of online consumer behavior (adoption).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Orientation(ii) Awareness(iii) Experience(iv) Demographic factors(v) Attitude(vi) Perceived risk(vii) Perceived value(viii) Perceived enjoyment(ix) ConcentrationHuyen et al. [138]; Karaveg [105]; Gu et al. [137]; Barcic et al. [15]; Teka [104]; Changchit et al. [106]; Jindal et al. [134]; Ling and Yazdanifard [139]; Tate [140]; Zhang et al. [141]; Lee and Chen [113]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Perceived behavioral control(ii) Subjective norms(iii) Facilitating conditions(iv) Social influence(v) HabitTeka [104]; Mengesha and Garfield[108]; Bucko et al. [124]; Tate [140]; Zhang et al. [141]; Yusmita et al. [112]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) Product involvement(ii) Price(iii) Scarcity(iv) Product detailsKaraveg [105]; Bucko et al. [124]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Feasibility and readiness(ii) Usefulness(iii) Ease of use(iv) Security(v) Constancy(vi) Adaptiveness(vii) Introversion(viii) Compatibility(ix) Web design qualitySingh and Sahu[132]; Karaveg [105]; Gu et al. [137]; Guthrie et al. [133]; Teka [104]; Mengesha and Garfield [108]; Changchit et al. [106]; Lee and Lee [123]; Bucko et al. [124]; Yusmita et al. [112]; Lee and Chen [113]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Performance expectancy(ii) Effort expectancy(iii) Perceived quality(iv) Time pressure(v) Perceived delivery(vi) Perceived after-sale(vii) Perceived privacyKaraveg [105]; Mengesha and Garfield[108]; Tate [140]; Zhang et al. [141]; Lee and Chen [113]Source: own survey (2022).The same table (Table3) also described environmental and product-related factors affecting online consumer purchase in the extant literature. In this regard, environmental factors that affect online purchase are perceived behavioral control, subjective norms, facilitating conditions, social influence, and habit (e.g. [104, 108, 124, 140]). As Teka [104] revealed that perceived behavioral control as well as subjective norms have a significant positive impact on users’ e-banking usage practice in Ethiopia. Exposure, attention, and image are factors that shortages of studies clearly observed in the extant online consumer behavior literature. Besides, product involvement, price, scarcity, and product details are identified as a product characteristic factors that influence online consumer purchase [105, 124]. For instance, the factor of price explained the largest part and is especially important for university students which critically affect their online purchase in the Slovak Republic, according to Bucko et al. [124]. Conversely, product type, layout, frequency of purchase, tangibility, and differentiation are product characteristics factors not yet getting emphasis in the existing online consumer studies on their impact on online consumers.Feasibility and readiness, usefulness, ease of use, security, constancy, adaptiveness, introversion, compatibility, and web design quality are medium characteristics factors affecting online consumer purchase [104–106, 108, 132, 133, 137], as indicated in Table 3. A study by Karaveg [105] revealed that usefulness and ease of use factors have positively significant influences on consumers online purchasing behavior, especially those who have apparel product experience and who enjoy shopping process, in apparel industry, in Thailand. In this respect, our results support the usefulness of the multiperspective react-cope-adapt framework of constrained consumer behavior in an online environment [133].Regarding merchants and intermediate characteristic-related factors, Table3 shows that performance expectancy, effort expectancy, perceived quality risk, time pressure, perceived delivery risk, perceived after-sale risk, and perceived privacy risk are factors affecting online consumer purchase, in the existing literature [105, 108, 113, 140, 141]. According to the findings of Zhang et al. [141], for instance, perceived quality risk, perceived time risk, perceived delivery risk, and perceived after-sale risk affect significantly customers’ purchasing behavior in the overall process of B2C in China. Nevertheless, brand reputation and incentive factors are not yet apparently investigated with regard to their impacts by the extant online consumer behavior studies.


### 3.2.3. Determinants of Online Consumer Behavior (Continuance)

Table4 portrays factors that affect online consumer repurchase/continuance behavior in particular from consumers’ characteristics and environmental influence perspectives. Consumer characteristic factors affecting online repurchase behavior of consumers are attitudes, adults’ perceptions, customer satisfaction, customer trust, and perceived value (e.g. [116, 117]). For example, according to the study by Pham et al. [116], perceived value significantly influences repurchase intention of online consumers. However, there are significant number of possible factors that have not yet been obtained, especially, focus on the existing online consumer behavior studies, for instance, consumer lifestyle, motivation, knowledge, innovativeness, involvement, demographic factors (e.g. gender, occupation, education, and income), flow, and experience factors.Table 4
Determinants of online consumer behavior (continuance).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Attitudes(ii) Adults’ perceptions(iii) Customer satisfaction(iv) Customer trust(v) Perceived valueWu and Song [102]; Rita et al. [117]; Pham et al. [116]; Rahnamaee and Berger [126]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Perceived social isolation(ii) Subjective norms(iii) Perceived behavioral controlWu and Song [102]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) PriceRahnamaee and Berger [122]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Perceived usefulness(ii) Perceived ease of use(iii) Website design(iv) Convenience(v) Website security(vi) Website privacyWu and Song [102]; Rita et al. [117]; Pham et al. [116]; Rahnamaee and Berger [126]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Perceived lack of shopping mobility(ii) Service quality(iii) Brand prestigeWu and Song [102]; Rita et al. [117]; Rahnamaee and Berger [126]Source: own survey (2022).Besides, perceived social isolation, subjective norms, and perceived behavioral control are environmental factors influence online consumers repurchase [102]; and the finding of Wu and Song [102] revealed that these factors significantly impacting consumers’ intentions to continue online shopping. In contrast, environmental factors such as exposure, attention, and image are factors not yet received attention from researchers and scholars to investigate their impact on online consumer repurchase. Moreover, price factor is the only factor addressed by the existing studies—its impact on consumers’ online repurchases under product/service characteristic-related factors, and as per Rahnamaee and Berger [126] finding indicated that, price has a significant impact on intention to repurchase. In this regard, product/service characteristic factors that are most neglected or not apparently investigated, to list some: product knowledge, product type, layout, frequency of purchase, tangibility, and differentiation factors.The same table (Table4) shows factors investigated in relation to medium characteristics and merchants and intermediate characteristics. Medium characteristic factors influencing consumer online continuance/repurchase are perceived usefulness, perceived ease of use, website design, convenience (access, search, evaluation, transaction, and possession/postpurchase), and website security and privacy (e.g. [102, 108]). And as stated by Pham et al. [116], for example, perceived usefulness and perceived ease of use factors are significantly influencing consumers’ online continuance. Interface, reliability, and navigation factors are not investigated sufficiently in the extant literature.Moreover, perceived lack of shopping mobility, customer service quality, and brand prestige are merchants and intermediate characteristics-related factors influencing consumer online continuance (e.g. [117, 126]), and Rita et al. [117] indicated that e-service quality significantly influencing consumers online repurchase intention. However, there is a shortage of studies in relation to privacy and security control, delivery/logistic, after-sale service, and incentive factors in the extant literature. In a general format, very limited studies were conducted on consumers’ online repurchase intention in comparison to purchase intention and adoption stages.


### 3.2.4. The Most Influential Factors of Online Consumer Behavior

According to Figure10, the most influential factors affecting online consumer behavior in the extant online consumer behavior studies are presented. Accordingly, the top nineteen factors significantly affecting online consumers are identified based on their level of influence on consumers’ intention, adoption, and repurchase in the following manner: perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience. For example, perceived usefulness and ease of use are factors influencing consumers’ internet shopping [142–144].Figure 10
The most influential factors of online consumer behavior.


## 3.2.1. Determinants of Online Consumer Behavior (Intention)

As can be seen in Table2, regarding investigating determinants of online consumer behavior from individual/consumer characteristics, accordingly, various factors were addressed by the existing studies such as trust, attitude, experience, degree of knowledge, degree of participation, perceived risks (function, finance, product, and physical), value, motivation, perceived satisfaction, demographic factors (gender), psychological factors (perceived advantage, perceived information asymmetry, and emotions), perceived enjoyment, and innovativeness are factors that affecting online consumer behavior (e.g. [2, 9, 32, 82, 103, 115, 118, 120–122, 129, 130, 135]). For example, according to Al-Hattami [135], trust has a positive impact on consumers’ intention to continue the usage of online shopping. By the same token, the finding of Ha et al. [82] revealed that the shopping intention of online consumers is positively affected by their attitude. Though, there are also consumer characteristic-related factors that do not yet get emphasis in the extant online consumer studies such as consumer lifestyle, flow, and related to demographic factors such as age and income level.Purchase intention of online consumers is also affected by various environmental factors, as can clearly be understood from Table2, factors such as vulnerability, cognition of government policy, subjective norm, perceived behavioral control, facilitating conditions, social influence, cultural values, image, and structure (e.g. [9, 82, 83, 103, 109, 115, 118, 121, 129, 130]). For instance, subjective norms significantly affect online shopping behavior [9, 114]. Similarly, the shopping intention of online consumers is significantly affected by their perception of behavioral control [82]. However, there are shortages of online consumer behavior studies investigating environmental factors such as exposure and attention.From the same table (Table2), factors affecting relation to product/service characteristics, medium characteristics, and merchants and intermediate characteristics, on online consumer behavior, has presented. Product/service characteristics: product information, product variety, and price are factors obtained emphasis by the existing online consumer behavior studies [2, 69, 80]. For example, according to Daroch et al. [2], product information has a significant impact on consumers buying from online sites. However, there are lack of studies in the extant literature on the factors: product knowledge and type, layout, frequency of purchase, tangibility, and differentiation factors on online consumer purchase intention.Medium characteristic-related factors that have obtained focus in the online consumer behavior studies are perceived usefulness, confirmation, security, compatibility, perceived ease of use, convenience, and website design (e.g. [2, 32, 33, 82, 103, 115, 119–122, 129, 131, 135]). According to the findings of Mengistie and Worku [103] and Lai and Wang [107], perceived usefulness and perceived ease of use were significant in affecting customer’s online purchase intention. Likewise, security practices influence a buyer’s perceived risk to purchase gemstones online [136]. On the other hand, navigation, interface, and reliability are factors to those have not yet been investigated in the extant literature.According to Table2, with regard to merchants and intermediate characteristics, merchandise, retailer brand, performance expectation, service quality, privacy and security, and time risk are factors that affect online consumer behavior [2, 80, 83, 86, 114, 115, 118, 122, 136]. For example, security and privacy risk are significant factors that prevent consumers from online apparel products shopping in Ethiopia [131]. However, delivery/logistic, after-sale service, and incentive are factors that shortages of studies seriously observed in the existing online consumer behavior literature.


## 3.2.2. Determinants of Online Consumer Behavior (Adoption)

Table3 presented determinants of online consumer behavior from adoption stage, accordingly, individual characteristic-related factors that affect consumers’ online purchases are the following: orientation, awareness, experience, demographic (age, gender, occupation, education, and income), attitude, perceived risk, perceived value, perceived enjoyment, and concentration factors (e.g. [15, 104–106, 134, 137–139]). According to the research findings of Gu et al. [137], the impact of consumer awareness and experience has increased in affecting online consumer buying behavior. Similarly, the findings of the study [134] revealed that online shopping among selected respondents is strongly influenced by the demographic profile of the purchaser which included factors like age, gender, education, occupation, and income. And also, the same result was obtained regarding perceived risk, and it has a significant effect on online consumer purchases [104]. Nonetheless, individual factors such as lifestyle, motivation, knowledge, innovativeness, involvement, flow, and satisfaction factors and their impact on online consumer purchases have not yet been investigated in the existing studies.Table 3
Determinants of online consumer behavior (adoption).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Orientation(ii) Awareness(iii) Experience(iv) Demographic factors(v) Attitude(vi) Perceived risk(vii) Perceived value(viii) Perceived enjoyment(ix) ConcentrationHuyen et al. [138]; Karaveg [105]; Gu et al. [137]; Barcic et al. [15]; Teka [104]; Changchit et al. [106]; Jindal et al. [134]; Ling and Yazdanifard [139]; Tate [140]; Zhang et al. [141]; Lee and Chen [113]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Perceived behavioral control(ii) Subjective norms(iii) Facilitating conditions(iv) Social influence(v) HabitTeka [104]; Mengesha and Garfield[108]; Bucko et al. [124]; Tate [140]; Zhang et al. [141]; Yusmita et al. [112]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) Product involvement(ii) Price(iii) Scarcity(iv) Product detailsKaraveg [105]; Bucko et al. [124]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Feasibility and readiness(ii) Usefulness(iii) Ease of use(iv) Security(v) Constancy(vi) Adaptiveness(vii) Introversion(viii) Compatibility(ix) Web design qualitySingh and Sahu[132]; Karaveg [105]; Gu et al. [137]; Guthrie et al. [133]; Teka [104]; Mengesha and Garfield [108]; Changchit et al. [106]; Lee and Lee [123]; Bucko et al. [124]; Yusmita et al. [112]; Lee and Chen [113]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Performance expectancy(ii) Effort expectancy(iii) Perceived quality(iv) Time pressure(v) Perceived delivery(vi) Perceived after-sale(vii) Perceived privacyKaraveg [105]; Mengesha and Garfield[108]; Tate [140]; Zhang et al. [141]; Lee and Chen [113]Source: own survey (2022).The same table (Table3) also described environmental and product-related factors affecting online consumer purchase in the extant literature. In this regard, environmental factors that affect online purchase are perceived behavioral control, subjective norms, facilitating conditions, social influence, and habit (e.g. [104, 108, 124, 140]). As Teka [104] revealed that perceived behavioral control as well as subjective norms have a significant positive impact on users’ e-banking usage practice in Ethiopia. Exposure, attention, and image are factors that shortages of studies clearly observed in the extant online consumer behavior literature. Besides, product involvement, price, scarcity, and product details are identified as a product characteristic factors that influence online consumer purchase [105, 124]. For instance, the factor of price explained the largest part and is especially important for university students which critically affect their online purchase in the Slovak Republic, according to Bucko et al. [124]. Conversely, product type, layout, frequency of purchase, tangibility, and differentiation are product characteristics factors not yet getting emphasis in the existing online consumer studies on their impact on online consumers.Feasibility and readiness, usefulness, ease of use, security, constancy, adaptiveness, introversion, compatibility, and web design quality are medium characteristics factors affecting online consumer purchase [104–106, 108, 132, 133, 137], as indicated in Table 3. A study by Karaveg [105] revealed that usefulness and ease of use factors have positively significant influences on consumers online purchasing behavior, especially those who have apparel product experience and who enjoy shopping process, in apparel industry, in Thailand. In this respect, our results support the usefulness of the multiperspective react-cope-adapt framework of constrained consumer behavior in an online environment [133].Regarding merchants and intermediate characteristic-related factors, Table3 shows that performance expectancy, effort expectancy, perceived quality risk, time pressure, perceived delivery risk, perceived after-sale risk, and perceived privacy risk are factors affecting online consumer purchase, in the existing literature [105, 108, 113, 140, 141]. According to the findings of Zhang et al. [141], for instance, perceived quality risk, perceived time risk, perceived delivery risk, and perceived after-sale risk affect significantly customers’ purchasing behavior in the overall process of B2C in China. Nevertheless, brand reputation and incentive factors are not yet apparently investigated with regard to their impacts by the extant online consumer behavior studies.


## 3.2.3. Determinants of Online Consumer Behavior (Continuance)

Table4 portrays factors that affect online consumer repurchase/continuance behavior in particular from consumers’ characteristics and environmental influence perspectives. Consumer characteristic factors affecting online repurchase behavior of consumers are attitudes, adults’ perceptions, customer satisfaction, customer trust, and perceived value (e.g. [116, 117]). For example, according to the study by Pham et al. [116], perceived value significantly influences repurchase intention of online consumers. However, there are significant number of possible factors that have not yet been obtained, especially, focus on the existing online consumer behavior studies, for instance, consumer lifestyle, motivation, knowledge, innovativeness, involvement, demographic factors (e.g. gender, occupation, education, and income), flow, and experience factors.Table 4
Determinants of online consumer behavior (continuance).
Domain areaDescriptionKey factorsStudiesIndividual/consumer characteristicsReferring to internal individual factors and behavioral characteristics(i) Attitudes(ii) Adults’ perceptions(iii) Customer satisfaction(iv) Customer trust(v) Perceived valueWu and Song [102]; Rita et al. [117]; Pham et al. [116]; Rahnamaee and Berger [126]Environmental influencesReferring to the structural influences, including market-related issues (uncertainty, competition, and concentration) and national and international issues (legal structure, trade restrictions, and culture)(i) Perceived social isolation(ii) Subjective norms(iii) Perceived behavioral controlWu and Song [102]Product/service characteristicsReferring to knowledge about the product, product type, frequency of purchase, tangibility, and product quality(i) PriceRahnamaee and Berger [122]Medium characteristicsReferring to both traditional IS attributes (ease of use, quality, security, and reliability) and web-specific factors (navigation, interface, and network speed)(i) Perceived usefulness(ii) Perceived ease of use(iii) Website design(iv) Convenience(v) Website security(vi) Website privacyWu and Song [102]; Rita et al. [117]; Pham et al. [116]; Rahnamaee and Berger [126]Merchants and intermediate characteristicsReferring to the key attributes/features of the online stores(i) Perceived lack of shopping mobility(ii) Service quality(iii) Brand prestigeWu and Song [102]; Rita et al. [117]; Rahnamaee and Berger [126]Source: own survey (2022).Besides, perceived social isolation, subjective norms, and perceived behavioral control are environmental factors influence online consumers repurchase [102]; and the finding of Wu and Song [102] revealed that these factors significantly impacting consumers’ intentions to continue online shopping. In contrast, environmental factors such as exposure, attention, and image are factors not yet received attention from researchers and scholars to investigate their impact on online consumer repurchase. Moreover, price factor is the only factor addressed by the existing studies—its impact on consumers’ online repurchases under product/service characteristic-related factors, and as per Rahnamaee and Berger [126] finding indicated that, price has a significant impact on intention to repurchase. In this regard, product/service characteristic factors that are most neglected or not apparently investigated, to list some: product knowledge, product type, layout, frequency of purchase, tangibility, and differentiation factors.The same table (Table4) shows factors investigated in relation to medium characteristics and merchants and intermediate characteristics. Medium characteristic factors influencing consumer online continuance/repurchase are perceived usefulness, perceived ease of use, website design, convenience (access, search, evaluation, transaction, and possession/postpurchase), and website security and privacy (e.g. [102, 108]). And as stated by Pham et al. [116], for example, perceived usefulness and perceived ease of use factors are significantly influencing consumers’ online continuance. Interface, reliability, and navigation factors are not investigated sufficiently in the extant literature.Moreover, perceived lack of shopping mobility, customer service quality, and brand prestige are merchants and intermediate characteristics-related factors influencing consumer online continuance (e.g. [117, 126]), and Rita et al. [117] indicated that e-service quality significantly influencing consumers online repurchase intention. However, there is a shortage of studies in relation to privacy and security control, delivery/logistic, after-sale service, and incentive factors in the extant literature. In a general format, very limited studies were conducted on consumers’ online repurchase intention in comparison to purchase intention and adoption stages.


## 3.2.4. The Most Influential Factors of Online Consumer Behavior

According to Figure10, the most influential factors affecting online consumer behavior in the extant online consumer behavior studies are presented. Accordingly, the top nineteen factors significantly affecting online consumers are identified based on their level of influence on consumers’ intention, adoption, and repurchase in the following manner: perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience. For example, perceived usefulness and ease of use are factors influencing consumers’ internet shopping [142–144].Figure 10
The most influential factors of online consumer behavior.


## 4. Conclusion and Recommendation

### 4.1. Conclusion

Based on the findings generated, the following conclusions were drawn. First, existing online consumer behavior studies are addressed based on the nature of the organization and sector-wise. Accordingly, there are shortages of studies conducted considering both public and private organizations and manufacturing sector, even the existing manufacturing-focused studies are only limited to apparel, fashion, pharmaceutical, and wood furniture industries. Similarly, there is also a shortage of online consumer behavior studies on primary and secondary level education. Regarding the reachability of the studies, African countries are the place where a shortage of online consumer behavior studies is exclusively seen. And also, a number of online consumer behavior studies are insufficient regarding studies conducted across countries or by considering two or more countries or conducted at a multinational level in the existing studies.Second, with regard to research methods employed, there is a lack of studies conducted using a longitudinal design and qualitative and mixed approaches. And also, the existing studies were not used apparent models/theories to investigate online consumer behavior, and there is also a contradiction. The finding indicated that there are serious shortages of studies carried out considering users and nonusers type of sample source in extant online consumer behavior studies. Even consumers selected for the studies were not inclusive of all age groups and education levels, for instance, older and illiterate consumers. Moreover, out of the student consumers’ focused studies, none of them considered primary and secondary level student consumers.Third, as the finding of this study revealed that continuance/repurchase stage is the most forgotten and underresearched area in online consumer behavior studies, in the existing literature, conversely, the high priory is given for investigating purchase intentions of consumers, followed by studies focused on adoption. There are also shortages of studies on factors such as consumer lifestyle, flow, innovativeness, demographic factors (e.g., age and income), exposure, attention, product type, layout, frequency of purchase, tangibility, differentiation, brand reputation, and incentive in the extant online consumer behavior literature.Lastly, the study finding has identified the most influential factors of online consumer behavior: perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience.


### 4.2. Recommendation

The following recommendations were drawn as per the conclusions. It is suggested that future researchers and scholars conduct their studies on organizations mixed of public and private natures while there is a shortage of studies in this area, particularly on online consumer behavior, which helps to identify determining factors affecting online consumer behavior that are equally applicable to all organizations. Also, future studies recommended basing their focus on manufacturing and service (particularly at primary and secondary level education) sectors, as these areas are not yet sufficiently researched. In addition to this, regarding the reachability of online consumer behavior studies, there is a shortage of studies, especially in African countries, therefore, future research advised to give emphasis to these countries to investigate factors affecting consumers’ online purchase specific to Africa, as well as investigating and experiencing across countries within the continent.Methodologically, extant online consumer behavior studies were conducted more in quantitative approach and cross-sectional design, which is primarily using structural equation modeling (SEM) analysis method. However, according to Picincu [145], a qualitative research approach can provide better insights into the underlying reasons and motivations of online customers’ behaviors than the quantitative approach via using focus groups and interviews. Thus, future research is recommended to be carried out with more of a qualitative approach with a longitudinal design. Likewise, as clearly shown in the finding, the existing studies used consumers in general without having groups as a sample source such as based on consumers’ online purchase experience and demographic characteristics like age, gender, education, and income level of consumers. For instance, as Jiang et al. [146] revealed younger consumers may more likely to purchase online than older consumers. Therefore, future researchers and scholars are advised to use different groups of consumers such as user and nonusers groups and also based on demographic characteristics (e.g., age, gender, education, and income level) of consumers to generate very convincing findings regarding determinants of online consumer behavior.Regarding the center of focus of the studies, the finding indicated that a large majority of the studies were conducted at purchase intention and adoption stages, conversely, a shortage of study observed at continuance stage, therefore, scholars and researchers suggested conducting their future studies on repurchase intention of online consumers. In addition, it is advised future studies focus on the impacts of consumer lifestyle, flow, innovativeness, demographic factors (e.g., age and income), exposure, attention, product type, layout, frequency of purchase, tangibility, differentiation, brand reputation, and incentive factors on consumers’ online purchase.Moreover, this study has identified the most influencing factors of online consumer behavior which are perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience. In this regards, industries require developing a clear plan to work on information communication technology to join or sustain in the online business in this hyperdynamic digitalized world, to cope these factors. And also, generate loyal consumers by providing trusted, secure, and convenient online services to consumers, which enhance industries to be competent in the online business world. Therefore, those industries shopping online or the newcomers advised working on the above-identified factors to build loyal consumers for their products or services.


## 4.1. Conclusion

Based on the findings generated, the following conclusions were drawn. First, existing online consumer behavior studies are addressed based on the nature of the organization and sector-wise. Accordingly, there are shortages of studies conducted considering both public and private organizations and manufacturing sector, even the existing manufacturing-focused studies are only limited to apparel, fashion, pharmaceutical, and wood furniture industries. Similarly, there is also a shortage of online consumer behavior studies on primary and secondary level education. Regarding the reachability of the studies, African countries are the place where a shortage of online consumer behavior studies is exclusively seen. And also, a number of online consumer behavior studies are insufficient regarding studies conducted across countries or by considering two or more countries or conducted at a multinational level in the existing studies.Second, with regard to research methods employed, there is a lack of studies conducted using a longitudinal design and qualitative and mixed approaches. And also, the existing studies were not used apparent models/theories to investigate online consumer behavior, and there is also a contradiction. The finding indicated that there are serious shortages of studies carried out considering users and nonusers type of sample source in extant online consumer behavior studies. Even consumers selected for the studies were not inclusive of all age groups and education levels, for instance, older and illiterate consumers. Moreover, out of the student consumers’ focused studies, none of them considered primary and secondary level student consumers.Third, as the finding of this study revealed that continuance/repurchase stage is the most forgotten and underresearched area in online consumer behavior studies, in the existing literature, conversely, the high priory is given for investigating purchase intentions of consumers, followed by studies focused on adoption. There are also shortages of studies on factors such as consumer lifestyle, flow, innovativeness, demographic factors (e.g., age and income), exposure, attention, product type, layout, frequency of purchase, tangibility, differentiation, brand reputation, and incentive in the extant online consumer behavior literature.Lastly, the study finding has identified the most influential factors of online consumer behavior: perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience.


## 4.2. Recommendation

The following recommendations were drawn as per the conclusions. It is suggested that future researchers and scholars conduct their studies on organizations mixed of public and private natures while there is a shortage of studies in this area, particularly on online consumer behavior, which helps to identify determining factors affecting online consumer behavior that are equally applicable to all organizations. Also, future studies recommended basing their focus on manufacturing and service (particularly at primary and secondary level education) sectors, as these areas are not yet sufficiently researched. In addition to this, regarding the reachability of online consumer behavior studies, there is a shortage of studies, especially in African countries, therefore, future research advised to give emphasis to these countries to investigate factors affecting consumers’ online purchase specific to Africa, as well as investigating and experiencing across countries within the continent.Methodologically, extant online consumer behavior studies were conducted more in quantitative approach and cross-sectional design, which is primarily using structural equation modeling (SEM) analysis method. However, according to Picincu [145], a qualitative research approach can provide better insights into the underlying reasons and motivations of online customers’ behaviors than the quantitative approach via using focus groups and interviews. Thus, future research is recommended to be carried out with more of a qualitative approach with a longitudinal design. Likewise, as clearly shown in the finding, the existing studies used consumers in general without having groups as a sample source such as based on consumers’ online purchase experience and demographic characteristics like age, gender, education, and income level of consumers. For instance, as Jiang et al. [146] revealed younger consumers may more likely to purchase online than older consumers. Therefore, future researchers and scholars are advised to use different groups of consumers such as user and nonusers groups and also based on demographic characteristics (e.g., age, gender, education, and income level) of consumers to generate very convincing findings regarding determinants of online consumer behavior.Regarding the center of focus of the studies, the finding indicated that a large majority of the studies were conducted at purchase intention and adoption stages, conversely, a shortage of study observed at continuance stage, therefore, scholars and researchers suggested conducting their future studies on repurchase intention of online consumers. In addition, it is advised future studies focus on the impacts of consumer lifestyle, flow, innovativeness, demographic factors (e.g., age and income), exposure, attention, product type, layout, frequency of purchase, tangibility, differentiation, brand reputation, and incentive factors on consumers’ online purchase.Moreover, this study has identified the most influencing factors of online consumer behavior which are perceived usefulness, perceived risk, attitude, perceived ease of use, trust, social influence, subjective norms, perceived enjoyment, security, perceived behavioral control, web design quality, privacy and security concerns, demographic factors (e.g., age, gender, occupation, education, and income), perceived value, service quality, perceived satisfaction, psychological factors (e.g., relative advantage), facilitating conditions, and consumers’ experience. In this regards, industries require developing a clear plan to work on information communication technology to join or sustain in the online business in this hyperdynamic digitalized world, to cope these factors. And also, generate loyal consumers by providing trusted, secure, and convenient online services to consumers, which enhance industries to be competent in the online business world. Therefore, those industries shopping online or the newcomers advised working on the above-identified factors to build loyal consumers for their products or services.



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*Source: 1298378-2022-11-28.xml*