online shopping apps research paper

ORIGINAL RESEARCH article

Factors affecting consumers’ online choice intention: a study based on bayesian network.

• 1 Key Laboratory of Electronic Commerce and Modern Logistics, Chongqing University of Posts and Telecommunications, Chongqing, China
• 2 School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, China
• 3 78111 Troops, People’s Liberation Army of China, Chengdu, China

In China, the mature development of online retail channels provides consumers with multiple consumption choices, and the factors that affect whether consumers choose to search or purchase online are numerous and complex. In this context, this paper reports on experimental research regarding consumers’ willingness to choose channels based on the two-stage decision-making theory. Using structural equation modeling, the factors influencing consumers’ online search intention and purchase willingness and the relationship between them are studied. In particular, the perceived benefits, channel trust, and channel transfer costs are explored. Furthermore, a Bayesian network is used in order to analyze the degree of influence of each factor quantitatively. It is found that online trust is an important factor affecting consumers’ online search intention, and the most important factor for consumers’ online purchase intention is their perceived benefits of online shopping. At the same time, there is a positive relationship between online search intention and purchase intention. This study can provide management decision support for online retail enterprises and help to promote the healthy development of online shopping.

Introduction

The recent emergence of multiple retail channels has made consumers’ choice of shopping channels more complex, causing consumers to rethink their choice of shopping channels. This phenomenon has attracted the attention of scholars, who have examined product pricing and channel choice willingness. Many scholars have analyzed channel choice willingness from the theoretical perspective of consumer perception, as consumer behavior is motivated by consumers’ psychological assessment of the results that will be achieved by the specific attributes of products or services, such as perceived benefit ( Khan et al., 2015 ), perceived value ( Zhao and Chen, 2021 ), and perceived usefulness ( Wang et al., 2021 ). However, the above-mentioned researches are one-sided, because they only analyze the choice of shopping channels from the perspective of consumer perception. Trust is the attitude and cognition of consumer toward shopping channels. It is believed by some scholars that perceived benefits are based on trust toward the shopping channels ( Costa e Silva et al., 2020 ). Both online and offline channels boast their own advantages, and consumers can choose different channels at different purchasing stages. But current researches can hardly clarify the complexity on studying consumer channel choice in a theoretical way.

Consumer channel choice is the study of consumer behavior with unique features, because it is based on analyzing real problems and giving choices. The early researches are dominated by theoretical analysis. For example, Huang et al. (2016) analyzed the impact exerted by the emergence of mobile retail channels on online consumption behavior. Based on qualitative research, some scholars try to adopt statistical methods and models to study consumer shopping channels, such as using correlation analysis to analyze factors influencing consumers in online shopping decision-making ( Elida et al., 2019 ). However, the relationship between variables cannot be well explained and the latent variable measurement error remains unresolved by the mentioned methods. With the development of statistical theory, some more rigorous and sound statistical techniques and model analysis methods have been introduced into the research on factors affecting online shopping, such as research on impulsive consumption in online retail ( Gupta and Shukla, 2019 ) and the influence of brand experience on consumer behavior ( Chen-ran, 2020 ). Most of the above researches are carried out around the structural equation model (simply called SEM). SEM is a multivariable statistical analysis method for testing the hypothetical relationships between observed variables and latent variables and among latent variables. It has good processing ability in proving the authenticity of hypotheses ( Akbarzadeh et al., 2019 ). In the behavior research of online consumer, latent variables, such as cognition, attitude, behavior, and willingness, are often unmeasurable, which need to be represented by observed variables. By combining the characteristics of the online consumer behavior, the SEM pre-selects several factors that affect the consumption behavior, sets up the relevant observation and latent variables, and builds the path analysis model. In the research, it is positive to observe the multiple relationships between different variables by considering the significance, coefficient, and mediation or moderating effects to determine the variables correlation. However, building the entire path analysis framework relies on subjective assumptions and judgments. Setting different paths will correspondingly produce different results, therefore, and it is difficult to ensure its stability. Such research is the confirmatory research and highly related to research hypotheses, which greatly limits how this method is applied in investigating online consumer behavior. At the same time, the SEM lacks the ability to predict and diagnose the relationship between variables ( Song and Lee, 2008 ). A Bayesian network is a statistical method for expressing the causality between variables and the relationship between prediction and diagnosis variables ( Chickering, 2002 ). Due to its good prediction and diagnosis ability, it can be used to accurately analyze consumers’ purchasing behavior ( Song et al., 2013 ). However, it lacks the empirical ability of examining variable relationships ( Song et al., 2011 ). Therefore, this paper proposes to combine SEN and Bayesian network, which not only adopts SEM in the empirical research to fit non-standard models, but also uses Bayesian network to make diagnosis and prediction. Based on two-stage decision-making theory, we take into account the online channel searching and purchasing intention of consumers in this paper and accurately analyze the factors that affect how consumers make choices online and their complex relationships. It can provide references for online retail companies to formulate reasonable marketing strategies.

Theory and Hypotheses

Two-stage decision-making theory.

Once consumers generate a shopping desire, searching for information and buying products are the most two important stages of their shopping decision-making process. Haubl and Trifts (2000) presented a two-stage decision-making theory based on the study of consumer shopping behavior. In the search stage, consumers search for a large amount of relevant information about the product. In the purchase stage, they make an in-depth comparison and evaluation of the options, and then, they make the final purchase decision. Two-stage decision-making theory has been applied by many scholars in the choice of consumption channels. Schneider and Zielke (2020) used two-stage decision-making theory to study the consumer Showrooming behavior. Meanwhile, Balladares et al. (2016) studied the factors that affect consumers in the information search stage based on two-stage decision-making theory. Singh and Jang (2020) studied the impact of consumer’s perception on choosing searching and purchasing channels and the satisfaction.

Due to the coexistence of online and offline retail channels, consumers have more choices in purchasing channels, and channel choice willingness is the main factor for measuring consumers’ channel choice behavior, because consumers have different channel selection behaviors when they are in different purchase decision-making stages. Hence, we can get four consumers’ channel choice models: search online–purchase online, search online–purchase offline, search offline–purchase online, and search offline-purchase offline. Based on this, this paper discusses the factors that affect consumers’ online search and purchase intention and the complex relationship between these intentions based on two-stage decision-making theory.

Bayesian Network

A Bayesian network shows the relationship between latent variables in the form of a causality graph, which is composed of a network structure S and parameter set θ . The network structure S is used to represent the independent and conditional independent relationship between the sets of classified random variable x ={ x 1 , x 2 ,…, x n }, and the network structure S is composed of nodes and directed arcs, which is a directed acyclic graph. The parent node of the node x i is represented by pa i , and the value set of the parent node is represented by the value set of the parent node: p a i = p a i 1 , p a i 2 , … , p a i r p a i . The parameter set θ is the local probability corresponding to each variable, and it is the conditional probability set under a given parent node. The parameter set of the variable X i is as follows: θ x i = P x i 1 | p a i j , P x i 2 | p a i j , … , P x i r i | p a i j . j = 1 , 2 , … , r p a i . Figure 1 shows the Bayesian network structure.

Figure 1 . Bayesian network structure.

There are many Bayesian network algorithms. The TAN (Tree-Augmented-Naive) Bayesian network proposed by Friedman et al. relaxes the application conditions of the classic Bayesian network and allows complex correlations between variables. TAN Bayesian networks are trained by constantly training the sample sets to find the best parameters S , θ , which is also the analysis method used in this article. TAN Bayes is an extension of the classic Bayesian network model. It can handle variables that have correlations and have good predictive power for high-dimensional data. The basic idea of the TAN Bayesian network is to use the Bayesian network to express the dependency relationship and to connect the relationship between attribute variables with a directed arc from the parent node to the child node. TAN Bayesian networks are widely used in data mining in the fields of computer, business and communication.

The Bayesian model involves the causal prediction and inference of the observed variables, while the SEM involves empirical analysis of the path relationship of the latent variables. Therefore, the key to combining the SEM and Bayesian network is to obtain the sample data of each node of the Bayesian network through the observation variables to make predictions and diagnostic analysis. The main design ideas of this paper are as follows:

First, we identify the factors influencing consumers’ online choice, as shown in Table 1 , collect data through a questionnaire survey, and then construct the relationship between the observed variables of the SEM.

Table 1 . List of variables, measure items, and literature sources.

Second, based on the SEM, we lay the foundation for the construction of the Bayesian network by calculating the score of each latent variable in the SEM.

Third, based on the relationship between latent variables in the SEM and the score of latent variables, in order to draw better research conclusions, the Bayesian network is used to further predict and diagnose the relationship between variables.

Search Intention

Searching is an important part of consumers’ purchase decision-making stage; the more abundant product information consumers have, the more likely they are to make satisfactory purchase decisions, but their willingness to engage in the information search is limited by the cost of the channel search ( Tien and Kiureghian, 2016 ). From the perspective of utility maximization, consumers will choose the lowest-cost way to search for product information. Compared with completing purchases through multiple channels, consumers will spend less in a channel to search for information and buy the products. It is indicated by Singh and Swait (2017) that online channels provide greater searching or purchasing benefits. Ngwe et al. (2019) found that guiding consumers to search for products will increase the overall expected purchasing probability of sold products. Zhai et al. (2019) showed that the channel searching and purchasing behavior of consumers can influence each other.

Trust transfer theory is widely used in the study of consumer behavior when multiple retail channels coexist. Scholars divide trust transfer into intra-channel and inter-channel trust transfer ( Stewart and Qin, 2013 ). Intra-channel trust transfer refers to consumers’ trust transfer between different shopping stages in the same channel (online or offline; Lee et al., 2011 ). Path dependence theory points out that once economic, social, or technological systems enter a certain path, for better or worse, they will constantly strengthen themselves under the action of inertia ( Thietart, 2015 ). In other words, people’s past choices determine their possible choices now. When consumers enter the online retail environment, the impact of search willingness on purchase intention is also a manifestation of path dependence. Based on the theories of trust transfer and path dependence within the channel, consumers’ willingness to search in one channel affects their willingness to buy in the same channel. Therefore, the first hypothesis is proposed:

H1 : There is a positive relationship between online search intention and purchase intention, and purchase intention can have a reverse impact on search intention.

Perceived Benefit

Both online and offline channels have the functions of information search and product sales ( Balasubramanian et al., 2010 ). However, because different channels have different characteristics, consumers have different perceived benefits of product selection, product quality, service quality, and so on, which will affect their choice of channels. According to Lee et al. (2018) , perceived benefit is the customers’ evaluation of the overall utility of using a certain channel based on their own needs, which has a direct impact on their purchase decisions. Due to the particularity of online channels, consumers cannot personally experience the utility of products when shopping through such channels. When consumers make an evaluation, one of the most direct factors to consider is the benefits that the channels can bring; the greater the perceived benefits, the stronger the consumers’ willingness to buy the products ( Martin et al., 2015 ). van der Lans et al. (2016) pointed out that perceived benefits are most important in determining purchase intentions. The perceived benefits of channels not only affect consumers’ willingness to purchase but also attract consumers’ willingness to search. Based on the above analyses, the degree of consumers’ perceived benefits of retail channels reflect their willingness to choose search information or purchase products. Building on this discussion, the study suggests the following hypotheses:

H2 : There is a positive relationship between perceived benefits and online search intention, and search willingness can have a reverse impact on perceived interests. H3 : There is a positive relationship between perceived benefits and online purchase intention, and purchase intention can have a reverse impact on channel trust.

Channel Trust

Many scholars have proved that trust is one of the main factors affecting consumers’ intention to purchase, especially when they cannot touch the transaction object as in the online retail environment, consumers will rely on trust to reduce the uncertainty of their purchase decisions, hence increasing the probability of interaction between consumers and retail channels. Channel trust is a reliable way for consumers to search for information. Consumers will trust the channel more because of its reliability and the high-quality information it provides. Trust will increase consumers’ goodwill toward businesses and reduce their perceived risks ( Zhao et al., 2017 ). In a study of consumer behavior, Martin et al. (2015) found that consumer trust has a positive impact on channel choice intention. Reimer and Benkenstein (2016) studied the impact of the credibility of other online consumers’ comments on consumers’ channel choices. They found that the higher the consumers’ trust in the channel, the more likely they are to think that online reviews are more credible. Hajli (2015) argued that consumers’ trust and purchase willingness are affected significantly by online retailers’ ratings and comments, recommendations and introductions, and forums and communities. King et al. (2014) showed that consumers’ trust for a certain brand or product significantly affects their purchase willingness. Hence, the following hypotheses are presented:

H4 : There is a positive relationship between channel trust and online search intention, and search intention can have a reverse impact on channel trust. H5 : There is a positive relationship between channel trust and online purchase intention, and purchase intention can have a reverse impact on channel trust.

Switching Cost

In the multi-channel retail environment, consumers’ consumption behaviors are different online and offline, and the switching cost is the additional cost that consumers must pay for switching services. It includes the economic, psychological, and even emotional cognitive costs of stopping the use of current services and changing to new ones. The switching cost is the multi-channel consumers’ perception of the time and energy spent on the conversion between offline and online channels, and it is a part of their assessment of the total shopping cost. Some scholars ( Anderson and Simester, 2013 ; Stan et al., 2013 ) have shown that the switching cost has a significant impact on the choice of consumer information search channel and purchase channel in the multi-retail channel environment. Specifically, the higher the switching cost, the less easy it is for consumers to make cross-channel purchases. It is discovered by the research of Chang et al. (2017) that the switching cost prevents free-riding behavior. In this paper, the switching cost is set as the perceived cost caused by consumers transferring from the online channel to the offline channel. Building on this discussion, the next set hypotheses are stated as follows:

H6 : There is a positive relationship between switching cost and online search intention, and search intention can have a reverse impact on switching cost. H7 : There is a positive relationship between switching cost and online purchase intention, and purchase intention can have a reverse impact on switching cost.

Based on the above assumptions, the conceptual model is shown in Figure 2 .

Figure 2 . Conceptual model of consumers’ intention to choose channels.

Methods and Results

Data collection and sample.

A five-level Likert scale is used, with options of “very much agree,” “agree,” “generally agree,” “disagree,” and “very much disagree,” corresponding to values of 5, 4, 3, 2, and 1. The higher the degree of identity, the higher the score.

Before the formal survey, we conducted a pre-survey on the questionnaire with college students who have online shopping experience (100 students in total) and revised the questionnaire based on the suggestions made by them and experts. In order to ensure the randomness of the collected data, questionnaires are distributed through Internet after revised, targeting at consumers in China who have both online and offline shopping experience.

It takes a week to collect questionnaires. A total of 591 questionnaires are collected in total, 30 of which are invalid and thus excluded, reasons for invalidity included as: (1) answer time is not normal (e.g., answer time less than 30s), (2) have missing data on their questionnaires (e.g., the question “compared with offline channels, other consumers’ evaluation of the product is trustworthy” is not answered), and (3) have no obvious regular answers (e.g., choosing the same option for 10 or more successive questions). Finally, 561 questionnaires were actually processed, and the validity rate was 94.92%. There were more female participants (60.1%) than male participants (39.9%), including students (23.5%), office workers (7.0%), clerks (56.1%), and others (13.4%), and the possible explanation for imbalanced sex ratio is that women are more interested and enthusiastic in online shopping. Overall, 89.1% of the respondents were aged between 20 and 39years, and most were highly educated, including graduate (20.1%), undergraduate (65.2%), college degree (10.2%), and high school (4.5%). More detailed characteristics of the sample are shown in Table 2 .

Table 2 . Sample characteristics ( n =561).

Reliability and Validity Test

We evaluated the reliability and internal consistency of the measure with SPSS 23.0. Cronbach’s alpha was calculated for the construct and ranged from 0.654 and 0.789, indicating that the reliability of each variable of the scale is acceptable and can be analyzed later. The reliability analysis results are shown as Table 3 . In order to test the validity of the measured data, SPSS 23.0 was used to conduct an exploratory factor analysis. Principal component factor analysis of the data was carried out using the maximum variance method, and the results showed that the overall KMO (Kaiser-Meyer-Olkin) value of the factor analysis was 0.827 and the significance was 0.000, indicating that the data were suitable for factor analysis. According to the principle that the eigenvalue was greater than 1, five principal components were extracted, and the factor load of each measurement item was greater than 0.5, indicating that the measurement items of the unified construction variables were loaded on the same factor, and the scale had good convergence validity.

Table 3 . Reliability analysis.

In order to verify the scientific rationality of the model, it is necessary to test whether each fitting index meets the fitting standard. Take PB, OT, SC, SI, and PI as endogenous variables, and use Amos 23.0 to build the SEM shown as Figure 3 . As shown in Table 4 , the results of the fitting indexes in this study show a GFI (goodness of fit index) of model-fit of 0.969, CFI (comparative fit index) of 0.977, RMR (root mean square residual) of 0.029, X2/DF of 1.103, AGFI (adjust goodness of fit index) of 0.955, and RESEA (root mean square error of approximation) of 0.014. The fitting indexes meet the acceptance standard level.

Figure 3 . Path coefficient diagram of structural equation model.

Table 4 . Index table of main fitting effects of SEM.

Data Analysis With SEM

With the help of Amos 23.0, we used the maximum likelihood estimation method to verify the hypotheses proposed in this paper. It can be seen from the SEM that the influence of the variables is obvious. We can see that: (1) The path coefficients of the perceived benefit on online search intention and online purchase intention are 0.16 and 0.49, respectively, indicating that perceived benefit has a positive impact on search intention and purchase intention, and from the path coefficient, we can see that the perceived benefit has a greater influence on the purchase intention. (2) The path coefficients of channel trust on online search intention and purchase intention are 0.38 and 0.26, respectively, indicating that channel trust has a positive impact on search intention and purchase intention. (3) The path coefficients of switching cost on online search intention and purchase intention are 0.16 and 0.21, respectively, indicating that switching cost has a positive effect on search intention and purchase intention, and the influence on purchase intention is slightly greater than that on search intention. (4) The path coefficient of search intention on purchase intention is 0.26, indicating that search intention will also have a positive impact on purchase intention. Therefore, H1, H2, H3, H4, H5, H6, and H7 are supported.

Data Analysis With Bayesian Network

The average score of each latent variable was analyzed by K-means cluster analysis with SPSS 23.0. In order to reduce the complexity of the operation and increase the identifiability of the judgment results, each latent variable was clustered into three states: high, medium, and low. Before clustering, in order to ensure the quality of the data, the box diagram of the sample data was drawn to deal with abnormal values, and the “minimum and maximum” abnormal data far away from the whole were eliminated; hence, a total of 42 outliers were removed, and 519 valid data were analyzed. In this study, an SPSS analysis of variance (ANOVA) was used to verify the differences in the latent variables in each dimension and to verify the significance of the classification to the dimension scores. The specific results are shown in Table 5 . The ANOVA results show that it is reasonable to cluster sample data into high, medium, and low dimensions.

Table 5 . Analysis of variance.

We used SPSS modeler 18.0 to construct the TAN Bayesian network based on clustering data with the maximum likelihood method, as shown in Figure 4 . We can see that purchase intention is the parent node of transfer cost, perceived benefit, search intention, and channel trust, indicating that purchase intention is affected by these four latent variables from the constructed Bayesian network structure. In addition, online search intention is the parent node of transfer cost, perceived benefit, and channel trust, indicating that online search intention is also affected by these three variables. The influence of online search intention on online purchase intention depends not only on itself but also on the perceived benefits of online purchase, channel trust, and channel transfer cost.

Figure 4 . Bayesian network model of consumers’ willingness to choose channels.

Bayesian Prediction

According to the constructed Bayesian network, the prediction of online search intention and purchase intention in different states can be obtained from switching costs, channel trust, and perceived benefit, as shown in Table 6 . As can be seen from the tables, with the changes in switching costs, channel trust, and perceived benefit along the high-medium-low (simply called H-M-L), online search and purchase intentions change positively. Due to the low search cost of online channels, consumers tend to search for product information online after generating a shopping demand. Table 6A shows that the higher the perceived cost caused by the transfer of online search to offline purchase, the stronger consumers’ intention to choose purchase, indicating that the switching costs plays a positive role in the locking of online channels. From Table 6B , we can see that the state of “high” search intention and purchase intention changes positively with the change of channel trust from high to middle to low. Meanwhile, from Table 6C , it can be seen that, with the decrease of consumers’ perception of purchase benefit, the decreasing probability of high purchase intention is more obvious than that of high search intention, indicating that purchase benefit has a greater impact on online purchase intention. Table 6D shows that, when consumers’ willingness to search online is low, their willingness to buy online is also very low, indicating that consumers are less likely to choose the path of offline search–online direct purchase.

Table 6 . (A) Bayesian inference of switching cost in different state, (B) Bayesian inference of channel trust in different states, (C) Bayesian inference of perceived interests in different states, and (D) Bayesian inference of search intention in different states.

Bayesian Diagnosis

Bayesian diagnosis is the reverse operation of Bayesian reasoning; that is, the state of the independent variables is obtained through the state of the dependent variables. The following tables show the Bayesian diagnosis of search intention, purchase benefit, online trust, and switching costs given the purchase intention of the parent node. Table 7A shows the conditional probability set of search intention under the condition of a given parent node of purchase intention. It can be seen from Table 7A that search intention changes positively with the change in the H-M-L intention, and when the online purchase intention is clear, consumers have a high probability of choosing online search product information, indicating that online channels have a certain channel lock.

Table 7 . (A) Conditional probability table of search intention and (B) conditional probability of search intention and purchase intention.

Table 7B shows the set of conditional probabilities of switching costs, perceived benefit, and channel trust under the conditions of search intention and purchase intention. It can be seen from Table 7B that, with the H-M-L change of search intention and purchase intention, the probability of switching costs, perceived benefit, and channel trust gradually decreases. When the search intention is in the state of “high,” with the change of H-M-L purchase intention, the probability of a “high” perceived benefit is not obvious, which indicates that perceived benefit is an important reason to attract consumers to choose an online channel to buy products, while the probability of perceived benefit, transfer cost, and channel trust being “medium” and “low” decreases at first and then increases. When the search intention is in the “middle” state, with the change of purchase intention from high to low, the change of channel trust to “high” is more obvious. This shows that whether consumers choose to buy products directly online depends to a large extent on the degree of trust of they have in the channel, and enterprises that carry out online retail business can attract consumers to online channels by improving consumers’ trust in online channels.

Combining the empirical ability of SEM and the predictive and diagnostic ability of Bayesian networks, we analyzed the factors influencing consumers’ online search and purchase intention in multi-retail channels as well as the relationship between these factors. The results showed that as: (1) Consumers’ perceived benefits, channel trust, and switching cost have a positive impact on search intention, and consumers’ trust in online channels is the main factor driving their choice of online search, this result is consistent with the results found in the previous studies (e.g., Hajli, 2015 ; Martin et al., 2015 ; Reimer and Benkenstein, 2016 ; Zhao et al., 2017 ). (2) Consumers’ perceived benefits, channel trust, and switching cost have a positive impact on purchase intention, and the main factor for attracting consumers to choose online product purchasing is the perceived benefit factor, the greater the perceived benefits, the stronger the consumers’ willingness to buy the products (e.g., Martin et al., 2015 ; van der Lans et al., 2016 ). (3) Consumers’ willingness to search online also affects their willingness to buy online, and this result is consistent with the results found in the previous studies (e.g., Ngwe et al., 2019 ; Zhai et al., 2019 ); therefore, guiding consumers to search for products will increase purchasing probability of sold products. (4) When channel trust reaches a certain level, online channels have a certain channel lock, that is, consumers will choose the path of online search–online purchase, and channel switching cost also has a positive effect on the online channel lock, this is because the higher the perception of switching cost, the less likely it is for consumers to search for product information in one channel and purchase products in another channel (e.g., Anderson and Simester, 2013 ; Stan et al., 2013 ). (5) According to the Bayesian network diagnosis, search intention can adversely affect consumers’ perceived benefit, channel trust, and switching cost, and purchase intention can adversely affect consumers’ perceived benefit, channel trust, switching cost, and search intention.

This research provides new ideas on the research methods of consumer channel selection. The existing research on consumer channel choice is mostly qualitatively based on theory or empirical analysis of the causal relationship between variables with the help of statistical software (e.g., Huang et al., 2016 ; Elida et al., 2019 ), while ignoring the in-depth discussion of the complex interrelationships between variables. This article proposes a combination of structural equation modeling and Bayesian network research methods to explore the variables and complex relationships that affect consumers’ willingness to choose online shopping channels, and in-depth analysis of the attributes of online channels, with a view to further enriching consumer channel choice behaviors related research.

For retailers carrying out online retail business, analyzing the influencing factors of consumers’ online choice under multiple channels helps to better satisfy consumers’ channel preference, thus increasing the probability of interaction between retailers and consumers, improving consumers’ channel stickiness, and reducing enterprise service costs. Therefore, this study has important practical implication for solving the problem of ineffective online channel operation after traditional retail enterprises adopt multi-channel retail strategy.

Suggestions

According to the above research conclusions, we provide the following marketing suggestions for online retailers and company with an online business.

First, it is important to give attention to value marketing and strengthen customer stickiness. Online retailing as an important part of the new retail environment, and the continuous low-price strategy has been unable to retain consumers over the long term. Retailers need to balance the relationship between price and cost. Improving the price-to-performance ratio of products and the quality of distribution service is crucial to enhance the customer experience and maintain brand image. In addition, retailers can promote product updates, discount activities, brand value images and other information to customers through official accounts, well-known bloggers, and other ways to improve consumer loyalty.

Second, online retailers should focus on content marketing and improving customer attention. In the multi-channel retail environment, consumers have more independent choice of information search, and more vivid content is very important when consumers search and make purchase decisions. In addition to the e-commerce platforms, retailers could also make use of the emerging business infrastructure to provide convenient and quick product search channels, such as Mini Programs, official accounts or life accounts, to present products or brands in the form of text, pictures, short videos, and live broadcasts to attract consumers through multiple channels and increase consumer attention through multiple means. In addition, for different consumer groups, differentiated content marketing according to the positioning of the brand can also yield twice the results with half the effort in terms of attracting consumers’ attention.

Third, online retailers must engage in honest marketing and enhance the reputation of their brands. Trust is the key factor that supports the success of online retailing. The more consumers trust in the channel, the more likely they are to have positive search and shopping intention. Retailers can improve their credibility through the evaluation and certification of third-party sellers or with the help of consumers’ trust in well-known brands. Moreover, they can use credit mechanisms, such as “commitment+guarantee,” to allay consumers’ shopping concerns. This will impact the shopping procedures of consumers and help to win their trust.

In studying consumers’ willingness to choose channels under multiple retail channels, we considered their willingness to search and purchase through online channels, but we did not further compare online channels with offline channels. Future research will further refine channel selection factors, such as channel attributes. In addition, when analyzing consumers’ channel choice willingness in this study, we did not consider specific product types, because different product categories will affect consumers’ channel choice intention in the two-stage decision-making of searching information and purchasing products, future research could consider dividing different product categories or introducing other factors that affect consumer preferences to conduct research on consumers’ willingness to choose channels.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was implied via completion of the survey.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This research has been funded by the National Social Science Fund of China under grant no. 20CGL004, the Social Science Foundation of the Chinese Education Commission under grant no. 15XJA630003, and the Doctor Foundation of Chongqing University of Posts and Telecommunications under grant no. A2015-20.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: two-stage decision-making theory, search intention, purchase intention, structural equation model, Bayesian network

Citation: Deng W, Su T, Zhang Y and Tan C (2021) Factors Affecting Consumers’ Online Choice Intention: A Study Based on Bayesian Network. Front. Psychol . 12:731850. doi: 10.3389/fpsyg.2021.731850

Received: 28 June 2021; Accepted: 27 September 2021; Published: 20 October 2021.

Reviewed by:

Copyright © 2021 Deng, Su, Zhang and Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ting Su, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Início Números Vol.2 nº4 / nº3 Nº3 Artigos Drivers of shopping online: a lit...

Drivers of shopping online: a literature review

Consumers are increasingly adopting electronic channels for purchasing. Explaining online consumer behavior is still a major issue as studies available focus on a multiple set of variables and relied on different approaches and theoretical foundations. Based on previous research two main drivers of online behavior are identified: perceived benefits of online shopping related to utilitarian and hedonic characteristics and perceived risk. Additionally, exogenous factors are presented as moderating variables of the relationship between perceived advantages and disadvantages of internet shopping and online consumer behavior.

Entradas no índice

Keywords: , texto integral, 1. introduction.

1 The increasing dependence of firms on e-commerce activities and the recent failure of a large number of dot-com companies stresses the challenges of operating through virtual channels and also highlights the need to better understand consumer behavior in online market channels in order to attract and retain consumers.

2 While performing all the functions of a traditional consumer, in Internet shopping the consumer is simultaneously a computer user as he or she interacts with a system, i.e., a commercial Web site. On the other hand, the physical store has been transformed into Web-based stores that use networks and Internet technology for communications and transactions.

3 In this sense, there seems to be an understanding that online shopping behavior is fundamentally different from that in conventional retail environment, (Peterson et al ., 1997) as e-commerce relies on hypertext Computer Mediated Environments (CMEs) and the interaction customer-supplier is ruled by totally different principles.

4 Understanding the factors that explain how consumers interact with technology, their purchase behavior in electronic channels and their preferences to transact with an electronic vendor on a repeated basis is crucial to identify the main drivers of consumer behavior in online market channels.

5 Online consumer behavior research is a young and dynamic academic domain that is characterized by a diverse set of variables studied from multiple theoretical perspectives.

6 Researchers have relied on the Technology Acceptance Model (Davis, 1989: Davis et al ., 1989), the Theory of Reasoned Action (Fisbein and Ajzen, 1975), the Theory of Planned Behavior (Ajzen, 1991), Innovation Diffusion Theory (Rogers, 1995), Flow Theory (Czikszentmihalyi, 1998), Marketing, Information Systems and Human Computer Interaction Literature in investigating consumer’s adoption and use of electronic commerce.

7 While these studies individually provide meaningful insights on online consumer behavior, the empirical research in this area is sparse and the lack of a comprehensive understanding of online consumer behavior is still a major issue (Saeed et al ., 2003).

8 Previous research on consumer adoption of Internet shopping (Childers et al ., 2001; Dabholkar and Bagozzi, 2002; Doolin et al ., 2005; Monsuwé et al .; 2004; O´Cass and Fenech, 2002) suggests that consumers’ attitude toward Internet shopping and intention to shop online depends primarily on the perceived features of online shopping and on the perceived risk associated with online purchase. These relationships are moderated by exogenous factors like “consumer traits”, “situational factors”, “product characteristics” and “previous online shopping experiences”.

9 The outline of this paper is as follow. In the next section an assessment of the basic determinants that positively affect consumers’ intention to buy on the Internet is carried out. Second, the main perceived risks of shopping online are identified as factors that have a negative impact on the intention to buy from Internet vendors. Third, since it has been argued that the relationship between consumers’ attitude and intentions to buy online is moderated by independent factors, an examination of the influence of these factors is presented. Finally, the main findings, the importance to professionals and researchers and limitations are summarized.

2. Perceived benefits in online shopping

10 According to several authors (Childers et al ., 2001; Mathwick et al ., 2001; Menon and Kahn, 2002;) online shopping features can be either consumers’ perceptions of functional or utilitarian dimensions, or their perceptions of emotional and hedonic dimensions.

11 Functional or utilitarian perceptions relate to how effective shopping on the Internet is in helping consumers to accomplish their task, and how easy the Internet as a shopping medium is to use. Implicit to these perceptions is the perceived convenience offered by Internet vendor whereas convenience includes the time and effort saved by consumers when engaging in online shopping (Doolin, 2005; Monsuwé, 2004).

12 Emotional or hedonic dimensions reflect consumers’ perceptions regarding the potential enjoyment or entertainment of Internet shopping (Doolin, 2005; Monsuwé, 2004).

13 Venkatesh (2000) reported that perceived convenience offered by Internet Vendors has a positive impact on consumers’ attitude towards online shopping, as they perceive Internet as a medium that enhances the outcome of their shopping experience in an easy way.

14 Childers et al . (2001) found “enjoyment” to be a consistent and strong predictor of attitude toward online shopping. If consumers enjoy their online shopping experience, they have a more positive attitude toward online shop ping, and are more likely to adopt the Internet as a shopping medium.

15 Vijayasarathy and Jones (2000) showed that Internet shopping convenience, lifestyle compatibility and fun positively influence attitude towards Internet shopping and intention to shop online.

16 Despite the perceived benefits in online shopping mainly associated with convenience and enjoyment, there are a number of possible negative factors associated with the Internet shopping experience. These include the loss of sensory shopping or the loss of social benefits associated with shopping (Vijayasarathy and Jones, 2000).

17 In their research, Swaminathan et al . (1999) found that the lack of social interaction in Internet shopping deterred consumers from online purchase who preferred dealing with people or who treated shopping as a social ex perience.

3. Perceived risk in online shopping

18 Although most of the purchase decisions are perceived with some degree of risk, Internet shopping is associated with higher ri sk by consumers due to its newness and intrinsic characteristics associated to virtual stores where there is no human contact and consumers cannot physically check the quality of a product or monitor the safety and security of sending sensitive personal and financial information while shopping on the Internet (Lee and Turban, 2001).

19 Several studies reported similar findings that perceived risk negatively influenced consumers’ attitude or intention to purchase online (Doolin, 2005; Liu and Wei, 2003; Van der Heidjen et al ., 2003).

20 Opposing results were reported in two studies (Corbitt et al ., 2003; Jar venpaa et al ., 1999). The authors found that perceived risk of Internet shopping did not affect willingness to buy from an online store. One of the reasons for this contradictory conclusion might be due to the countries analyzed, respectively New Zealand and Australia, where individuals could be more risk- taken or more Internet heavy-users.

21 In examining the influences on the perceived risk of purchasing online, Pires at al. (2004) stated that no association was found between the fre quency of online purchasing and perceived risk, although satisfaction with prior Internet purchases was negatively associated with the perceived risk of intended purchases, but only for low-involvement products. Differences in perceived risk were associated with whether the intended purchase was a good or service and whether it was a high or low-involvement product. The perceived risk of purchasing goods through the Internet is higher than for services. Perceived risk was found to be higher for high-involvement than for low-involvement-products, be they goods or services.

22 Various types of risk are perceived in purchase decisions, including prod uct risk, security risk and privacy risk.

23 Product risk is the risk of making a poor or inappropriate purchase deci sion. Aspects involving product risk can be an inability to compare prices, being unable to return a product, not receiving a product paid for and product not performing as expected (Bhatnagar et al ., 2000; Jarvenpaa and Todd, 1997; Tan, 1999; Vijayasarathy and Jones, 2000).

24 Bhatnagar et al . (2000) suggest that the likelihood of purchasing on the Internet decreases with increases in product risk.

25 Other dimensions of perceived risk related to consumers’ perceptions on the Internet as a trustworthy shopping medium. For example, a common perception among consumers is that communicating credit card information over the Internet is inherently risky, due to the possibility of credit card fraud (Bhatnagar et al ., 2000; George, 2002; Hoffman et al ., (1999); Jarvenpaa and Todd, 1997; Liebermann and Stashevsky, 2002).

26 Previous studies found that beliefs about trustworthiness of the Internet were associated with positive attitudes toward Internet purchasing (George, 2002; Hoffman et al ., (1999); Liebermann and Stashevsky, 2002).

27 Privacy risk includes the unauthorized acquisition of personal information during Internet use or the provision of personal information collected by companies to third parties.

28 Perceived privacy risk causes consumers to be reluctant in exchanging personal information with Web providers (Hoffman et al ., 1999). The same authors suggest that with increasing privacy concerns, the likelihood of purchasing online decreases. Similarly, George (2002) found that a belief in the privacy of personal information was associated with negative attitudes toward Internet purchasing.

4. Exogenous factors

29 Based on the previous literature review, four exogenous factors were reported to be key drivers in moving consumers to ultim ately adopt the Internet as a shopping medium.

4.1. Consumer traits

30 Studies on online shopping behavior have focus mainly on demographic, psychographics and personality characteristics.

31 Bellman et al . (1999) cautioned that demographic variables alone explain a very low percentage of variance in the purchase decision.

32 According to Burke (2002) four relevant demographic factors – age, gen der, education, and income have a significant moderating effect on consum ers’ attitude toward online shopping.

33 In studying these variables several studies arrived to some contradictory results.

34 Concerning age, it was found that younger people are more interested in using new technologies, like the Internet, to search for comparative information on products (Wood, 2002). Older consumers avoid shopping online as the potential benefits from shopping online are offset by the perceived cost in skill needed to do it (Ratchford et al ., 2001).

35 On the other hand as younger people are associated with less income it was found that the higher a person’s income and age, the higher the propen sity to buy online (Bellman et al ., 1999; Liao and Cheung, 2001).

36 Gender differences are also related to different attitudes towards online shopping. Although men are more positive about using Internet as a shop ping medium, female shoppers that prefer to shop online, do it more frequently than male (Burke, 2002; Li et al ., 1999).

37 Furthermore Slyke et al . (2002) reported that as women view shopping as a social activity they were found to be less oriented to shop online than men.

38 Regarding education, higher educated consumers have a higher propen sity to use no-store channels, like the Internet to shop (Burke, 2002). This fact can be justified as education has been positively associated with individ ual’s level of Internet literacy (Li et al ., 1999).

39 Higher household income is often positively correlated with possession of computers, Internet access and higher education levels of consumers and consequently with a higher intention to shop online (Lohse et al ., 2000).

40 In terms of psychographics characteristics, Bellman et al . (1999) stated that consumers that are more likely to buy on the Internet have a “wired life” and are “starving of time”. Such consumers use the Internet for a long time for a multiple of purposes such as communicating through e-mail, reading news and search for information.

41 A personality characteristic closely associated with Internet adoption for shopping is innovativeness defined as the relative willingness of a person to try a new product or service (Goldsmith and Hokafer, 1991).

42 Innovativeness seems to influence more than frequency of online purchasing. It relates to the variety of product classes bought online, both in regard to purchasing and to visiting Web sites seeking information. (Blake et al ., 2003). In this sense innovativeness might be a fundamental factor determining the quantity and quality of online shopping.

4.2. Situational factors

43 Situational factors are found to be factors that affect significantly the choice between different retail store formats when consumers are faced with a shopping decision (Gehrt and Yan, 2004). According to this study, the time pressure and purpose of the shopping (for a gift or for themselves) can change the consumers’ shopping habits. Results showed that traditional stores were preferred for self-purchase situations rather than for gift occasions as in this case other store formats (catalog and Internet) performed better in terms of expedition. As for time pressure it was found that it was not a significantly predictor of online shopping as consumers when faced with scarcity of time responded to temporal issues related to whether there is a lag of time between the purchase transaction and receipt of goods rather than whether shopping can take place anytime.

44 Contradictory results were reported by Wolfinbarger and Gilly (2001). According to this study important attributes of online shopping are convenience and accessibility. When faced with time pressure situations, consumers engaged in online shopping but no conclusions should be taken on the effect of this factor on the attitude toward Internet shopping.

45 Lack of mobility and geographical distance has also been addressed has drivers of online shopping as Internet medium offers a viable solution to overcome these barriers (Monsuwé et al ., 2004). According to the same au thors the physical proximity of a traditional store that sells the same prod ucts available online, can lead consumers to shop in the “brick and mortar” alternative due to its perceived attractiveness despite consumers’ positive attitude toward shopping on the Internet.

46 The need for special items difficult to find in traditional retail stores has been reported a situational factor that attenuates the relationship between attitude and consumers’ intention to shop online (Wolfinbarger and Gilly, 2001).

4.3. Product characteristics

47 Consumers' decisions whether or not to shop online are also influenced by the type of product or service under consideration.

48 The lack of physical contact and assistance as well as the need to “feel” somehow the product differentiates products according to their suitability for online shopping.

49 Relying on product categories conceptualized by information economists, Gehrt and Yan (2004), reported that it is more likely that search goods (i.e. books) can be adequately assessed within a Web than experience goods (i.e. clothing), which usually require closer scrutiny.

50 Grewal et al . (2002) and Reibstein (1999) referred to standardized and fa miliar products as those in which quality uncertainty is almost absent and do not need physical assistance or pre-trial. These products such as groceries, books, CDs, videotapes have a high potential to be considered when shopping online.

51 Furthermore in case of certain sensitive products there is high potential to shop online to ensure adequate levels of privacy and anonymity (Grewal et al ., 2002). Some of these products like medicine and pornographic movies are raising legal and ethical issues among international community.

52 On the other hand, personal-care products like perfume or products that required personal knowledge and experience like cars or computers, are less likely to be considered when shopping online (Elliot and Fowell, 2000).

4.4. Previous online shopping experiences

53 Past research suggests that prior online shopping experiences have a direct impact on Internet shopping intentions. Satisfactory previous experiences decreases consumers’ perceived risk levels associated with online shopping but only across low-involvement goods and services (Monsuwé et al ., 2004).

54 Consumers that evaluate positively the previous online experience are motivated to continue shopping on the Internet (Eastlick and Lotz, 1999; Shim et al ., 2001; Weber and Roehl, 1999).

5. Conclusion

55 Relying on an extensive literature review, this paper aims to identify the main drivers of online shopping and thus to give further insights in explaining consumer behavior when adopting the Internet for buying as this issue is still in its infancy stage despite its major importance for academic and professionals.

56 This literature review shows that attitude toward online shopping and in- tention to shop online are not only affected by perceived benefits and perceived risks, but also by exogenous factors like consumer traits, situations factors, product characteristics, previous online shopping experiences.

57 Understanding consumers’ motivations and limitations to shop online is of major importance in e-business for making adequate strategic options and guiding technological and marketing decisions in order to increase customer satisfaction. As reported before consumers´ attitude toward online shopping is influenced by both utilitarian and hedonic factors. Therefore, e-marketers should emphasize the enjoyable feature of their sites as they promote the convenience of shopping online. As personal characteristics also affect buyers´ attitudes and intentions to engage in Internet shopping e-tailers should customize customers´ treatment. Furthermore, the e-vendor should assure a trust-building relationship with its customers to minimize perceived risk associated to online shopping. Adopting and communicating a clear privacy policy, using a third party seal and offering guarantees are mechanisms that can help in creating a reliable environment.

58 Some limitations of this paper must be pointed out as avenues for future. The factors identified as main drives of shopping online are the result of a literature review and there can always be factors of influence on consumers´ intentions to shop on the Internet that are not included because they are addressed in other studies not included in this review. However there are methodological reasons to believe that the most relevant factors were identified in this context. A second limitation is that this paper is the result of a literature review and has never been tested in its entirety using empirical evidence. This implies that some caution should be taken in applying the findings that can be derived from this paper Further research is also needed to determine which of the factors have the most significant effect on behavioral intention to shop on the Internet.

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Ana Teresa Machado , «Drivers of shopping online: a literature review» ,  Comunicação Pública , Vol.2 nº4 / nº3 | 2006, 39-50.

Referência eletrónica

Ana Teresa Machado , «Drivers of shopping online: a literature review» ,  Comunicação Pública [Online], Vol.2 nº4 / nº3 | 2006, posto online no dia 30 outubro 2020 , consultado o 16 março 2024 . URL : http://journals.openedition.org/cp/8402; DOI : https://doi.org/10.4000/cp.8402

Ana Teresa Machado

Escola Superior de Comunicação Social Instituto Politécnico de Lisboa

[email protected]

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CONSUMERS' BUYING BEHAVIOR ON ONLINE SHOPPING: AN UTAUT AND LUM MODEL APPROACH

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A Meta-Analysis of Online Impulsive Buying and the Moderating Effect of Economic Development Level

• Published: 11 August 2021
• Volume 24 , pages 1667–1688, ( 2022 )

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• Yang Zhao 1 ,
• Yixuan Li 1 ,
• Ning Wang 1 ,
• Ruoxin Zhou 2 &
• Xin (Robert) Luo   ORCID: orcid.org/0000-0003-0122-7293 3  

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Online impulsive buying has become increasingly prevalent in e-commerce and social commerce research, yet there is a paucity of systematically examining this particular phenomenon in the paradigm of information systems. To advance this line of research, this study aims to gain insight into online impulsive buying through a meta-analysis of relevant research. Derived from 54 articles, this meta-analysis categorized the critical factors that influence online impulsive buying into the website, marketing, and affective stimuli. This study further explores the moderating effect of economic development level. The empirical results reveal that the chosen 13 main factors are significantly and positively related to online impulsive buying except for website security, price, novelty, and negative emotion. Moreover, economic development level moderates the relationship between several factors (i.e., website visual appeal, ease of use, price, promotion, pleasure, and positive emotion) and online impulsive buying. This study contributes to both theory and practice. It not only extends the impulsive buying literature to the online context by emphasizing the IT-supported website stimuli, but also provides implications for future research on online impulsive buying behavior across different economic development levels. Moreover, it provides guidelines for practitioners on how to leverage information technology to induce online impulsive buying.

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1 Introduction

With the prosperity of e-commerce, we have witnessed a paradigmatic shift where an increasing amount of people switch from offline to online shopping. Since 2015, the number of global online shoppers has been on the rise, exceeding 1.7 billion in 2018 and reaching 1.92 billion in 2019. It is expected to maintain a significant upward trend in the future (iimedia, 2021 ). In particular, during the COVID-19 pandemic, online shopping has increased significantly across many categories, and consumers’ intentions to shop online continue to grow (Tamara et al. 2020). In the online context, consumers are highly susceptible to irrational purchases, such as impulsive buying (Chen & Zhang, 2015 ). Impulsive buying, defined as consumers making unplanned purchases suddenly (Rook, 1987 ), occurs more online. Previous research suggested that impulsive purchases are 5% more likely online than offline (Nielsen, 2017 ), and money spent on online impulsive buying approximately accounts for 40% of consumers’ online expenditure (Liu et al., 2013 ).

Noticing the trend, researchers have made comparisons between offline shopping and online shopping (Gilly & Celsi, 2000 ; Levin et al., 2005 ; Sarkar & Das, 2017 ; Wang et al., 2018 ). The key differences between online and offline shopping are the way product information being collected, perceived risk, and the ability of consumers to access similar products based on preference (Sarkar & Das, 2017 ). Additionally, compared with offline shopping, online shopping, supported by information technology, offers more favorable and facilitating conditions to impulsive buying in terms of the shopping environment (Eroglu et al., 2001 ), such as simplified product-searching process and easy to buy (e.g., one-click ordering) (Verhagen & Dolen, 2011 ). The impulse is more about motivating consumers to buy through human-computer interactions in the buying process (Nielsen, 2017 ).

In the extant studies, researchers have primarily explored the antecedents of online impulsive buying behavior based on the Stimulus-Organism-Response (SOR) framework, and online impulsive buying is seen as the result of being exposed to a stimulus (Mehrabian & Russell, 1974 ; Piron, 1991 ). In previous research, the main antecedents (stimuli) of online impulsive buying can be generally divided into three types: website, marketing, and affective. First, website stimuli are the key factors that distinguish online impulsive buying from offline one. E-commerce website plays as an intermediary between consumers and products, and consumers’ online buying process has to interact with the website, which directly affects the possibility of online impulsive buying (Wells et al., 2011 ). For instance, Åberg and Kurdieh ( 2013 ) suggested that online grocery shopping sites can successfully trigger consumers’ online impulsive buying by emphasizing features associated with interactivity. In terms of website stimuli, researchers have investigated website security, website navigability, website visual appeal, interactivity, ease of use, etc. All these features are realized by information technology and online exclusive. Second, marketing stimuli also play a crucial role in influencing online impulsive buying. Among marketing stimuli, some factors are similar to those in offline impulsive buying, such as discount price and promotion (Iyer et al., 2019 ). However, the online context has its unique advantages, because IT-facilitated online context can amplify the effect of scarcity on online impulsive buying (Wu et al., 2020 ). For example, online retailers can provide real-time inventory availability information, which underlines the scarcity effect. The results of field experiments on Amazon show that a 10% increase in past claims leads to a 2.08% increase in cart add-ins in the next hour (Cui et al., 2019 ). Third, affective stimuli as internal trigger cues were widely studied in prior research on online impulsive buying. Consumers’ affective state is found to have an influence on their online impulsive buying behavior (Dawson & Kim, 2009 ). For example, researchers suggested that pleasure and arousal both positively affect online impulsive buying (Liu et al., 2020 ). The most widely studied affective stimulus factors include arousal, pleasure, positive emotion, and negative emotion.

As a research topic with many empirical studies, researchers have conducted meta-analyses on impulsive buying (Amos et al., 2013 ; Iyer et al., 2019 ). However, despite that the online context has its idiosyncrasies and warrants further investigation, there is a scarcity of comprehensive research on online impulsive buying, and the role of information technology has yet to be investigated. Since consumers’ shopping behavior in offline physical stores is rather divergent from that of online shopping, the triggers of impulsive buying are also different between the online and offline paradigms. Besides the antecedents of traditional offline impulsive buying, online impulsive buying is also affected by a myriad of factors, especially website-related factors. Websites play a crucial role in the shopping process, acting as the mediator between products and consumers that helps to build consumer relationships, facilitate consumer support, and convert visitors into consumers in the online context (Ghose & Dou, 1998 ). Hence, it is of vital importance to shed new light on and further examine consumers’ online impulsive buying. Albeit considerable empirical research, results are inconsistent in the literature. Take online stores’ navigability for an example, Zou ( 2018 ) found that it has a strong positive relationship with online impulsive buying, whereas Floh and Madlberger ( 2013 ) showed that the influence of online store’s navigation is insignificant. Therefore, it is paramount to synthesize these inconsistent findings and further investigate the phenomenon. Meta-analysis, as an integrated statistical analysis, can quantify the inconsistency of results across studies and this method has been frequently applied to information systems research (Ismagilova et al., 2020 ; Tamilmani et al., 2020 ; Trang & Brendel, 2019 ).

This study aims to gain insight into online impulsive buying by conducting a meta-analysis of relevant research, hoping to further advance this line of research and fill the research gap that website stimuli have yet to be scientifically epitomized and there is no comprehensive review on online impulsive buying. In addition to emphasizing the IT-supported website stimuli, this meta-analysis also synthesizes the inconsistent findings and uncovers the key factors influencing online impulsive buying from the marketing and affective perspectives, as well as the moderating effect of economic development level. To our knowledge, this is the first meta-analysis on online impulsive buying where website stimuli are first included in the comprehensive research on online impulsive buying. 1354 sample papers were collected by retrieving a combination of keywords in academic databases, and they were carefully screened before the meta-analysis. Finally, 54 related empirical studies were used for the analysis. These studies were published during 2006–2020, which is in correspondence with the development process of e-commerce research from early to modern times. Regarding the development of electronic commerce, the second wave began in 2004 and the third wave began in 2010 (Schneider, 2017 ). In 2006, electronic commerce has reached a high development speed and received wide attention. After that, the advancement of information technology contributes to the fast growth of e-commerce, which has amplified impulsive buying behavior in the online context. To conclude, the time frame of this study is from 2006 to 2020, which includes the research on online impulse buying from the early to the modern stage. The time frame covers the electronic commerce recent developing stages, and the results are comprehensive. Specifically, the results showed that the chosen 13 main factors are significantly and positively related to online impulsive buying except for website security, price, novelty, and negative emotion. Furthermore, the relationship between several factors (i.e., website visual appeal, ease of use, price, promotion, pleasure, and positive emotion) and online impulsive buying are significantly moderated by economic development level. This study contributes to the research on online impulse buying: first, this research fills the literature gap by synthesizing inconsistent results of the existing research on online impulsive buying and highlighting the importance of website stimuli; second, we provide a theoretical basis for future research on online impulsive buying by proposing a comprehensive framework that includes the website, marketing, and affective stimuli; third, we provide implications for future research on online impulsive buying behavior across different economic development levels; fourth, this study provides managerial guidelines for practitioners of e-commerce websites. With the analysis results, they can take appropriate actions to optimize consumers’ online buying experience and use marketing methods to induce online impulse buying.

2 Literature Review and Hypothesis Development

2.1 impulsive buying and online impulsive buying.

According to Rook ( 1987 ), impulsive buying is defined as “a sudden, often powerful and persistent urge to buy something immediately”. Based on the definition, Beatty and Ferrell ( 1998 ) extended the definition as “a sudden and immediate purchase with no pre-shopping intentions either to buy the specific product category or to fulfill a specific buying task”. Although the definitions of impulsive buying varied in detail, the nature of it remains the same — unplanned. Previous studies have investigated it from various perspectives, including environment (Chang et al., 2011 ; Mattila & Wirtz, 2008 ; Mohan et al., 2013 ), individual (Peck & Childers, 2006 ; Sharma et al., 2010 ; Verplanken & Herabadi, 2001 ), product (Bellenger et al., 1978 ; Kacen et al., 2012 ; Liao et al., 2009 ). To integrate diverse findings and to provide a comprehensive overview of impulsive buying, researchers have made considerable efforts to review the literature (Amos et al., 2013 ; Iyer et al., 2019 ; Muruganantham & Bhakat, 2013 ).

In recent years, the advancement of information technology contributes to the fast growth of e-commerce, which amplified impulsive buying behavior in the online context. From the view of facilitators, taking advantage of information technology, consumers are experiencing a much smoother buying decision process in the online context. First, navigation and search functions help consumers accelerate their searching process, and some people may come to a quick decision (Moe, 2003 ). Second, personalized recommendation efficiently optimizes consumers’ product discovery process, which drives impulsive buying (Smith & Linden, 2017 ). Third, one-click buying online makes the path to purchase shorter and easier, which both increases the conversion rate and the incidences of impulsive buying (Verhagen & Dolen, 2011 ). In general, with all these IT-facilitated features, consumers’ online shopping experience is smoother and the likelihood of impulsive buying may increase (Stern, 1962 ). However, from the view of prohibitors, consumers may have security concerns. That is, making consumers feel secure is the prerequisite for online shopping. Besides, people with little Internet experience may have higher shopping costs online. Even experienced online shoppers may get frustrated if the website is hard to use. In this regard, optimizing the website and making it easy to use is necessary. These are specific factors that influence consumers’ online shopping experience dramatically. Therefore, exploring how different factors, especially website-related attributes, affect online impulsive buying is worthwhile (Liu et al., 2013 ; Parboteeah et al., 2009 ; Turkyilmaz et al., 2015 ).

Website-related attributes, supported by information technology, serve as environmental cues for online impulsive buying and they can be categorized into task-relevant cues and mood-relevant cues (Parboteeah et al., 2009 ). Task-relevant cues, such as navigability and search functions, can help consumers achieve their online shopping goals. On the contrary, mood-relevant cues, such as website visual appeal, mainly influence how much users enjoy browsing a website, but they do not directly support specific shopping goals (Parboteeah et al., 2009 ). Focusing on the website attributes as stimuli, Liu et al. ( 2013 ) found that website ease of use, website visual appeal, and product availability (scarcity) are crucial antecedents of online impulsive buying. Despite the meta-analysis of impulsive buying, to our knowledge, there is no meta-analysis on online impulsive buying, and hence these unique website stimuli were not emphasized. Therefore, this study aims to bridge this research gap and provide an amalgamation of the findings of online impulsive buying.

Traditional impulsive buying studies have divided influencing factors into two categories: internal and external ones (Iyer et al., 2019 ; Kalla & Arora, 2011 ; Wansink, 1994 ; Xiao & Nicholson, 2013 ). The most widely studied internal factors are consumer-related factors, like impulsive buying tendency and pre-purchase mood (Ozer & Gultekin, 2015 ). Take a step further, researchers explored more into consumers’ emotion-related factors, which are defined as affective stimuli factors. As for external factors, environmental factors, such as window displays and in-store design are widely studied (Gudonavičienė & Alijošienė, 2015 ).

When it comes to the online context, internal factors remain unchanged. The main internal factors are still personality, hedonic motivation, trust, and so on. However, external factors are different from those of offline, mainly because online impulsive buying happens when a consumer interacts with the website (Verhagen & Dolen, 2011 ). That is, websites act as an intermediary between consumers and products. Consequently, online impulsive buying researchers have seen website-related attributes, such as IT-facilitated website attributes and media format (Adelaar et al., 2003 ; Liu et al., 2013 ), as the primary external environmental factors. For website attributes, perceived ease of use, visual appeal, and product availability (scarcity) are crucial cues of online impulsive buying (Liu et al., 2013 ). For media format, Adelaar et al. ( 2003 ) found that the media format of items’ information presented in the online shopping environment could increase impulsive buying behavior.

To summarize, the key distinction between online and traditional impulsive buying is that e-commerce is full of IT features, and the external stimuli in online impulsive buying are website-related factors.

2.2 The Factors for Meta-Analysis

Based on the research emphasis of the collected online impulsive buying literature and following the guidelines of prior research, Footnote 1 this study included 13 main factors (i.e., website security, website navigability, website visual appeal, interactivity, ease of use, scarcity, novelty, price, promotion, arousal, pleasure, positive emotion, and negative emotion) that have been explored most in previous research and they fall into three categories of stimuli according to the Stimulus-Organism-Response (SOR) framework: two from external factors (i.e., website stimuli (Liu et al., 2013 ; Parboteeah et al., 2009 ; Wells et al., 2011 )) and marketing stimuli (Chan et al., 2017 ; Park et al., 2014 ; Shim & Altmann, 2016 )), and one from internal factors (i.e., affective stimuli (Dawson & Kim, 2009 ; Huang, 2016 ; Rook & Gardner, 1993 )).

Website stimuli, as the important external factors that distinct online and offline impulsive buying, were extensively examined in online impulsive buying research. Therefore, website stimuli were included first in this meta-analysis. Besides website stimuli, another important external factor is the marketing stimuli. Moreover, as part of internal factors, factors related to individuals’ emotions or moods were also included in the meta-analysis due to the importance of the affective process of online impulsive buying.

2.3 Website Stimuli

2.3.1 website security.

Website security is the measures taken to ensure the confidentiality of personal information, the security of online payment, an explanation of confidentiality policy, and reliability of the website (Wu et al., 2012 ). Website security serves as a high task-relevant cue that can contribute to consumers’ purchasing goal attainment and affects consumers’ behavior (Wells et al., 2011 ). When people shop online, website security is one of the main concerns. In most cases, with higher website security, people will be more likely to feel assured when shopping on this website, which is the prerequisite for online impulsive buying. According to Zou ( 2018 ), users are more likely to make online impulsive buying if they feel secure shopping on this website. Therefore, we hypothesize that:

H1. There is a significant, positive relationship between website security and online impulsive buying.

2.3.2 Website Navigability

Website navigability is defined as the order of the pages, the organization of the layout, and the consistency of the navigation protocols (Palmer, 2002 ). Website navigability plays a crucial role when consumers browse the website and search for a specific product. Website navigability is an e-commerce interface characteristic that provides functional convenience (Wells et al., 2011 ). Moreover, navigation is important to improving users’ experience for the website (Nielsen, 2000 ), and improve online impulsive buying tendency (Li et al., 2016 ; Zou, 2018 ). Therefore, website navigability is a facilitating factor, and we hypothesize that:

H2. There is a significant, positive relationship between website navigability and online impulsive buying.

2.3.3 Website Visual Appeal

Website visual appeal involves the choice of various visual elements such as fonts, graphics and so on to enhance the overall appearance of the website (Loiacono et al., 2007 ). If the website is visually appealing, it will increase the probability of browsing this website and also consumer intention to purchase products. In extant literature on online impulsive buying, website visual appeal is positively related to online impulsive buying behavior (Liu et al., 2013 ; Wells et al., 2011 ). Hence, website visual appeal can increase the probability of online impulsive buying, and the following hypothesis is put forward:

H3. There is a significant, positive relationship between website visual appeal and online impulsive buying.

2.3.4 Interactivity

Interactivity refers to “the extent to which users can participate in modifying the form and content of the mediated environment in real-time” (Steuer, 1992 ), which can also be defined as “the degree to which consumers perceive that the items manifestation is two-way, controllable, and responsive to input” (Mollen & Wilson, 2010 ). Better interactivity leads to a good sense of local presence for the consumer through the availability of options to manipulate the product (Vonkeman et al., 2017 ), which provides a better understanding of the item for consumers. With a comprehensive understanding of the product, it is more likely for consumers to be stimulated to make an instant online buying decision. Hence, we propose the following hypothesis:

H4. There is a significant, positive relationship between interactivity and online impulsive buying.

2.3.5 Ease of Use

Ease of use is a proxy for functional convenience (Chen & Yao, 2018 ). As one of the website elements, it significantly influences consumers’ attitudes toward the website (Elliott & Speck, 2005 ). In most cases, the easier to use the website, the more likely for people to use it. Ease of use positively affects online impulsive buying (Chen & Yao, 2018 ; Liu et al., 2013 ; Turkyilmaz et al., 2015 ). Therefore, we propose the following hypothesis:

H5. There is a significant, positive relationship between ease of use and online impulsive buying.

2.4 Marketing Stimuli

2.4.1 scarcity.

Scarcity is used to describe the state that a product or a service is in short demand (Kemp & Bolle, 1999 ), including two types: limited-time scarcity and limited-quantity scarcity (Lynn, 1989 ). As one of the marketing principles in e-commerce, scarcity can arouse the urgency of consumers thus motivating them to make more purchases (Aggarwal et al., 2011 ). It enhances the buying process by informing consumers that access to a particular product is limited (Lynn, 1989 ). According to Wu et al. ( 2020 ), both limited-quantity scarcity and limited-time scarcity can positively lead to online impulsive buying. Thus, the following hypothesis is proposed:

H6. There is a significant, positive relationship between scarcity and online impulsive buying.

2.4.2 Price

Price refers to the amount of money paid for the products, which is a decisive factor for shopping, especially for people with lower income or with a limited budget. Consumers shopping online are more sensitive to the price of products because they can do price comparisons easily with little cost (Xu & Huang, 2014 ). Park et al. ( 2012 ) found that while browsing the website, consumers are likely to take the impulsive buying action if the price is unusually attractive. The results of Zou ( 2018 ) confirmed that the price of a product has a significant effect on online impulsive buying behavior. Therefore, we propose the following hypothesis:

H7. There is a significant, positive relationship between price and online impulsive buying.

2.4.3 Novelty

Novelty is a triggering factor that inspires consumers to generate a desire for a new product or new experience; thus it can easily promote impulsive buying behavior in an online context (Khare et al., 2010 ). As one facet of hedonic shopping value, novelty makes shopping a way to explore the new world and if consumers seek novelty, they will feel excited about finding unique things (Yu & Bastin, 2010 ). Novelty was found to have a strong positive impact on online impulsive buying behavior (Yu & Bastin, 2010 ; Zou, 2018 ). Hence, the following hypothesis is put forward:

H8. There is a significant, positive relationship between novelty and online impulsive buying.

2.4.4 Promotion

Promotion is defined as a way to increase sales by giving a discount or offering an extra value or incentive for the product to persuade consumers into making the purchases (Haugh, 1983 ). When consumers are attracted by the promotion, it is easier for them to purchase something which is seemingly a bargain even if they do not need it. According to Nochai and Nochai ( 2011 ), promotion factors such as “a discount on membership”, “extending the warranty” and “being able to pay by installments” are the important influencing factors of consumers’ purchasing decisions. Studies have found that there is a significant positive relationship between sales promotion and online impulsive buying (Hasim et al., 2018 ; Longdong & Pangemanan, 2015 ). Thus, we propose the following hypothesis:

H9. There is a significant, positive relationship between promotion and online impulsive buying.

2.5 Affective Stimuli

2.5.1 arousal.

Arousal reflects to what degree an atmosphere can influence stimulation (Shen & Khalifa, 2012 ). High arousal is associated with impulsive buying behaviors through mobilization (Rook & Gardner, 1993 ). Shen and Khalifa ( 2012 ) found that arousal is significantly positively related to impulsive buying when consumers feel the environment is pleasant. Also, arousal is positively related to online impulsive buying (Lin & Lo, 2016 ; Mattila & Wirtz, 2001 ). Therefore, we propose the following hypothesis:

H10. There is a significant, positive relationship between arousal and online impulsive buying.

2.5.2 Pleasure

Pleasure refers to “the hedonic valence of the affective response to a stimulus” (Mehrabian & Russell, 1974 ). More specifically, it measures “the degree to which a person feels happy and joyful when subject to a stimulus” (Menon & Kahn, 2002 ). If people perceive their previous shopping experience as pleasant, they are more likely to process information that is consistent with this positive mood (Adaval, 2001 ). Consistent with previous studies, Shen and Khalifa ( 2012 ) found that pleasure serves as an important determinant of online impulsive buying behavior, suggesting that a delighted emotional experience in online buying has a positive effect on their subsequent buying behavior tendency. Thus, the following hypothesis is proposed:

H11. There is a significant, positive relationship between pleasure and online impulsive buying.

2.5.3 Positive Emotion

Positive emotion refers to “the extent to which a person feels enthusiastic, excited, and inspired” (Chan et al., 2017 ). Previous studies have explored the effect of positive emotion on online impulsive buying behavior. Impulsive buyers are usually more emotional, they enjoy getting fun from browsing and shopping, and when they are aware of their desire to purchase something impulsively, they tend to take immediate action in a state of hyperactivity and excitement (Weinberg & Gottwald, 1982 ). That is, positive emotion positively and significantly influences impulsive purchasing (Lu, 2013 ; Suhud & Herstanti, 2017 ). Thus, the following hypothesis is proposed:

H12. There is a significant, positive relationship between positive emotion and online impulsive buying.

2.5.4 Negative Emotion

Negative emotion refers to “the extent to which a person feels distressed, irritated, and disturbed” (Chan et al., 2017 ). It is also known as negative affect which is defined as the extent to which a person reflects the painful and unhappy engagement with one’s surrounding environment (Watson et al., 1988 ). Compared with positive emotions, negative emotions drain customers’ energy, resulting in less impulsive purchasing behavior (Rook & Gardner, 1993 ). However, Mano ( 1999 ) found that consumers with negative emotions are more likely to make purchases because they take it as a way to make themselves happy. Therefore, we propose the following hypothesis:

H13. There is a significant, positive relationship between negative emotion and online impulsive buying.

To summarize, factors influencing online impulsive buying were categorized into three types: website stimuli, marketing stimuli, and affective stimuli. Table 1 displays the 13 influencing factors included in this meta-analysis.

2.6 Moderating Variable: Economic Development Level

Consider budget constraint, consumption level tremendously affects consumers’ buying decision (Tian & Liu, 2011 ). Also, it affects consumers’ price sensitivities. In this regard, consumption level might moderate consumers’ online impulsive buying behavior. However, only the countries and regions were recorded in previous studies. To deal with this data availability issue, we chose the economic development level as a proxy of consumers’ consumption level. The solution is supported by the following reasons:

First, economic development level is usually positively correlated to consumption level. Statistically, consumers who live in countries or regions with a higher economic development level usually also have a higher consumption level. On the one hand, people in developed countries or regions are more likely to have higher disposable income. Hence, when they are faced with a buying decision, they are less likely to hesitate due to budget constraints. On the other hand, in terms of price sensitivity, the results of a PayPal study indicated that 56.0% of consumers in the USA shop online out of the price advantage as compared to 68.0% in India and 83.0% in China (Saxena, 2019 ). Besides, Kübler et al. ( 2018 ) analyzed the sensitivity of sales to price and user ratings across developing and developed countries. The result indicated that countries with a lower level of income inequality are more sensitive to rating volume when it comes to economic factors. To some extent, this demonstrates the difference in price sensitivity between consumers in developed and developing countries or regions. Second, the economic development level is also positively related to the development level of information technology. Online shopping has been widely accepted in many developed countries while it is still in the primary stage in many developing countries. In this regard, the online shopping experience, from the searching stage to the delivery, may be markedly different in developed and developing countries. That is, people from a country or region with higher development usually have a better online shopping experience, and it may further lead to online impulsive buying. Given these reasons, we decide to use the economic development level as a proxy for consumption level, and we suppose that it may affect online impulsive buying.

The source of the sample in articles that we chose to conduct the meta-analysis includes both developed and developing countries and regions (Zhao et al., 2019 ). Based on the existing online impulsive buying literature, we speculated that the influences of the antecedents of online impulsive shopping are different between developed and developing countries and took the economic development level as a moderator. Hence, the following hypothesis is proposed:

H14. Economic development level has a significant moderating effect on the relationship between the website stimuli, marketing stimuli, and affective stimuli and online impulsive buying.

Figure 1 presents the proposed research model.

Proposed Research Model

3 Methodologies

3.1 data collection.

To ensure the accuracy of meta-analysis results, we made considerable efforts to search relevant literature, including published journals, conference proceedings, and dissertations. Following Webster and Watson ( 2002 ), “online impulsive buying”, “online impulsive purchasing”, “online impulsive shopping” were used as keywords and we added keywords such as “website stimuli”, “marketing stimuli” and “affective stimuli” to list the search formula with Boolean logic in the systematic retrieval of relevant articles in the following databases: Google Scholar, Web of Science, Science Direct, SpringerLink, etc. The preliminary search found 1345 initial papers. Then, we read the title and abstract of each paper carefully to check whether it is related to online impulsive buying and dropped the repeated articles. Finally, 121 articles were included.

Meta-analysis requires papers to meet the following criteria:

The paper must be an empirical study of online impulsive buying and quantitatively tested relationships between antecedent factors and online impulsive buying tendency or behavior.

The paper must have reported correlation coefficients or other values (e.g. F-value) that could be converted to correlation coefficients.

The paper must have reported the sample size.

Besides the above screening criteria, to ensure the independence of the research, we also excluded relevant research conducted by the same research team using the same sample.

Finally, 54 articles met all the above criteria and were used for the meta-analysis. Among the 54 articles, 37 are journal articles, 6 are published in conference proceedings, and 11 are dissertations. These studies were published during 2006–2020. In specific, 2 studies were published before 2010 (during the second wave of e-commerce), and 52 studies were published between 2010 and 2020 (during the third wave of e-commerce). The total sample size of the articles is 19,085 and the average sample size is 353. Figure 2 shows the paper selecting process. Figure 3 demonstrates the world distribution and coverage of the studies included in the meta-analysis. Selected papers are listed in Appendix 2 (Table 7 ).

The research progress of articles

World distribution and coverage of the studies included in the meta-analysis

3.2 Coding Procedure

Each article was scrutinized to extract key data to be used in the study. The key data include: author name, publication date, publications, investigated countries or regions, sample size, key constructs, and reported effect sizes. Considering that there are many constructs with different names expressing similar meanings, we merged constructs with similar meanings. For example, perceived enjoyment is similar to entertainment, and visual appeal is similar to aesthetic appeal. According to the guidelines of Rana et al. ( 2015 ), we only selected those relationships that have been explored three or more times in the literature in the meta-analysis, and finally, we got a total of 13 relationships.

To conduct the moderator analysis, all articles included in the meta-analysis were divided into two groups based on the economic development level of the country or region. Footnote 2

A small number of studies did not report the correlation coefficient, but they reported the standard regression coefficient. In this case, we treated the standard regression coefficient as effect size. Very few studies reported F -value. For these studies, we used the formula proposed by Wolf ( 1986 ) to calculate the effect size: \( r=\sqrt{\frac{F}{F+ df}} \) , where F is the F -value of the path, and df is the degree of freedom.

In order to ensure the accuracy of data coding, two researchers in this study conducted back-to-back coding on the literature samples according to the coding specifications proposed by Lipsey and Wilson ( 2001 ), and then cross-checked the coding results. The consistency ratio was 93.5%. Finally, the research team discussed the inconsistent results carefully and referred to previous classifications to reach agreements.

3.3 Analysis Procedure

First, we provided descriptive statistics of each antecedent factor to roughly observe the impact of each antecedent factor on online impulsive buying.

Second, we calculated the combined effect of each pair of relationships (Fleiss, 1993 ). Besides, to ensure the normal distribution of the correlation coefficient of each pair of relationships, we made the Fisher r to z transformation.

Then, we used a heterogeneity test (Q-test) to test the heterogeneity of the distribution of effect sizes and find potential moderator effects. The economic development level in each study was used as the categorical moderator factor. A forest plot was used to visualize the results of the subgroup analysis.

Finally, to avoid publication bias, we calculated the fail-safe N for each relationship. Publication bias refers to the phenomenon that in academic research, researchers tend to report significant results and avoid reporting results that are not statistically significant (Kraemer & Andrews, 1982 ).

4.1 Descriptive Statistics

This research examined 13 antecedent factors. The average sample size of each path is over 200. The descriptive statistics of each relationship are shown in Table 2 .

4.2 Correlation Analysis

In this section, we used the correlation coefficient and sample size to calculate the relationship between website stimuli factors, marketing stimuli factors, affective stimuli factors, and online impulsive buying.

It is worth mentioning that the choice of the fixed-effect model or random-effect model is very important. According to Borenstein et al. ( 2007 ), when there is a single effect in the hypothesis sample, the fixed-effect model should be selected. Otherwise, the random-effect model should be selected. Given the differences in the samples, the random-effect model is selected to calculate the combined effect.

The results of the meta-analysis are shown in Table 3 . Except for the combined effect sizes of the relationship between website security, price, negative emotion, and online impulsive buying, all 95% confidence intervals of combined effect sizes exclude zero, and all Z-scores are significant, indicating that these combined effect sizes are statistically significant.

According to Cohen ( 1988 ), the combined effect sizes can be categorized into weak (around 0.1), moderate (around 0.3), and strong (around 0.5). The findings indicated that the website stimuli factors have a significant and positive relationship with online impulsive buying except for the website security factor. Among them, the correlation between interactivity and online impulsive buying is the weakest and the combined effect size of it is only 0.17. It can be inferred that in previous studies, the interactivity of websites is not the main factor affecting online impulsive buying. With regard to the marketing stimuli factors, price, novelty, and promotion are crucial factors affecting online impulsive buying, with their combined effect size over 0.3. In addition, the relationships between price, novelty and online impulsive buying are not significant. As for affective stimuli factors, although the relationship between the negative emotion factor and online impulsive buying is insignificant, the rest of the affective factors are significantly and positively related to online impulsive buying at a moderate level. By observing the results of correlation analysis, it is easy to find that the number of studies with insignificant relationships is no more than five, and the maximum and minimum correlation coefficients for each study varied greatly. Therefore, the limited number of studies and the large differences in the effect sizes reported in the selected studies may account for the insignificant results of these four relationships.

The heterogeneity test of the effect size of our study was estimated using the Q statistic to determine whether each effect size can be merged into a new value. The result of the Q-test in Table 3 revealed that the heterogeneity of the effect sizes is significant and this confirmed the validity of choosing the random-effect model in this study. Also, the Q-values in Table 3 are significant, indicating that all the relationships have significant heterogeneity. Therefore, we can further examine the existence of moderators that affect each pair of relationships.

4.2.1 Moderator Analysis

In this study, the economic development level of countries or regions was used as a moderator. The selected 54 studies were divided into two subgroups to conduct moderator analysis, and the Z-score was calculated to see if there was a significant difference between the two subgroups (Cohen & Cohen, 1985 ; Preacher, 2002 ). Due to the limited number of selected articles, the relationships between website security, novelty, interactivity, negative emotion, scarcity, and online impulsive buying were not examined because only one developed country’s data had been collected. Therefore, only 8 pairs of relationships could be analyzed. Table 4 shows the moderator analysis results.

First, focusing on each subgroup (developing or developed in each pairwise relationship), most of the confidence intervals excluded zero except the relationship between ease of use and online impulsive buying in the developed subgroup, price and online impulsive buying in both subgroups, and positive emotion and online impulsive buying in the developed subgroup. After scrutinizing the raw data, we speculate that the insignificance may be caused by too few studies in the subgroup and the large variance of correlation coefficients within these subgroups.

Second, the moderator analysis indicated that the economic development level moderates 6 pairwise relationships. The differences in subgroups caused by the economic development level are significant in the relationship between website visual appeal, ease of use, price, promotion, pleasure, positive emotion, and online impulsive buying. Specifically, from Table 4 , the combined effect size of website visual appeal on online impulsive behavior in developed countries or regions is close to 0.5, significantly higher than that in developing countries or regions, suggesting that website visual appeal is a vital predictor for high consumption level in developed countries or regions. This phenomenon shows that shopping websites in developed countries or regions are mature. That is, compared with developing counterparts, the use of visual elements in developed countries is more ingenious and attractive. Similarly, the combined effect size of promotion is higher than that in developing countries or regions, which shows that consumers in developed countries or regions are more likely to be influenced by sales promotion to make impulsive online consumption than those in developing countries or regions. This may be explained by that in developed countries, companies are using big data techniques to design their online promotion strategy, which is more precise, so the promotion effect is better than that in developed countries. On the contrary, ease of use, price, pleasure, and positive emotion are more useful stimulating factors for online consumers in developing countries.

Besides, to visualize the effect of moderator analysis, this study used the forest plot to show the subgroups with significant differences. The 6 pairs of relationships (i.e., website visual appeal, ease of use, price, promotion, pleasure, and positive emotion) with significant moderator effects are shown in Fig.  4 (1)–(6) respectively.

(1) Website visual appeal, (2) Ease of use, (3) Price, (4) Promotion, (5) Pleasure, (6) Positive emotion. Note: The authors’ names, Melis Kaytaz Yiğit and Mehmet Tığlı, includes non-English characters which cannot be recognized by R 3.6.0, so this paper uses “?” as a substitute.

4.3 Publication Bias

The fail-safe N is used to test the publication bias. In Table 5 , the fail-safe N of all the relationships is greater than the corresponding “5*K + 10” (K is the number of studies) standard, indicating that publication bias is not a concern (Rosenthal, 1979 ).

5 Discussion

Based on 54 prior empirical studies on the influencing factors of online impulsive buying, this study conducted a meta-analysis to explore 13 main factors affecting online impulsive buying.

From the correlation analysis results, all factors are significantly and positively related to online impulsive buying except website security, price, novelty, and negative emotion. According to the statistics, the number of studies on insignificant relationships is no more than 5 and there is a great difference between the maximum and minimum of the correlation coefficient of each relationship. Hence, we speculated that the insignificant results may be caused by the limited number of studies and the large differences in the effect sizes reported in the selected studies.

H1-H5 assumed that the website stimuli factors have positive and significant relationships with consumers’ online impulsive buying. From the results, H2-H5 were supported, which is consistent with the previous studies. For example, Zou ( 2018 ) found that website navigability and website visual appeal are positively related to online impulsive buying among undergraduates. As for the website stimuli factors, the website security factor (H1) with the combined effect size of 0.32 is the most critical factor of online impulsive buying. However, the website security factor was not significantly associated with online impulsive buying. H1 was not supported, which may be attributed to the small number of studies selected.

H6-H9 proposed that marketing stimuli factors have significant positive relationships with online impulsive buying. H6 (scarcity) and H9 (promotion) are supported, but H7–8 (price and novelty) are not supported, which may be explained by the limited samples.

With regard to the affective stimuli factors, the results indicated that arousal, pleasure, and positive emotion have a significant positive relationship with online impulsive buying, supporting H10-H12. The results are consistent with previous studies. For example, Lin and Lo et al. ( 2016 ) found that arousal and pleasure have a significant positive relationship with online impulsive buying. However, H13 (negative emotion) was not supported, which may be explained by the large differences in the effect sizes reported in the selected studies. It is worth mentioning that from the values of combined effect size, all affective factors are positively related to online impulsive buying at a moderate level. Among them, the combined effect value of pleasure, positive emotion, and promotion are greater than 0.4, showing their importance in online impulsive buying.

Finally, in moderator analysis, economic development level significantly moderated the relationship between website visual appeal, ease of use, price, promotion, pleasure, positive emotion, and online impulsive buying. H14 was partially supported. Specifically, consumers in developed countries or regions are more sensitive to the websites’ visual appeal and promotion. However, ease of use, price, pleasure, and positive emotion are more important stimulating factors for online consumers in developing countries or regions. The moderator analysis can provide some guidelines for cross-border e-commerce practitioners.

6 Conclusion

6.1 theoretical implications.

In recent years, impulsive buying has received wide attention from consumer behavior researchers. However, the current meta-analysis research on impulsive buying mainly focused on the offline market, and few studies have involved the unique factors of the online market. This study fills this literature gap by focusing on the factors that influence online impulsive buying, especially the IT-supported website stimuli. Meanwhile, this study can extend the influence of different factors on the impulsive buying of digital products. Digital products can only be sold through online channels. Thus, the conclusions of research on offline impulsive buying behavior may not apply to these products. Therefore, compared with the traditional offline impulsive shopping behavior research, this study can be applied to a wider range of product types.

To address the research bias caused by inconsistent findings in the existing research results on online impulsive buying, this study conducted a meta-analysis to integrate 54 empirical studies and proposed a comprehensive framework for studying the influencing factors of online impulsive buying based on quantitative statistical analysis. In this research, 54 relevant empirical studies were analyzed. From these articles, we selected the factors that have been explored three times or above, and finally focused on 13 main factors. Particularly, factors were classified into the following three categories: website stimuli (website security, website navigability, website visual appeal, interactivity, and ease of use), marketing stimuli (scarcity, novelty, price, and promotion), and affective stimuli (arousal, pleasure, positive emotion, and negative emotion). According to the results of the study, these factors are significantly and positively related to online impulsive buying except website security, price, novelty, and negative emotion.

In addition, this study also verifies the applicability and effectiveness of meta-analysis methods in the field of information systems, providing new ideas and methods for related research. To our knowledge, this study is the first attempt to conduct a meta-analysis to study online impulsive buying. This study takes full advantage of the meta-analytic approach to expand the limited small sample of a single independent study into a large sample of data to verify the relationships of variables in existing empirical studies at a higher logical level. It provides effective research ideas and methods to clarify the sources of heterogeneity, avoid potential measurement errors, and then propose more credible and robust research conclusions. The results of our study will provide a theoretical basis for future research on online impulsive buying.

Further, this study selected economic development level as a moderator and categorized the sample into developed and developing countries or regions to explore the moderating effect on the relationship between influencing factors and online impulsive buying. The results of the moderator analysis illustrate that the economic development level has a significant moderating effect on the relationship between website visual appeal, ease of use, price, promotion, pleasure, positive emotion, and online impulsive buying. Specifically, in terms of website stimuli factors, consumers in developed countries or areas attach more importance to website visual appeal. Nevertheless, for consumers in developing countries or areas, the ease of use of websites is a more important factor to trigger their impulsive buying intention or behavior compared with consumers in developed countries or regions. With regard to marketing stimuli factors, consumers in developed countries or areas pay more attention to promotional factors, while consumers in developing countries or areas are more susceptible to price factors. For affective stimuli factors, this study confirmed that consumers in developing countries or areas are more likely to be stimulated by pleasure and positive emotion factors and induce online impulsive buying. With the moderator analysis, this study provides implications for future research on online impulsive buying behavior across different economic development levels.

6.2 Practical Contributions

This study also provides managerial insights for practitioners of e-commerce websites. First, managers of online stores should pay attention to the website stimuli, marketing stimuli, and affective stimuli, and take steps to optimize consumers’ online buying experience. Specifically, in terms of website stimuli, IT capacity should be enhanced. To enhance the visual appeal of websites, the navigability, interactivity, and ease of use of websites, e-commerce websites should improve the interactive design of websites and website performance. Online shopping websites can use VR and AR technology to provide a more interactive consumer experience so as to stimulate consumers to buy. For example, many online clothing retailers have begun to launch AR/VR applications such as AR shoes and virtual reality fitting mirrors. This allows customers to try on clothes virtually, greatly improving customers’ shopping experience. Besides, checking the updating of the system and software patch regularly can help to improve the consumer experience, and it would be helpful to upgrade the professional network security firewall to improve website security. With regard to marketing stimuli, besides using traditional promotion activities to persuade consumers into consumption, managers can use IT-enabled inventory availability to underline product scarcity as well as use big data techniques to conduct precision marketing. Therefore, e-commerce websites may employ hunger marketing to induce impulsive buying, use inventory availability information to emphasize the scarcity effect and maximize the marketing effect by targeting consumers. Specifically, hunger marketing is to give a surprisingly attractive price with limited quantity, creating the illusion of hot sales in short supply. Thus, companies can not only benefit from the raised price but also make the brand more appealing. Finally, for affective stimuli, managers should: (1) keep the purchasing flow smooth to provide consumers with a pleasant online buying experience, (2) design an easy-to-use interface to keep users in a good mood when shopping online, and (3) provide customized service and precision marketing to increase consumers pleasure or arousal level. For example, to provide a better shopping experience, online stores should provide quick and valid responses to consumers’ questions and requests to improve the feedback efficiency and recommend products according to consumers’ consumption history and characteristics.

Second, the moderator analysis can help cross-border e-commerce. Concretely, cross-border e-commerce practitioners should pay more attention to website visual appeal and promotion activities in developed countries or regions to increase the likelihood of online impulsive buying. In contrast, in developing countries or regions, to provide users with a pleasant buying experience, more efforts should be put into website performance optimization and product pricing strategy, simplifying the website operation process and improving user satisfaction. In short, managers should develop targeted strategies to stimulate consumption according to the economic condition of the area and the consumption level of the consumers.

Finally, with the rapid development of Internet technology and the prosperity of e-commerce, the proportion of online shopping in retailing is increasing steadily. Meanwhile, in the context of the COVID-19 pandemic, to avoid cross-infection, more and more consumers tend to buy goods online. Our research focuses on the factors that influence online impulsive shopping by improving consumers’ shopping experience, which has strong significance for serving consumers better in the context of the COVID-19 pandemic.

6.3 Limitations and Directions for Future Research

Despite attempts to conduct this meta-analysis rigorously, there are still some limitations. First, the number of relevant studies that can be used for the meta-analysis of online impulsive buying is limited. Moreover, some online impulsive buying studies were excluded because they did not provide the necessary data for statistical calculation.

Second, this study emphasized website-related factors. Although we have included the most investigated antecedents of online impulsive buying in the meta-analysis, there are other factors that may also have a significant effect. Future research could include more factors, such as consumer personality.

Third, due to the data availability issue, we took the economic development level as the proxy of consumers’ consumption level, which may not be as accurate as the real individual-level data. If possible, future research can employ individual-level data.

Fourth, due to the fact that some articles included in the meta-analysis suffer from poor data quality, for example, the collected data may be sparse, or the sample is biased, we do not have enough data to analyze the moderating effects of age, product type, context, etc. However, the moderating effects of these factors are worth studying. Therefore, in the future, researchers can conduct more in-depth and detailed studies on these factors to find boundary conditions.

Finally, only quantitative studies were used in the meta-analysis. Future research could consider weight-analysis that allows the inclusion of qualitative studies while evaluating the strength between antecedents and consequences. Moreover, researchers can attempt to use the structural equation modeling technique to test the relationship in and out of the study.

In the process of meta-analysis, factors that have been studied three times or above in selected research were extracted according to the guidance of Rana et al. ( 2015 ). Therefore, constructs with few empirical studies and insufficient research were not included in this meta-analysis.

It is worth noting that there has been no consensus on the definition of developing and developed countries and regions. The reference standard we used is the data published by The World Bank ( 2015 ). Please refer to Appendix 2 for the specific classification of the countries or regions of the selected article samples.

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Zhao, Y., Li, Y., Wang, N. et al. A Meta-Analysis of Online Impulsive Buying and the Moderating Effect of Economic Development Level. Inf Syst Front 24 , 1667–1688 (2022). https://doi.org/10.1007/s10796-021-10170-4

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Online Food Shopping: A Conceptual Analysis for Research Propositions

Shopping foods online is different from shopping other things online. To stimulate more thinking and enrich potential future research imagination, this paper reviews for online food shopping features, offers a commentary, and proposes future research directions. The propositions include the following: (1) The design and implementation of online food shopping (eco)systems should engage the consumers and other stakeholders to co-create collective and social values; (2) A better fit between technologies’ and food businesses’ natures could generate better applications for online food shopping; (3) A business model with sound finance systems becomes the core of a healthy online food ecosystem; (4) The interaction and transformation between online (virtual) and offline (virtual) food businesses determines the dynamic development of future food shopping.

Introduction

Most studies on online shopping focus on the implications and benefits of e-commerce. This focus is expected to increase as more people are pushed toward shopping online in a bid to avoid crowded shopping malls for fear of contracting the dreaded COVID-19 virus. A gap in the literature, however, is that while the topic is rife with studies detailing how online shopping works, there is limited research on shopping foods online, which is inherently with very different characteristics from buying other kinds of commodities via the World Wide Web. Nonetheless, food is one of the most common products for the mankind, and so are with great impact for human’s online shopping life. A critical analysis for in-depth understanding of the special attributes that online food shopping has can facilitate the construction of a precise (for stakeholders’ needs) and high-quality (for stakeholders’ safety and satisfactions) online food shopping ecosystem. This paper presents a conceptual analysis aimed at explicating the significant themes within the current literature. The review will conduct critical propositions reflected from these studies to propose future research directions. The academic review is significant to both researchers and online food stores as people across the world start embracing online shopping more than ever before.

Background Descriptions

Before beginning the conceptual analysis with literature review, a broader background discussion is needed. Practically, the broader background constitutes: e-commerce platforms, consumer preferences and attitudes, marketing approaches, and packaging and delivery considerations.

E-Commerce Platforms

Silva et al. (2017) define e-commerce platforms as the set of technologies designed to help online businesses to manage their marketing, sales, and operations. Wei’s et al. (2018) study sought to examine the purchase intention of fruits among online shoppers. The authors argue that the past few years have seen the emergence of online purchase platforms for fruits, a trend that has significantly advanced e-commerce development and improved the quality of human life. Although their study sought to investigate consumers’ purchase intention, the results reveal that compared to other products, the e-commerce platforms for fruits did not play a major role in influencing a buyers’ purchase decision. On the contrary, the success of fashion products and electronics is dependent on how online customers perceive their e-commerce platforms ( Huete-Alcocer, 2017 ). For example, customers are less likely to purchase luxury fashion products from a poorly designed website ( Kang et al., 2020 ) and ( Buckley, 2016 ). Thus, while there are limited studies on the differences between buying food and other products online, at least the current studies evidence that e-commerce platforms do not play a significant role in influencing buyers’ purchase decisions.

Consumer Preferences and Attitudes

Kim Dang et al. (2018) study on consumer preference and attitudes regarding online food products examines how the Internet has changed people’s food-buying behaviors. The study is significant because it establishes the underlying consumers’ concerns with regards to food safety information, especially for online food products. Compared to other products, consumer preferences and attitudes toward buying food online differs in that the perceived risks and information quality do not play major roles in influencing their buying behavior ( Li and Bautista, 2019 ; Sanchez-Sabate and Sabaté, 2019 ; Zieliñska et al., 2020 ). Kim Dang et al. (2018) study relies on a cross-sectional study conducted in Hanoi, Vietnam. The findings are reliable as they are based on responses gathered from over 1736 customers through face-to-face interviews. While the preferences and attitudes toward buying food online differ from buying other commodities, Kim Dang et al. (2018) note that the laws governing e-commerce in Vietnam are the same. As such, the findings provide practical advice to online food retailers and the Vietnam government on how to implement appropriate legislation with regards to trading online food products.

Martínez-Ruiz and Gómez-Cantó’s (2016) study emphasizes that using the Internet to seek food service information has now become a common practice among people today. More people than ever before have positive attitudes toward finding information about food online ( Martínez-Ruiz and Gómez-Cantó, 2016 ; Maison et al., 2018 ). Also, people are more likely to search information about food on the Internet than any other product or service ( Hidalgo-Baz et al., 2017 ; Whiley et al., 2017 ; Wong et al., 2018 ). However, Kim Dang et al. (2018) study found that a significant number of consumers were unconcerned about the accuracy of the evidence regarding food safety they found online in selecting food products on the Internet. The conclusions drawn from the current article review produces practical pieces of advice to consumers buying food online as well as the food retailers selling food over the Internet.

Marketing Approaches

Rummo et al. (2020) examine the relationship between youth-targeted food marketing expenditures and the demographics of social media followers. The authors sought to establish the extent to which teenagers follow food brands on Twitter and Instagram by examining the relationships between brands’ youth-targeted marketing practices and the overall percentages of adolescent followers. The study provides evidence showing that unhealthy food brands, especially fast food and sugary drink have more adolescent followers on social media ( Rummo et al., 2020 ). These study results are consistent with Salinas et al. (2014) findings which show that unhealthy food products enjoy a higher market base than the healthy ones. The high percentage of teenage followers is concerning among health experts mainly because most of the advertisements from these companies are biased and do not highlight the unhealthy consequences associated with eating these foods. Compared to other products, food companies are often not required by regulations to highlight their negative consequences ( Salinas et al., 2014 ). For example, cigarette and alcohol companies are mandated to disclose their effects of use on all marketing materials ( Gravely et al., 2014 ). Consequently, with the ubiquitous use of social media by teenagers, young people are more exposed to food and beverage advertising which occurs across multiple digital channels.

The failure to address digital advertising when formulating policies makes it harder to governed youth-targeted food marketing. Food products are often marketed using the general techniques and approaches applied in other products and services. Juaneda-Ayensa et al. (2016) note that food marketing topics such as market segmentation, strategic positioning, test marketing, branding, consumer research, targeting, and market entry strategy are highly relevant. Moreover, food marketing is affected by the major challenges that affect conventional markets such as dealing with perishable products whose availability and quality varies as a function of the current harvest conditions ( Hongyan and Zhankui, 2017 ). However, Topolinski et al. (2015) note that the value chain in food marketing is particularly important because it highlights the extent to which sequential parties within the marketing channel add value to the final product. According to Linder et al. (2018) processing new distribution options often provides additional opportunities available to food marketers to provide the final consumer with convenience. However, when overhead costs such as marketing and processing are added they result in significantly higher costs ( Lou and Kim, 2019 ).

Demographics play an essential role in food marketing almost more than any other product. According to Qobadi and Payton (2017) , food companies must utilize statistical demographics to understand the inherent characteristics of a population. For food marketing purposes, such knowledge can help firms develop a better understanding of the current market place as well as predict future trends ( Isselmann DiSantis et al., 2017 ). For example, with regards to the current market, food companies interested in entering a new market with sports drinks might first study the overall number of people between the ages of 15 and 35, who would constitute a particularly significant market. In such cases, most food companies often prefer shifting their resources toward products consumed by a growing population. As such, the success of the marketing strategy employed by a food company is contingent on how good it studies the demographical makeup of its target market.

Packaging and Delivery Consideration

One of the primary consideration food consumers take into account when making a purchase decision online involves packaging and delivery. According to Chen et al. (2019) , the modern consumer is more interested in food products that utilize sustainable packaging and delivery systems. Hu et al. (2019) add that most customers today are more focused on recyclable packaging systems. Grace (2015) further notes that sustainability is one of the primary sustainability attributes online shoppers look for. For example, over 33% of online consumers believe that packaging and recyclability are more important to them when ordering food items online ( Gutberlet et al., 2013 ). Additionally, 13% of online shoppers cite a lack of packaging information available online, which suggests that there is an existing opportunity for e-retailers to increase their sustainability information ( Quartey et al., 2015 ).

As the world continue grappling with the COVID-19 pandemic, online purchases for fresh food is gradually becoming the norm across the world. As such, food producers must be able to adapt accordingly to take advantage of the emerging market. However, the majority of consumers are still concerned about freshness and food waste ( Yu et al., 2020 ). Unlike in a brick-and-mortar store where shoppers can visibly check the freshness of their produce, this is more difficult with home delivery ( Song et al., 2016 ). Thus, brands must try and opt for packaging that can keep food safe and fresh during transit and displays its freshness to re-assure customers. Moreover, to meet sustainability goals, fresh food brands need to balance the use of more sustainable, recyclable materials, with packaging that continues to extend shelf life and avoid food waste.

Conceptual Analysis for Future Research Propositions

The article review shows that sufficient studies have been conducted on online food shopping. As more people start shopping online, the number of articles on online food shopping is expected to increase. However, despite studies on online food shopping and business models remain rife, there are key gaps in research. These gaps are a result of the majorities of the researchers’ focus on highlighting their perspectives and largely ignore those of the consumers and businesses. Moreover, these studies do not consider crisis (e.g., COVID-19 pandemic) when making these future predictions. The forecasts made about future help in developing a better understanding of the various implications of ordering via mobile apps. Also, it provides a background for examining the emerging technologies in online food ordering. As such, the critical propositions reflected in the literature review propose the following four future research directions.

Value Co-creation With Stakeholders

From a business perspective, getting partners and investors on board is not easy and most restaurants tend to stay away from technology. Thus, the preposition made involves conducting research aimed at developing a better understanding of the customer and business’ perspectives. According to Chen et al. (2018) , setting the commission rates with restaurants is a major problem within the online food industry. Moreover, the majority of startups are depended on restaurants to deliver food at the customer’s doorstep ( Onyeneho and Hedberg, 2013 ). Hwang et al. (2020) argue that relying on technology is not the main focus of a restaurant because preparing food is its main core business. As such, even if an investor trusts a food startup, integrating technology within its business process will always be perceived as a high risk. The lack of sufficient evidence on the business’ perspective toward technology and online platforms make it more difficult for rescuers to tailor their studies to generate crucial insights that help in making better business decisions.

One of the problems identified from the consumer’s perspective is that most of the things mentioned in the online food menus are often not available. Instead, they act as click baits designed to entice online users to continue interacting with their platform and marketing content ( Lara-Navarra et al., 2020 ). In rare cases, some clickbait links often forward online users to pages that require them to make payments, register, or even fill in their payment details. Consequently, a significant communication gap exists between consumers and restaurants while shopping on phone and online. While numerous studies examine the purchase intention of food among online shoppers, few highlight the inherent challenges experienced by consumers as they go about their day.

While it is crucial to investigate both perspectives, more studies need to be conducted on the customer ones. This is because most online businesses often find it difficult to deal with customers, but Ho et al. (2014) note that this is usually because they do not see things from the buyers’ point of view. The authors, however, refutes the popular phrase that “customer is always right” and notes that even when they are completely wrong, they can always win. For example, customers can criticize a business online or even refuse to pay their bills. As such, failing to grasp a customer’s perspective can result in a meltdown with them which is always bad business. It is also essential for future businesses to take into consideration the fact that work is much more enjoyable and profitable when people work alongside the customer rather than against them. Thus, conducting more studies aimed at understanding customers can help develop the necessary recommendations to help businesses see things from their point of view.

One of the ways future studies can explore to better understand the customer’s perspective involves exploring the issues related to empathy. Charles et al. (2018) note that empathy does note naturally to most people but it reinforces one’s ability to understand and share the feelings of a customer by placing themselves in their shoes. Future studies should highlight how online businesses can ask questions about how their current and potential customers would feel in different circumstances. Also, future studies must examine how well online businesses can listen to their customers. Afshar Jahanshahi and Brem (2018) notes that the first step in customer relations involves actively listen to them. Finally, future studies must be able to provide recommendations on how online food businesses can grow trust and show respect to their customers. The prepositions made with regards to the business and customers’ perspective provides the background information for future studies. Also, bridging the current research gaps will help business adopt a more effective online model that maximizes customer satisfaction when purchasing foods. Based on the discussions above, this article suggests the following proposition to both identify the gap in the literature and the corresponding future research directions.

Proposition 1: the design and implementation of online food shopping (eco)systems should engage the consumers and other stakeholders to co-create collective and social values.

Technological Nature

Although smartphone apps provide an efficient way to replace the conventional methods of ordering food through a phone call, there lacks sufficient evidence on the implications of placing orders through them. A partial but potentially important reason is the lack of in-depth and broader understanding of the technology per se . Mobile ordering apps have caused a significant change in food delivery and pickup business ( Onyeneho and Hedberg, 2013 ). With more and more retailers and restaurants adopting these technologies, the modern consumer is willing to place fewer delivery and pickup orders through their phones. Instead, they are now opting to utilize mobile apps. Studies aimed at exploring the implications of food delivery apps help in establishing whether it is hurting or assisting the business. Thus, as a restaurant owner, one has to be careful with regards to utilizing third-party services to do business. For instance, apps such as Uber Eats have endless possibilities as they make delivery faster, for both the customers and the business. However, future studies must examine the potential disadvantages to using such third-party services. Firstly, the added cost of a food delivery app may be prohibitive to most customers. For example, the cost of using services like Uber Eats changes how businesses price their meals. In the end, customers are likely to end up paying more. Thus, future studies have to consider this fact when developing recommendations on how businesses can use food delivery apps without undermining their financial positions. Also, these studies will help show how customers are likely to react to a price surges.

Subsequent studies on the implications of ordering food through mobile apps should also focus on the issues relating to control and accountability. Cecchi and Cavinato (2019) note that some customers have complained about being unable to control the food ordering process. For example, once the customer’s food is in the possession of the Uber driver, there is little left for them to do, which is perceived as a bad thing. Also, Isoni Auad et al. (2018) note that customers lack control over how their drivers handle their food. One of the consequences of being unable to control the process is that when a customer’s food is mishandled or ends up late, the restaurant is the one that is held accountable. Finally, with regards to the implications, future studies must monitor their third party service to safeguard their brand’s reputation. As such, subsequent studies need to ensure that they highlight the importance of maintaining an effective brand image. Mao et al. (2018) recommend online food businesses to monitor how long it takes their delivery people to transport their customers’ food to establish whether it is being handled with the necessary care it deserves. However, more studies are required to highlight the customer’s grievance which can easily fall on the businesses when the delivery issues are ignored.

Despite the various implications of using mobile apps to order food online, there are numerous benefits associated with online models. As such, as the growth of online applications continues, the subsequent studies need to add to the existing literature on the benefits businesses are likely to accrue from adopting such technologies. According to Li et al. (2020) , this trend is a result of the numerous benefits associated with using the apps compared to the conventional methods of shopping over the phone or waiting in line. These benefits are 2-fold, they include benefits to the consumer and the restaurants. Firstly, there are numerous consumer benefits of using mobile ordering apps to purchase food.

Consumers across the world are downloading mobile ordering apps at lightning speed. For example, When Chick-fil-A, one of the largest American fast food restaurant chains, released its first official app, it reached first place in the app store in only 3 days after it was launched. Mayordomo-Martínez et al. (2019) note that these apps are popular for four main reasons. Firstly, customers feel that no one is waiting in line or getting put on hold. Secondly, customers can pick up food on the go. Thirdly, customers get the whole menu right at their fingertips, including items they may not have known existed. Finally, most restaurants award patrons’ loyalty reward points. In most cases, these points are easy to track directly through applications and lead to big savings if the customer order frequently.

The restaurant benefits from the mobile ordering apps too. While these apps may be created for the customer, they achieve some important objectives that can greatly help out the restaurant or retail store as well ( Ferguson and Solo-Gabriele, 2016 ). For example, they can handle more orders as is the case with Chipotle, an American chain of fast-casual restaurants, which claims that it is capable of processing six additional orders every hour when placed through a mobile app ( Ferguson and Solo-Gabriele, 2016 ). Moreover, customers are more likely to spend more through an ordering app than in person because they have more time to decide since the entire menu is in front of them and they typically want to score more reward points. Based on the discussion above, this article made the propositions as follow.

Proposition 2: A better fit between technologies’ and food businesses’ natures could generate better applications for online food shopping.

New Business Models and Finance Systems

Although numerous studies have highlighted the various emerging trends in buying food online, most were conducted before the COVID-19 pandemic. As such, future studies need to capture how the pandemic has affected the online ordering industry. Such studies will provide the insights necessary to help the business withstand emerging competition as well as keep up with the ever-changing customer demands and the latest trends and technological advancements. Wang et al. (2020) note that the various responses to the COVID-19 global pandemic will shape the online food delivery industry in 2020 and beyond. Thus, future studies need to identify and critically examine the top online food shopping trends that customers and businesses must remain aware of.

For the better part of the year 2020, global cities have become deserted and shopping malls closed. The restaurant sector is one of the most affected as people are recommended to maintain social distancing and remain at home. As the Coronavirus continue spreading across the world, the pandemic is projected to have more economic implications than undermine global health. Thus, future studies must offer people a glimpse of how lockdowns will affect the online food industry, which is hailed as the future in the restaurant sector. However, even at the current stage of the Covid-19 pandemic lifecycle, several lessons are already emerging from China with regards to how people can cope with the commercial and social disruptions. For example, the pandemic is a key driver for digital technologies.

There are three areas that future studies need to focus on. They include the emergence of digitally enabled delivery systems and consumer comfort with the online food sector. Firstly, the prevalence of digitally enabled delivery systems is expected to grow in the coming years. As such, studies are needed to develop a better understanding of how these online delivery systems will affect the food industry. For example, since the COVID-19 pandemic began, more people than ever before purchase their groceries and other food items online ( Hua and Shaw, 2020 ; Zhang and Ma, 2020 ). This is mainly a result of the growing deployment of digital technologies across the country in an attempt to limit interactions among people and mitigate the spread of the virus. Secondly, subsequent studies must examine the factors affecting consumer comfort within the online world. It is projected that in the next decade, online platforms will transform people’s purchasing behaviors, especially with regards to acquiring food items. Thus, studies are needed to help businesses identify the existing opportunities and mitigate the main threats likely to undermine growth within the online food ordering business. Last but not least, more detailed academic investigation and practical development of payment mechanisms are needed. By its nature, payment mechanisms deal with technological development of payment methods and techniques that constantly try to improve user convenience and experiences of payments. Hence, existing discussions/examinations relied heavily on technical aspects of payment mechanisms (or schemes). However, technologies in business world can generate implications beyond technical dimension, but also in the social, cultural, psychological, and/or even political dimensions (e.g., Yang et al., 2012 ; Koenig-Lewis et al., 2015 ; Nelms et al., 2017 ; Verhoef et al., 2019 ). Hence, interdisciplinary works, either conceptual or empirical, can contribute to the literature for analyzing on more complex dynamics of online payment – not just about the technology/system per se , but also about the ecosystem composed of human, system, and knowledge in it. In sum, the discussions in this section emphasize the importance of business models with high-quality finance (e.g., payment) systems. This article makes the following proposition.

Proposition 3: A business model with sound finance systems becomes the core of a healthy online food ecosystem.

Online-Offline Interactions and Transformations

Shopping food online is viewed by most researchers as one of the biggest disruptions in the supermarket and grocery business models. From smaller stores to fewer discounts and more service and robots, these are just a few of the changes brought about by online platforms ( Kuss and Griffiths, 2011 ). The problem is that few studies are examining whether new disruptions will continue emerging or whether the online food sector has reached maturity. Such studies are necessary because they will help manufacturers and retailers react accordingly. These studies can focus on trying to understand how consumers can purchase food in the future, which can be online or in physical stores or from larger or smaller stores. Some of the research questions can focus on establishing whether future customers will continue buying to take dine at home or consume right on the spot.

Despite the numerous uncertainties, with regards to brick-and-mortar stores, Burgoine et al. (2017) note that they may survive even with the growth and prevalence of online businesses. As such, future studies must explore how changes in e-commerce will affect shoppers and online businesses. Such studies are essential because the current findings on consumer behavior seem to suggest that customers prefer interacting at a physical store by seeing, smelling, and even touching products they find there. Moreover, there is an immediate satisfaction when a customer picks up something. The insights generated from such studies can help retailers establish the inherent need to focus their attention on emotional elements as well as create unique experiences.

Studies focused on making future forecasting will help in understanding how online food platforms can achieve the social roles enjoyed by supermarkets. Otten et al. (2017) note that supermarkets increasingly place their shopper firsts and tap into their individual needs in an attempt to mitigate the rising competition from online shopping. As such, studies must thoroughly analyze the existing demographic data to make future predictions on whether the online food ordering platforms can ever enjoy the same social roles which are currently the precincts of supermarkets. Finally, a sufficient number of studies have predicted that artificial intelligence and robots are likely to take over the responsibilities of human beings within the online food sector. However, while most of these studies make future predictions, they do not take into account how automation and artificial intelligence will help online supermarkets to become more efficient. Thus, subsequent studies should establish a balance between human interaction and automation. This article makes the following proposition according to the discussions here.

Proposition 4: The interaction and transformation between online (virtual) and offline (virtual) food businesses determines the dynamic development of future food shopping.

The majority of studies examining online food shopping have provided sufficient evidence highlighting both the implications and benefits of e-commerce. However, most of these studies generalize all forms of online shopping and ignore the fact that shopping foods online is inherently different from buying other commodities. As such, the comprehensive academic review conducted helps at explicating the significant themes within the current literature. Hence, the critical propositions that reflected from these studies help in proposing the following four future research directions. They include conducting studies to highlight the customer and business’ perspectives, making future predictions, understanding the implications of ordering via mobile apps, and examining the emerging technologies in online food ordering. The academic review and prepositions made are significant to both researchers and online food stores as people across the world start embracing online shopping more than ever before.

Theoretical Implications

To generate theoretical implications in a more holistic and comprehensive level, this article focuses on the inter-relationships between the four propositions derived after our conceptual analysis. To recall, the four propositions are inherently about: engaging stakeholders to co-create values, in-depth understanding of technological natures, well-designed business models and finance systems, and online-offline dynamics. One suggestion for future research directions is to develop a holistic-view, often qualitative investigation of a online food shopping ecosystem that composes of interested stakeholders operating with diverse technological sets embedded in well-designed business models that simultaneously incorporate concerns of both online and offline developments of food shopping. Complexity is a point to be explored but is often oversimplified if we could not take a eco-systematic perspective and analyze for both qualitative-quantitative data sources. For a better theoretical development and practical design, the complexity of a food shopping ecosystem can help identify research questions, sketch phenomenon structures and elements, as well as specify heterogeneous interests for policy making. Following this point, another suggestion for future research directions is to address established issues/research questions through cross-disciplinary explorations. As has been discussed, complexity characterizes modern food shopping system, especially the online one. To explore in-depth knowledge of complexity, single disciplinary system of thoughts might limit the imaginations one can create. A cross-discipline approach for studies on online food shopping can both offer fresh explanations for unanswered questions or that in tension, and also help identifying unnoticed phenomenon for further exploration.

Practical Implications

For online retailers, conceptual analyses and the four resulting propositions can generate practical implications too. First, when designing a online food shopping business/system, practitioners need to adopt an ecosystem viewpoint to prevent incomplete thinking and ignorance of any stakeholder’s opinion. Second, practitioners need to take care of the interfaces between the virtual and physical sub-systems even if it is an online food shopping ecosystem. By considering the interfaces between the sub-systems, not just connection and coordination works would be cared about, but also transformation work should be articulated. For example, the transformation of values in the process flows between material (e.g., food products), informational/technological (safety labels; blockchain applications in supply chain communications; human-machines interface in online purchase procedures, etc.), financial (budgeting; pricing; payment, etc.), human (i.e., stakeholders), and other sub-systems should be implemented with a fully consistent and engaging logic.

Limitations

In nature, a conceptual analysis is done without empirical and original data collection. However, this article has tried to avoid such inherent limitation by conducting the conceptual analysis with as many practical examples as possible. Additionally, our analysis focuses on the online shopping for foods only. Future studies can also take a similar approach but discuss other characterized industries, such as online shopping for precious metals, intangible services, and so on. Also, our focus on food is limited to foods in general. Future studies can be more detailed, by characterizing more for different food categories (e.g., organic vs. non-organic foods).

Author Contributions

C-FL was the major author of this article. C-HL reviewed and revised the manuscript. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Online Grocery Shopping Adoption: A Systematic Literature Review

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