Flow state is an important theory to understand the consumer behavior of e-commerce. Perceived risk also has been object of academic studies because it is an inhibitor of the online purchases. In this way, the objective of this research is to investigate the relation between the perceived risk and flow state. Through a structural equations modeling, it was disclosed that the ability of the consumer with the use of the Internet intervenes with its perception of risk.
The researcher’s interest about online consumer behavior is growing [
It is important that the virtual companies know how to attract and to conquer the individuals during purchases on-line. Understanding flow theory is an important active to manage an online environment.
Csikszentmihalyi [
However, consumers may perceive several risks in the interaction between them and the machines (computer) [
Once Smith and Sivakumar [
The next item presents the theoretical revision of this study.
Online purchases can unchain some new types of risks, per example, the risk of the privacy and the transmitted information security [
Time is another risk that occurs in Internet purchase. Consumers must wait the delivery time to receive the product and if the consumer receive a wrong product he has just waste his time [
Perceived risk is not the only existing subject in the literature that helps to understand the behavior of the consumer in the Internet. Flow is another theory that is growing in marketing studies.
According Hoffman and Novak [
Interactivity is the easiness to use the website. Novak, Hoffman and Yung [
Telepresence occurs when the consumer fells the virtual word (website) as a physical world [
There are three more dimensions; the challenge and arousal, both regarding to the attitude of the costumer and the attention focus [
When the consumer is concentrated in the activity, it loses the time notion and all the irrelevant thoughts are disrespected, taking the individual to the loss of conscience, of notion of space and time [
Our objective in this paper is to understand the relation between the perception of risk and the state of flow considering previous studies as of Smith and Sivakumar [
Smith and Sivakumar [
In the first purchase, the existence of the risk takes the individual to be more intent in the hour of the purchase leading to the experience of flow state. The experience with the online purchase makes the individual to have a bigger ability and low sensation of challenge, being diminished the risk perception and reducing the possibility of the consumer to experience the flow [
Regarding to repeated purchases by Internet, Mathwick and Rigdon [
Based on literature, we understand that it is important to test empirically if there is any relationship between perceived risk and flow state. Our researches hypotheses are presented in
This study has a descriptive characteristic [
The questionnaire was composed by: Likert scale to measure perceived risk and flow state and semantic differential scale to measure the flow dimensions [
Next topic explains the analysis procedures.
. Hypotheses
Hypotheses | Source | |
---|---|---|
H1 | Perceived Risk is correlated with consumer ability on e-commerce. | Koufaris [1] , Bhatnagar and Ghose Bhatnagar and Ghose (2004), Doolin et al. Doolin et al. (2005) |
H2 | High risk perception on e-commerce leads to flow state. | Dailey Dailey (2004), Delespaul et al. Delespaul et al. (2004), Mathwick and Rigdon Mathwick and Rigdon (1998), Smith and Sivakumar Smith and Sivakumar (2002). |
. Scales
Theory | N˚ Items | Scale Used | Source |
---|---|---|---|
Perceived Risk | 8 | Likert | Kovacs and Farias Kovacs and Farias (2005) |
Flow State | 12 | Semantic Differential | Novack, Hoffman and Yung Novak et al. (2000) |
Flow Dimension (ability) | 20 | Likert |
The techniques used for the data analysis was: descriptive statistics, factorial analysis and modeling of structural equations. Initially it is necessary to identify the profile of the sample. The sample was composed mainly for men (53.8%), with some superior formation (71.8%), with age between 18 and 29 years (55.1%) and with average income between 6 and 10 minimum wages (27.2%). With regard to the daily time of connection with the Internet, it was observed that the majority of the searched ones is up to 4 daily hours connected (51%).
The next stage consisted of carrying through a confirmatory factorial analysis with the used scales. Due to the sample size, Hair et al. [
The first analyzed scale is relative to the perceived risk. The gotten KMO was equal to 0.853, qui-square of 477.663, 21 degrees of freedom and significance 0.000. The summary of the analysis can be observed in
The next scale is regarding to the flow. The gotten KMO was equal the 0.469, qui-square of 14.078, 6 degrees of freedom and significance 0.029. The result of this analysis can be observed in
Last scale is regarding to the ability. The gotten KMO was equal the 0.807, qui-square of 1399.647, 190 degrees of freedom and significance 0.000. The result of this analysis can be observed in
Several items had been excluded because they had not gotten the demanded minimum factorial load.
From the factorial analyses, 03 latent factors had been identified: Risk, Flow and Abilities. This variable will be the base to construct the path diagram tested in the structural equations modeling. The following item pre- sents the structural equations modeling.
A basic characteristic of the structural equations modeling is the possibility to test a theory of causal order between a set of variables. In such a way, the technique proposal offers to the researcher the possibility to investigate the dependent variable in such a way as independent [
The technique also adopted allows calculating props up them factorials for the latent variable, what it is possible when the cases for the calculation of the model are used directly. Two types of variable in a model (path diagram) of structural equations modeling exist: endogenous and exogenous variables.
The values of the endogenous variable are explained by one or more endogenous variables of the model. The values of the exogenous variable are assumed as given, that is, the model does not try to explain them. This distinction is similar to the made one between dependent variable (endogenous) and independent (exogenous) of the regression analysis [
Path diagram tested here can be visualized on Picture 1. The observed indices of adjustment (fit)-CFI (0.913) and RMSEA (0.061)-are valid [
We observed that hypothesis 1 was confirmed, therefore with a significance of 0.000 is possible to conclude that the ability influences in the perceived risk of the consumers of e-commerce. The second hypothesis was rejected. According the structural equations modeling there is no significant relationship between the flow and the perceived risk. The next topic presents the conclusions of the study.
Analysis has showed that the relation indicated on Hypothesis 1 was confirmed. This result confirms Koufaris
. Perceived risk factorial analysis
Item | Factor (Risk) | Cronbach’s Alpha |
---|---|---|
Not receive the product | 0.812 | 0.730 |
Not receive in the stated period | 0.775 | |
Improper debit on credit card | 0.763 | |
Receive a false product | 0.755 | |
Invasion in the computer | 0.715 | |
Loss of Privacy | 0.692 | |
No risk perceived | −0.412 |
. Flow state factorial analysis
Item | Factor (Ability) | Cronbach’s Alpha |
---|---|---|
Not Interested | −0.764 | 0.609 |
Relax | −0.750 | |
Glad | 0.506 | |
Attention | 0.467 |
. Flow dimensions factorial analysis
Item | Factor (Fluxo) | Cronbach’s Alpha |
---|---|---|
After using web, I feel like I come back to the “real world” after a journey. | 0.783 | 0.777 |
Using the web often makes me forget where I am. | 0.734 | |
Using the web creates a new world for me and this world suddenly disappears when I stop browsing. | 0.717 | |
Using the web provides a good test of my skills. | 0.635 | |
When I use the web, my body is in the room, but my mind is inside the world created by websites I visit. | 0.628 | |
When I use the web, the world generated by the sites I visit is more real for me than the “real world”. | 0.622 | |
I forget about my immediate surroundings when I use the web. | 0.610 | |
When I use the web, I feel I am in a world created by the websites I visit. | 0.572 | |
When I use the web, I tend to lose track of time. | 0.568 | |
I find that using the web stretches my capabilities to my limits. | 0.560 | |
Time seems to go by very quickly when I use the web. | 0.497 | |
Using the web challenges me. | 0.466 | |
Using the web challenges me to perform to the best of my ability. | 0.444 | |
I consider myself knowledgeable about good search techniques on the web. | −0.418 |
. Estimative
Relationships | Estimative | DE | Sig. |
---|---|---|---|
Risk ßà Flow | 0.033 | 0.170 | 0.844 |
Risk à Ability | 2.520 | 0.404 | 0.000 |
Flow à Ability | 0.023 | 0.118 | 0.846 |
Picture 1. Path diagram.
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Regarding Hypothesis 2, the perception of manipulated risk diminishes the occurrence of the flow state during the purchase process on-line. In such a way, the result of the analyses demonstrated that the perceived risk does not have relation with the state of flow of the consumer.
This information disconstructs the positioning of Dailey [
Other authors had also studied the relation between the perceived risk and the state of flow. Ghani and Deshpande [
Another hypothesis is that low perception of risk increases the probabilities to occur the flow state [