The utility of food traceability standards aims to reduce the of food security and to provide consumers with targeted information. A survey has been conducted on sample of consumers in Beijing (n = 234), aiming to explain the intention toward purchasing traceable beef using the theory of planned behavior (TPB). Based on the TPB , the present study has added new variable: past experience. Structural Equation Modeling is applied to test the TPB model. Results show that attitude , subjective norm and past experience are positively associated with intention to traceable beef.
Food safety has triggered growing attention because of the numerous food safety scandals that occurred in the last few decades. The food safety scandals that many factors caused have happened frequently. These factors include pesticide residues [
At present, it is limited that the degree of Chinese consumer cognition for traceable food. In terms of research content, region is usually considered very important factors influences consumers’ cognition. And consumers have different cognition for traceable food in different regions. As well as, the concern about food safety affects the cognition for traceable food.
Previous studies have revealed that past behavioral was crucial factors in predicting Consumers’ purchase intentions [
In conclusion, a large number of studies have focused on consumers’ purchase intentions toward traceable food. The most studies showed that the consumer cognition for traceable food, trust for traceable food and socio-demographic variables, are the main determinants of consumers’ purchase intentions toward traceable food, but have been rarely reported the factors such as subjective norms, perceived behavioral control and how they influence intention to purchase traceable food. To address this issue, attitude, subjective norms, perceived behavioral control, past behavior were included in the current study. This study aimed to assess Beijing consumers’ purchase intentions toward traceable beef, as a typical sample of traceable food. The results of this study will provide important guidance for both industries and policy makers.
The theory of Reasoned Action suggests that the behavior of individuals is considered to be the consequence of intention about the object. And the behavior is driven by attitude and subjective norm [
The TPB framework is applied in a wide range of health-related actions studies for predicting behavioral intentions. Mullan et al. [
human behavior. And the past experience may be considered an important predictor of human behavior. To model consumers’ purchase intention for traceable beef, in our study, an extended TPB model was tested by measuring consumers’ purchase intentions that attitude, subjective norm, perceived Behavioral Control, past experience. On the basis of this discussion, following hypotheses are formulated.
H1: Consumer’s attitude towards purchasing traceable beef positively influences consumers’ purchase intention.
H2: Consumer’s purchase intention towards purchasing traceable beef is positively influenced by the subjective norm.
H3: Perceived Behavioral Control significantly influences consumer’s intention towards purchasing traceable beef.
H4: Past experience significantly increases consumer’s intention towards purchasing traceable beef.
H5: Past experience influences consumer’s attitude towards purchasing traceable beef.
H6: The past experience positively influences consumer’s subjective norm.
H7: Consumer’s attitude towards purchasing traceable beef is positively influenced by the subjective norm.
H8: Perceived Behavioral Control significantly influences consumers’ attitude towards purchasing traceable beef.
To accomplish objective of the study, the questionnaire included items already used in the previous findings on similar literature [
Constructs and measuring items | Sources |
---|---|
Purchase intention | |
G1: I am willing to consume traceable beef if they are available for purchase | Yazdanpanah & Forouzani, 2015 |
G2: I plan to consume traceable beef if they are available for purchase | |
G3: I plan to consumer traceable beef if they are available for purchase | |
Attitude | Yadav & Pathak (2016) |
ATT1: Buying traceable beef is good idea | |
ATT2: Buying traceable beef is a wise choice | |
ATT3: Buying traceable beef would be pleasant | |
Subjective Norm | |
SN1: I would buy traceable beef because people important to me buy it | Menozzi et al., 2015 |
SN2: I would buy traceable beef because media are in favor | |
SN3: I would buy traceable beef because government promote it | |
Perceived Behavioral Control | |
PBC1: I am confident that I can purchase traceable beef rather than normal products when I want | Kamonthip et al., 2016 |
PBC2: I see myself as capable of purchasing traceable beef in future | |
PBC3: I have resources, time and willingness to purchase traceable beef | |
Past Experience | |
PA1: I am familiar with eating traceable beef | Mitterer-Dalté et al. 2013 |
PA2: I have much experience in buying traceable beef | |
PA3: I have much knowledge about traceable beef |
Primary data were collected through face-to-face interviews using a structured questionnaire at a number of parks and supermarkets in Beijing. In this study, a total of 300 questionnaires were distributed among target population who bought traceable beef from March 2016 to April 2016. In the end 249 questionnaires were returned, but only 234 questionnaires were considered complete. From the descriptive statistics shown in
To test the internal consistency among the items, the Cronbach’s α coefficient were calculated. According to Hair [
Items Classification | Frequency | Percentage | |
---|---|---|---|
Gender | Females | 130 | 55.6% |
Males | 104 | 44.5% | |
Age | 20 - 29 | 80 | 34.2% |
30 - 39 | 102 | 43.6% | |
40 - 49 | 34 | 14.5% | |
50 - 59 | 13 | 5.6% | |
>60 | 5 | 2.1% | |
Educational level | Elementary | 28 | 6.5% |
Secondary | 89 | 21% | |
University | 272 | 64% | |
Graduate and Doctoral | 36 | 8.4% | |
Family Monthly income(RMB) | <1000 | 8 | 1.9% |
1000 - 3000 | 23 | 5.4% | |
3001 - 5000 | 65 | 15.3% | |
5001 - 10,000 | 84 | 19.8% | |
10,001 - 20,000 | 203 | 47.8% | |
20,001 - 30,000 | 31 | 7.3% | |
30,001 - 40,000 | 9 | 2.1% | |
>40,001 | 2 | 0.5% |
acceptable limit of 0.700, and all the average variance extracted (AVE) estimates were >0.60. Therefore, the items had composite reliability. Confirmatory Factor Analysis (CFA) was applied on the consumers’ purchase intention towards traceable beef model to access the information about validity. In this study, five-factor CFA model (see
In
As can be seen form
Constructs | Items | Cronbach’s α | Factor loading | CR | AVE | KMO | Bartlett’s test significant |
---|---|---|---|---|---|---|---|
Purchase intention | G1 | 0.732 | 0.802 | 0.733 | 0.692 | 0.718 | 0.000 |
G2 | 0.612 | ||||||
G3 | 0.541 | ||||||
Attitude | ATT1 | 0.731 | 0.724 | 0.735 | 0.783 | 0.749 | 0.000 |
ATT2 | 0.708 | ||||||
ATT3 | 0.682 | ||||||
Subjective Norm | SN1 | 0.747 | 0.671 | 0.748 | 0.761 | 0.850 | 0.000 |
SN2 | 0.698 | ||||||
SN3 | 0.713 | ||||||
Perceived Behavioral Control | PBC1 | 0.804 | 0.711 | 0.809 | 0.822 | 0.725 | 0.000 |
PBC2 | 0.655 | ||||||
PBC3 | 0.761 | ||||||
Past Experience | PA1 | 0.885 | 0.844 | 0.886 | 0.623 | 0.821 | 0.000 |
PA2 | 0.838 | ||||||
PA3 | 0.793 |
Fit Indices | Criteria | Indicators | Results |
---|---|---|---|
Chi-square/df | <2.000 | 1.600 | Supported |
Root Mean Square Error of Approximation (RMSEA) | <0.100 | 0.051 | Supported |
Comparative Fit Index(CFI) | >0.900 | 0.963 | Supported |
Goodness of Fit Index(GFI) | >0.900 | 0.924 | Supported |
Adjusted Goodness of Fit Index (AGFI) | >0.800 | 0.890 | Supported |
Normed Fit Index(NFI) | >0.900 | 0.909 | Supported |
Incremental Fit Index | >0.900 | 0.964 | Supported |
Tucker-LewisIndex | >0.900 | 0.953 | Supported |
Path Correlation | Standardized Path Coefficient | Results |
---|---|---|
H1: Attitude ---> purchase intentions | 0.535*** | supported |
H2: Subjective norm ---> purchase intentions | 0.596** | supported |
H3: Perceived behavioral control ---> purchase intentions | 0.119 | not supported |
H4: Past experience ---> purchase intentions | 0.101** | supported |
H5: Past experience ---> Attitude | 0.182*** | supported |
H6: Past experience ---> Subjective norm | 0.238*** | supported |
H7: Subjective norm ---> Attitude | 0.163*** | supported |
H8: Perceived behavioral control ---> Attitude | 0.201*** | supported |
Note: p-value: ***p < 0.001, **p < 0.05.
A structural equation model (SEM) technique was employed on the data collected to test consumers’ purchase intention towards traceable beef in the extended framework of the TPB model. The result suggested that consumers’ purchase intention towards purchasing traceable beef can be predicted by attitude, subjective norm and past experience. However, subjective norm seemed to be the strongest determinants, which showed that consumers would buy traceable food if friends, members, media and government approve of it. The result has already been validated in other studies [
Attitude was found to be significant and positive for consumers’ purchase intentions. To increase consumers’ purchase intention, it is imperative to share more information on traceable beef, and government departments should fully strengthen the supervision of sharing information to improve the level of consumers’ trust in food traceability system.
Finally, the past experience exerted a stronger influence on consumers’ purchase intention towards traceable beef which showed that customer loyalty should be considered as important parameters while devising strategy for traceable beef. So the traceable business should increase the cultivation of old customers. This could help increase the consumers’ purchase intention towards traceable beef.
This paper was partially supported by National Key Technology R&D Program of the Ministry of Science and Technology (No. 2014BAL07B05), Humanities and Social Sciences Foundation of Ministry of Education of China (No. 13YJCZH182).
Song, H., Wang, R.M. and Hu, Y. (2017) Consumers’ Purchase Intentions toward Traceable Beef― Evidence from Beijing, China. American Journal of Industrial and Business Management, 7, 1128-1135. https://doi.org/10.4236/ajibm.2017.710081