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  , antimicrobial contamination  , the abuse of food additives  and the information asymmetry   in food market to mention a few. The traceability system is effective to provide consumer with more food related information throughout the entire supply chain, which can be build a reputation for food and restored consumer confidence. Starting in 2000, Food traceability system has been introduced in China. However, the extra cost of traceability system is passed on to the food price, which exceeds consumer’ affordability. Hence, consumers’ intention to purchase traceable food is a key for both industries and policy makers.
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  . Consumers’ purchase intentions toward traceable food are affected by a numbers of factors like concern about traceability system, trust for traceable food, and issuer for traceability certificate   . Moreover, Consumers’ purchase intentions were influenced by consumers’ socio-demographic variables  . HAN and ZHANG found that age, income and education have a significant impact on consumers’ purchase intentions  and Gou and Li found that female consumers gave more importance to the traceable milk  . The consumers’ willing to pay for traceable food has mainly focused on traceable food price, and consumers have different price premium.
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.
2. The Theoretical Framework
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  . Based on the TRA, Ajzen (1985) proposed the theory of planned behavior to predict and describe the behavior of individuals. According to the TPB model in Figure 1, individual’s behavior is guided by their intention, and the intention is impacted by attitude, subjective norms and perceived behavioral control. The attitude is measure of the favorable or unfavorable a behavioral alternative. Subjective Norm is perceived social pressure to perform the behavior for an individual. Finally, Perceived Behavioral Control is perceived confidence to the possibility of performing the behavior.
The TPB framework is applied in a wide range of health-related actions studies for predicting behavioral intentions. Mullan et al.  applied TPB towards safe food handling in the UK and Australia, showing that risk perception and PBC were a significant predictor of intention or behavior. Yadav  et al. demonstrated the utility of TPB in predicting the organic food purchase intention. However, it has been criticized for the level of prediction of the TPB. Numerous studies have developed the TPB models to understand and predict
Figure 1. Proposed theoretical framework.
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.
3. Research Methodology
3.1. Designing of Questionnaire
To accomplish objective of the study, the questionnaire included items already used in the previous findings on similar literature    , All items were scored on a 5-point Likert’s type scale(1 = “totally disagree”，5= “totally agree”). All the items and their source are mentioned in Table 1.
Table 1. Questionnaire.
3.2. Data Collection
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 Table 2, most respondents were females (55.6%), age between 30 and 39 (43.6%), or between 20 and 29 (34.2%). Almost 47.8% respondents reported a family monthly income between $10001- $20000. In terms of completed education, University (64%) and Secondary (21%) were most often reported.
To test the internal consistency among the items, the Cronbach’s α coefficient were calculated. According to Hair  , the Cronbach’s α should be greater than 0.700. Therefore, all of Cronbach’s α ranged from 0.732 to 0.886. The value of composite reliability (CR) ranged from 0.732 to 0.886, which also met the
Table 2. Sample characteristics.
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 Table 3) was tested. The results indicated the hypothesized measurement model was reliable.
In Table 4, χ2/df = 1.600, RMSEA = 0.051, CFI = 0.963, GFI = 0.924, AGFI = 0.890, NFI = 0.909, IFI = 0.964, TLI = 0.953. All these indices were higher than the suggested goodness-of-fit values for the proposed structural model.
As can be seen form Table 5, Subjective norm and past experience are all significant and positively related to consumer’s intention towards purchasing traceable beef (p < 0.05), which supported the hypothesisH2 and H4. Attitude has also a positive effect on purchase intentions (β = 0.535), this confirms our H1. Similarly, past experience, subjective norm and perceived behavioral control are all significant and positively related to attitude. The hypotheses for the model (H5, H7, H8) are supported. Past experience has a positive effect on subjective norm (β = 0.238), supporting H6. Perceived behavioral control showed no significant influences on consumer’s intention towards purchasing traceable beef, hence H3 was not supported.
Table 3. Reliability and validityof the items.
Table 4. Goodness-of-fit indices of the research model.
Table 5. Hypothesis test results.
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  . Therefore, we can argue that developing relevant marketing strategies of traceable food, such as advertising and price promotion, may improve the level of consumer awareness about traceable food and consequently their intention to purchase intention.
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).
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