Consumer purchase intention towards a quick response (QR) code for antibiotic information: an exploratory study

Growing consumer concern over food quality and safety, fueled by various food scandals, has led to the development of traceability systems. Recently, antimicrobial use (AMU) in food-producing animals has become a significant point of criticism. While AMU in the UK pork industry has decreased, the lack of a system for disseminating this information to consumers at the point of purchase presents an opportunity. Quick Response (QR) codes, a popular traceability system, could provide consumers with reliable antibiotic information. This study uses the Theory of Planned Behavior (TPB) to explore consumer perceptions and purchase intentions regarding QR code-labeled pork, focusing on attitudes, perceived behavioral control, and trust. The UK pork industry was chosen due to its high antibiotic usage and associated consumer concerns.

Substantial research on consumer food choice often uses the TPB to explain behavior. The TPB posits that behavioral intention is influenced by attitude, subjective norm, and perceived behavioral control (PBC). Studies have examined consumer willingness-to-pay (WTP) for products with improved animal welfare standards, showing a significant premium for higher welfare meat. Research on pig welfare indicates positive consumer attitudes towards increased welfare standards, but the introduction of 'antibiotic-free' labels has yielded mixed results. Some consumers mistakenly associate these labels with high animal welfare standards, highlighting an information gap. The proliferation of 'antibiotic-free' labels creates a need for a system providing reliable antibiotic information while ensuring animal welfare.

An online survey was conducted with a nationally representative sample of UK adults (n=1000). Respondents were randomly assigned to one of two groups: one group received information about QR code 1 (antibiotic usage labeled pork), and the other group received information about QR code 2 (farm assurance labeled pork). The survey measured various factors, including socio-demographics, pork consumption habits, knowledge of EU regulations and AMR, attitude towards QR code labeled pork, perceived behavioral control, trust, behavioral beliefs, perceptions of QR codes, purchase intention, and willingness-to-pay (WTP). Data analysis involved factor analysis to construct scales for different constructs, descriptive statistics, and hierarchical multiple regression analyses to assess the relationship between predictor variables and purchase intention. A pictorial example of QR-coded pork was shown to respondents, highlighting the information available through scanning the QR code. The QR codes provided information about origin, farmer, rearing conditions, breed, compliance with UK law, withdrawal periods, RSPCA welfare standards, and (for QR code 1) quantified antibiotic usage data.

The majority of participants (64%) purchased pork several times a month, considering extrinsic qualities (price, quality, quantity, appearance) as the most important purchase attributes. Respondents showed slightly favorable attitudes and high trust towards QR code-labeled pork. Behavioral beliefs varied, with a strong belief that QR code pork would be more expensive, but a negative perception regarding ease of locating the product. Regarding trust, respondents felt QR code pork would assure responsible antibiotic use. PBC was moderately high, but respondents preferred a rating system or color-coding for antibiotic usage instead of figures. Participants showed moderately positive perceptions towards QR code-labeled pork, but awareness of AMR was limited. Purchase intention was moderately positive. Willingness to pay extra for antibiotic usage labeled pork was ~10% and ~15% for farm assurance labeled pork. Regression analyses revealed that attitude, PBC, and perception of QR codes strongly correlated with purchase intention in both sub-groups. Other significant predictors included personal concern (farm assurance label), AMU acceptance (antibiotic usage label), and animal welfare standards (antibiotic usage label). Socio-demographic factors had little impact on purchase intention. Analysis of behavioral beliefs showed that product quality and animal welfare (farm assurance label) influenced attitudes. Younger participants had more positive attitudes toward traceable pork.

This study shows that consumer attitudes and perceived behavioral control are strong positive determinants of purchase intention for QR code-labeled pork. The findings support the potential of QR codes as a way to deliver antibiotic information to consumers. The relatively high importance placed on product quality in influencing attitudes suggests that marketing strategies should focus on this aspect. While the moderate positivity of perceptions towards QR code pork indicates room for improvement, the results show that perceived behavioral control can be increased by simplifying the information presented (e.g. color-coding instead of mg/kg). The significant influence of consumer perceptions underscores the need for marketing communications to highlight the benefits of QR codes for accessing traceability information. The varying influences of perceptions toward AMU practices across label sub-groups suggests the need for further research.

This study demonstrates the potential of QR codes as an alternative to 'antibiotic-free' labeling. Consumer attitudes and perceptions are key drivers of purchase intention. While the study highlights the importance of addressing consumer knowledge gaps about AMR and AMU practices, the results demonstrate a positive overall response to the QR code approach. Future research should compare QR code labeled pork directly with antibiotic-free labeled products and investigate in-store purchase behavior.

The study focuses on behavioral intention rather than actual purchase behavior. Consumers may not always notice or choose to scan the QR code. The survey used hypothetical scenarios. Future research should investigate in-store purchase behavior using real products and money, explore QR code design aspects (size, call-to-action), and replicate the findings to ensure robustness.