Consumer acceptance of a quick response (QR) code for the food traceability system: Application of an extended technology acceptance model (TAM) (original) (raw)

Highlights

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This study is to apply the TAM using the addition of perceived information to individuals’ behavioral intention to use.
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This study is to compare the explanatory power of the original TAM model with the expanded TAM.
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Food involvement plays a significant moderating function in between perceived information and perceived usefulness.

Abstract

The objectives of this study are to apply the TAM using the addition of perceived information to individuals' behavioral intention to use the QR code for the food traceability system; and to determine the moderating effects of food involvement on the relationship between perceived information and perceived usefulness. Results from a survey of 420 respondents are analyzed using structural equation modeling. The study findings reveal that the extended TAM has a satisfactory fit to the data and that the underlying dimensions have a significant effect on consumers' intention to use the QR code for the food traceability system. In addition, food involvement plays a significant moderating function in the relationship between perceived information and perceived usefulness. The implications of this study for future research are discussed.

Graphical abstract

Introduction

With the development of information technology, the food traceability system which can reduce individuals' concerns about food safety by providing unambiguous information about the safety and quality of the whole process, from producers to consumers—has been broadly disseminated in the food industries (e.g., Badia-Melis et al., 2015, Cozzolino, 2014, Lin et al., 2014, Melo et al., 2015).

The policy makers in the public health sectors of many countries have accepted the food traceability system. For instance, the EU Rapid Alert System for Food and Feed (RASFF) employed the tracking software system Grapenet, an internet-based electronic service for certification of grapes for export from India to the EU (Badia-Melis et al., 2015). In the US, food traceability information has been mandatory since 2002, and facilities, transport organizations, storage facilities, and other food handlers have been responsible for recording information, including product descriptions and providers' and recipients' addresses and phone numbers if the goods cross state lines (US FMSA, 2011). South Korea also introduced a beef traceability system, which traces the distribution channel for beef (Badia-Melis et al., 2015).

The Quick Response (QR) code, one of the traceability systems, has been introduced to the food industries as a two-dimensional barcode (e.g., Shin et al., 2012, Tarjan et al., 2011, Tarjan et al., 2015). The QR code can hold considerably more information than the one-dimensional code, as it can embed text, video, advertisements, personal information, etc.

The QR code can be integrated into users' smartphone applications; that is, the smartphone can scan and decode information and messages about products that the QR code provides. The use of the QR code is increasing globally (Shin et al., 2012, Tarjan et al., 2011), but even with its introduction for food traceability in the food industry (Fig. 1), there has been limited research on consumer acceptance of its usefulness for providing food information or the use of the QR code for the food traceability system in the context of food research.

A significant number of studies have indicated that the technology acceptance model (TAM) is a suitable psychometric tool with which to assess consumers' acceptance of technology, determined by the individual's perception of the new technology's usefulness (e.g., Venkatesh & Davis, 1996). Therefore, the first purpose of this study is to apply the TAM to individuals' acceptance of the QR code for the food traceability system.

While, Chen and Huang (2013) examined the moderating effect of involvement between uncertainty, formed by the lack of information on foods, and consumer behavior. They found that the higher one's degree of involvement and the more the food traceability system mitigates their uncertainty, the greater their intention to buy a food. Other previous studies have also stressed that the function of consumer involvement in foods is a topic worth investigation (Karlsen et al., 2011, Verbeke and Vackier, 2004). Therefore, the other purpose of the present study is to determine the moderating effects of food involvement on the relationship between constructs of the TAM.

Section snippets

Technology acceptance model (TAM) and perceived information

Derived from the theory of human reasonability developed by Ajzen and Fishbein (1980), the TAM explains the determinants of users' acceptance of technology (Davis, 1989, Davis et al., 1989). The TAM shows that a user's attitude toward a particular technology is determined by the individual's perceived usefulness (PU) and perceived ease of use (PEOU) of the technology. The TAM is supported by the relationships among belief, attitude, and behavior (Davis et al., 1989).

PU refers to the degree to

Research instruments

The current study uses the extended TAM—consisting of PI, PU, PEOU, attitude toward using (ATT)—to identify the level of consumers' acceptance of the food traceability system and BI to measure those structural relationships. Each construct was measured with multiple items using a 5-point Likert scale (ranging from 1

strongly disagree/extremely unlikely to 5

strongly agree/extremely likely) (e.g., Davis et al., 1989, Muk and Chung, 2015, Rese et al., 2014, Venkatesh and Davis, 1996).

This study

Measurement model

Confirmatory Factor Analysis (CFA) using AMOS 18.0 was conducted to test the measurement model before examining the relationships between the latent constructs and their indicators. Given the sensitivity of the chi-square statistics to sample size (Anderson and Gerbing, 1988, Hair et al., 2009), the overall model fit was assessed using six goodness-of-fit indices (i.e., comparative fit index (CFI), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), normalized fit index (NFI),

Discussions and implications

The current study began at the current point of the research; although previous studies have applied the TAM to consumer behaviors (e.g., Venkatesh & Davis, 1996), limited research in the food industry has dealt with the importance of PI in consumers' decisions related to the QR code for the food traceability system and the moderating influences on the relationship between PI and PU in the TAM. The study applied the TAM with PI to individuals' BI to use the QR code for the food traceability