Conceptual Framework and Theoretical Model

Almost all research studies in social and behavioral sciences regardless of disciplines/programs require a rationale or base for conducting research. This rationale/base is often called theoretical framework. A host of researchers have provided varying definitions of theoretical framework (Sekaran, 2000; Camp, 2001; Elliott; 2005, Tuckman, 1999). A theoretical framework is a conceptual model of how one theorizes or makes logical sense of the relationships among several factors that have been identified as important to the problem (Sekaran, 2000). In essence, it attempts to integrate key pieces of information especially variables in a logical manner, and thereby conceptualizes a problem that can be tested. A typical theoretical framework provides a schematic description of relationships between and among independent, dependent, moderator, control, and extraneous variables so that a reader can easily comprehend the theorized relationships. Radhakrishna, Leite, and Baggett (2003) presented a typology for research designs. Using the quantitative research paradigm, they classified research designs into three categories: descriptive, descriptive-correlational, and experimental. The decision to select a research design depends on the goals of one’s research study. It also depends on the review of literature which provides a solid foundation for developing theoretical framework. Therefore, the linkage between research types and theoretical framework becomes vital.

Formulating the Conceptual Framework

The conceptual framework of the study is a structure that can hold or support a theory of a research work. It presents the theory which explains why the problem under study exists. Thus, the theoretical framework is nothing but a theory that serves as a basis for conducting research, in other words it is the linkages between concepts from the literature that justifies the need to answer the question to the research problem. journclasses.pbworks.com/f/theoretical+framework.ppt

The Purposes of formulating a conceptual framework includes:

Helping the researcher see clearly the variables of the study.

Providing the researcher with a general framework for data analysis.

To achieve these purposes the theoretical framework when formulated should consider to:

Specify the theory used as basis for the study

Mention the proponents of the theory

Cite the main points emphasized in the theory

Support the exposition of the theory by ideas from other studies

Illustrate itself by means of a diagram.

In this chapter the researcher details the conceptual framework of this study by articulating the research problem and, the theoretical model developed to be tested in relation to the research problem and to provide the reasoning behind the developed hypotheses, to be followed by the measurement of scales of the variables discussed in the theoretical model.

The research process steps is diagramed in Figure:xx

Gathering Requirements

Finalizing Objectives

Choosing methodologies Planning Process

Exploring Literature

Understanding deeply the research problem

Identifying key factors

Developing the research modelExploratory Process

Developing Hypotheses to be tested

Defining factors and variables

Executing study and measure resultsQuantitative research

Figurexx: research process steps

Research problem statement:

This study aims to explore the added values of food traceability systems as electronic business applications in the agri-food industry to achieve quality control by revealing the role of food traceability systems in mitigating the information asymmetry taking place in the food supply chain between producers and consumers which refers to the fact that one market actor (producer/seller) is more or better informed than the other (consumer) -especially with the existence of credence attributes ; which are products attributes that cannot be observed by the consumer either at the point of sale neither after consumption, (e.g. the level of pesticide residues for vegetable food, genetically modified ingredients, or the level of animal welfare for animal food in products…..). Some of these credence attributes, related to health and food safety, has received increasing attention by consumers, particularly in EU. As a consequence, for example the competitiveness of a meat exporter depends heavily on its capacity to provide the relevant information in a credible way, with, for instance, an adequate traceability system. (Mello, Azevedo, 2004).

In this study the researcher presents a theoretical model that introduced four variables which are: Authenticity, information reliability provided by the system, information adequacy to satisfy consumers’ needs, and governmental third party credence embedded in the traceability system and their role in mitigating the previously mentioned information asymmetry situation which subsequently affects the perceived risks of consumers toward food products in terms of safety and quality. This reduction in perceived risks is considered to be a good cause that has its own effect on consumers’ willingness to pay price premiums for traceable products. In other words the model discusses the extent at which food traceability systems can provide consumers with reliable and adequate information, as well as supporting the authenticity of food products they are buying within the existence of governmental third party credence, will reduce consumer’s information asymmetry toward food products, especially for credence attributes of products and once this asymmetry is mitigated ,the consumers perceived risks related to food safety and quality inside active traceability systems will decrease, which will be a good cause for them to justify paying a price premium for traceable products.

Research questions:

This research study targets assessing the food traceability system added values from the consumer’s perspective through consumers’ perception toward and expectations from traceability systems in the food industry especially within the existence of information asymmetry situation mentioned above in the food supply chain. This information asymmetry affects on consumers’ bounded rationality and this rationality is limited by three factors:

The information consumers have.

The cognitive limitations of consumer’s minds.

The finite time consumers have to make decisions, which in turn increase uncertainty and perceived difficulty to evaluate quality and safety in products.

The primary question this research is addressing regarding the objective of assessing food traceability systems in agro products from the perspective of consumers is:

How does consumer’s knowledge of traceability systems affect their willingness to pay (WTP) a price premium for traceable products?

And through the assessment of traceability systems as a scenario of quality control within food supply chain, a secondary question falling into the attention of this s research is:

What are the added values of traceability systems as an electronic business application?

Conceptual ( theoretical) Framework:

Continued advances in information technology have created the infrastructure for a post-industrial economy, that is the knowledge economy in which modern software and networking enables producers and consumers’ unprecedented ease in creating and sharing digitized knowledge and this knowledge economy is abstracting away from products and services and is focusing on consumers’ experience itself, using products/services as props. The knowledge economy is transforming roles between all parties in the economy. It is witnessing extensive collaboration in which buyers are tapping into sellers’ resources to participate in the design and delivery of products and services. This process of interaction is mutual in nature , for that sellers are also accessing knowledge and feedback data from consumers’ experiences and future expectations as well.

As for the agri-food industry global demands for increased food safety and quality assurance programs, increased global competition, changing government rules and regulations, political and trade barriers, bioterrorism, and identity preservation requirements in global markets are all affecting the world’s food supply chain. To satisfy changing market demands, all suppliers in the food supply chain must adapt to these global issues. Total asset visibility must be maintained in production, in process, in storage, and in transit. Since 2001, new words have entered and dominated the global agricultural market place. Traceability, tracking, product integrity and quality assurance have become an important part of today’s global food supply chain.

Within these rapidly spreading practices, global consumers are also demanding verifications of food products and their sources for a disease-free food supply chain. This demand has called for intensified traceability that establishes the need for both operational shortage identi¬cation and tracing-back and forward capabilities in a food supply environment. Especially after recent global food-borne illness outbreaks which necessitated the importance and signi¬cance of traceability to the global food industry.

At the same time changing consumer attitudes have resulted in demands for greater food safety and quality control on the retail market. Quality control in food supply chain is a scientific discipline describing handling, preparation, and storage of food in ways that prevent food borne illness. This includes a number of routines that should be followed to avoid potentially severe health hazards and this is where food traceability systems function as record keeping of all the activities related to the food safety and quality assurance. This introduction to food traceability has its potentials to consumers as well and not only to the business, especially that consumer’s demand for greater food safety and quality is still faced by an information asymmetry situation taking place in the food supply chain which is strongly reported in different studies (McCluskey and Swinnen, 2004; Verbeke et al., 2007; Verbeke and Ward 2006; Grunert, 2002; Hobbs, 2003). In economics and contract theory, information asymmetry deals with the study of decisions in transactions where one party has more or better information than the other. This creates an imbalance of power in transactions which can sometimes cause the transactions to be unbalanced.

Economists explain moral hazard as a special case of information asymmetry, a situation in which one party in a transaction has more information than another. In particular, moral hazard may occur if a party that is insulated from risk has more information about its actions and intentions than the party paying for the negative consequences of the risk. More broadly, moral hazard occurs when the party with more information about its actions or intentions has a tendency or incentive to behave inappropriately from the perspective of the party with less information which in our case is translated to producers knowing more about all what is related to food products from the early stages in farms to the arrival of products to the sale or consumption point by consumers, whether this information is related to raw materials, manufacturing processes, health control programs, packaging and storage conditions…..etc among huge amounts of details recorded in the supply chain. This state of information asymmetry induces consumers to be affected by a perception of risk toward food products in terms of quality and safety.

A classic paper on adverse selection as another example of information asymmetry is George Akerlof’s “The Market for Lemons” from 1970, discusses two primary solutions to information asymmetry problem, signaling and screening.

Signaling:

Michael Spence originally proposed the idea of signaling. He proposed that in a situation with information asymmetry, it is possible for people to signal their type, thus believably transferring information to the other party and resolving the asymmetry. Spence proposes, for example, that going to college can function as a credible signal of an ability to learn assuming that people who are skilled in learning can finish college more easily than people who are unskilled, and then by finishing college the skilled people signal their skill to prospective employers. No matter how much or how little they may have learned in college, finishing functions as a signal of their capacity for learning.

So the idea behind signaling depends on transferring the information to the less informed party to reduce the gap between the better informed and the less informed, referring to producers and consumers respectively.

According to Bailey et al., (2002); Liddell and Bailey, (2001) labels and certifications are among the most used signaling mechanisms by the food industry. They also ascertain that the following are particularly important in the signaling process of food safety and quality:

(a) The implementation of traceability programs along the production and marketing chain.

(b) The transparency in the productive processes.

(c) Mechanisms of product assurance, in terms of food safety and quality (traceability, transparency and assurance – TTA).

Then it would be logical to conclude that traceability systems can function as a signaling mechanism to transfer information as signals to consumers to increase their level of knowledge about food products. In the model discussed in this research there are four variables that the researcher is arguing to be characterizing a food traceability system in order to achieve the signaling activity as a solution to the information asymmetry taking place between producers and consumers. These variables are:

Authenticity

Information reliability

Information adequacy

Credence of governmental third party:

Screening:

Joseph E. Stiglitz pioneered the theory of screening for resolving information asymmetry. In this way the under informed party can induce the other party to reveal their information. They can provide a menu of choices in such a way that the choice depends on the private information of the other party.

Examples of famous classical situations where the seller usually has better information than the buyer are numerous but include used-car salespeople, mortgage brokers and loan originators, stockbrokers, Realtors, real estate agents, and life insurance transactions.

Examples of situations where the buyer usually has better information than the seller include estate sales as specified in a last will and testament (estate sale or estate liquidation is a type of garage sale, yard sale or auction to dispose of a substantial portion of the materials owned by a person. The most common reason for an estate sale is the death of the property owner, and the consequent need to quickly liquidate the deceased’s belongings (Wikipedia, 2010). Another example lies in sales of old art pieces without prior professional assessment of their value. This situation was first described by Kenneth J. Arrow in an article on health care in 1963.

George Akerlof in “The Market for Lemons” notices that, in such a market, the average value of the commodity tends to go down, even for those of perfectly good quality. Because of information asymmetry, unscrupulous sellers can “spoof” items (like software or computer games) and defraud the buyer. As a result, many people not willing to risk getting ripped off will avoid certain types of purchases, or will not spend as much for a given item because of this risk perception. He even extends the consequence of this risk perception to the possibility for the market to decay to the point of nonexistence.

In food traceability systems consumers can obtain reliable information about quality and use this to screen out low quality products (Perloff, 2001). However, this comes at a cost as stated by Shapiro (1983) ‘Information costs are as real as production costs’ these costs can stop a market emerging. In some cases consumers can buy information about some types of goods from experts who have no incentive to provide misleading information, for examples The Royal Automobile Club of Victoria (RACV) pre-purchase car inspections, and Archicentre building design and inspection experts’ pre-purchase house inspections but how that could happen when buying food products! This is why traceability systems is an efficient tool to help consumers be better informed partners in the supply chain.

The informativness of traceability systems in terms of reliability and adequacy is very much connected to the outcome of signaling and screening which is the emergence of some markets for credence attributes, even in the presence of information asymmetries. But signals are costly and imperfect, and consumers incur costs in identifying and interpreting many signals. Therefore, some attributes, which would be profitable with full information, are not produced or there are no private incentives to promote a particular credence attribute that consumers want. This can happen when:

The information has a public good aspect and all companies would benefit from one company’s claim; for example, oats improve heart health

There is no competitive disclosure of negative attributes; for example, there are no ‘cholesterol-free’ eggs, so consumers are not alerted to the cholesterol content of eggs.

Communicating ethical traceability should be anticipated as a three step process that includes the following three approaches (Coff et.al 2008):

1. Providing sound information to consumers (one-way information strategy on the basis of the informed choice argument).

2. Establishing a reciprocal dialogue with consumers (participatory strategy on the basis of the normative argument).

3. Establishing a deeper engagement between dedicated consumers and producers (co-production strategy on the basis of the normative argument).

On its own, the first strategy runs the risks that the information given will fail to interest some or most consumers, because it will not connect with their own ethical and cognitive information preferences. Consumers nowadays are subjected to an overload of information that simply does not tell them anything, because the information totally neglects their own ethical and information preferences, taking us to the intensive need to the types of information consumers’ are concerned about within each domain in the food industry presented as the information reliability variable in the model of this research.

The second strategy looks more promising with respect to communicating issues of ethical traceability, because it takes the two-way information process seriously, and thereby recognizes the specific information needs of different consumers. However, this strategy runs the risk that the communication will take place at a late stage in corporate decision-making processes, on the basis of definitions of problems that have not been subjected to critical scrutiny by consumers. The third strategy looks more promising with respect to the joint development of (the premises for) ethical traceability schemes. However, this strategy is probably too demanding to count on the engagement of a large number of producers and consumers. The three approaches together do, offer a new strategy for involving consumers in the methods and decisions of the food supply chain.

This communication of information to consumers from traceability systems records carries substantial added values. It is essential when discussing values of food traceability to refer to the fact that the terms “value” and “values” are used in different ways when referring to food production and food business networks. Stevenson, (2008) addresses three points within this scope:

1. “Value-added” used to characterize food products that are converted from raw product through processes that give the resulting product an “incremental value” in the market place. An “incremental value” is realized from either higher price or expanded market. For example, jams, cheeses, and pre-cooked meats are considered “value-added” products.

2. “Value-added” is used to characterize food products that have incremental value in the marketplace by differentiating them from similar products based on product attributes such as: geographical location; environmental stewardship; food safety; or functionality. Examples of this type of “value-added” products include locally grown produce, organic or integrated pest management (IPM) grown fruits, antibiotic and/or hormone free meat, or functionally specified hops or baking flours.

3. “Value” and “values” used to characterize the nature of certain business relationships among interacting food business enterprises, rather than any attribute of the product itself. In general, this collection of relationships is referred to as the “supply chain”. When these relationships are expressly based in an articulated set of values, they are becoming known as “values-based supply chains” or, more succinctly, “value chains”.

As for the food industry in relation to the research problem and the described information asymmetry situation it is concluded that traceability systems added value to consumers fits perfectly as a tool to provide consumers with details of information about the recorded data related to food products and this can be motivated (screened) by consumer’s revelation of paying premiums to producers for traceable products to motivate them into investing in traceability systems within high levels of adoption that will allow the existence of diversified data details to be communicated and transferred( signaled) to consumers to mitigate their perception of risk toward food products and not only adopting traceability as a regulatory requirement to food laws which in this case is seen to be a daunting task by food organizations.

To finalize; the model presented in this research(Figure x) while delivering four variables to characterize the informativeness of traceability systems, which are: authenticity, information reliability, information adequacy, and governmental third party credence, to reduce the aforementioned situation of information asymmetry, it is also extending its stand point by using the four mentioned variables to function under the umbrella of signaling and screening solutions (Figure xx) , where signaling is the activity that producers can perform through traceability systems for transferring information to the less informed parties represented by consumers, and the activity of screening is performed by consumers to motivate producers to adopt traceability systems and to share the relevant information from the records of traceability systems by their revelation to paying price premiums to traceable products. In this way consumers’ will be using the producer’s signals to screen safety and quality. In other words the model realizes the objective of allowing consumers’ to be driven by signaling route which refers to the activities of the suppliers (as better informed side) offering indicators to consumers about food products in terms of safety and quality, and at the same time by transferring this information, consumers will be able to practice screening as less informed side by gathering information actively and thus assessing product attributes.

Bounded rationality

Information available

Time to make decisions

Cognitive limitations of minds

Authenticity

Information Asymmetry

Info. Reliability

Willingness to pay

Info. Adequacy

Perceived Risk

Governmental third party credence

Quality

Safety

`Figure x, the model of the research

Signaling through traceability systems by producers

Screening through motivating producers to share information with consumers

Product authenticity

Willingness to pay price premiums for traceable products

Info. Reliability

Info. Adequacy

Governmental third party credence

(Figure xx integrating the study model into the screening and signaling solutions to information asymmetry)

Research Hypotheses:

A hypothesis is a logical supposition, a reasonable guess, an educated conjecture. It provides a tentative explanation for a phenomenon under investigation. However, hypotheses are not unique to research. Hypotheses are constantly generated in the human mind as we work to understand day-to-day phenomena. By formulating a series of reasonable guesses of cause and effect we are able to understand and explore the events in our surrounding environment Leedy and Ormrod, 2001).

The importance of hypothesis is in its ability to guide the research. A researcher may refer to the hypothesis to direct his or her thought process toward the solution of the research problem or sub-problems. The hypothesis helps an investigator to collect the right kinds of data needed for the investigation. Hypotheses are also important because they help an investigator to locate information needed to resolve the research problem or sub-problems.

In research, a researcher is able to either support or reject a hypothesis. If a hypothesis is rejected, it will lead a researcher to new hypothesis to explain the phenomenon in question. If a hypothesis is continually supported, it may evolve into a theory (Leedy and Ormrod, 2001).

As a hypothesis is continually supported over time by a growing body of data, it becomes a theory. A theory is described as “an organized body of concepts and principles intended to explain a particular phenomenon”. A theory is similar to a hypothesis in that it offers a tentative explanation for a phenomenon that new data will either support or not support. Both are supported or rejected based on testing by various investigators under different conditions (Leedy and Ormrod, 2001).

A researcher will formulate a hypothesis based on the problem or sub-problems of the research. The hypothesis is driven by the research question (Leedy and Ormrod, 2001).

Depending on the review of traceability studies and the framework formulating the research under discussion and the theoretical model of this study in which the researcher proposes four factors characterizing a traceability system to reduce the aforementioned Information asymmetry the following hypotheses have been developed:

Authenticity:

The authenticity of food products is considered an important attribute, when a product is authentic it means that this product is what it claims to be and the information about this product is honest. An authentic product also gives consumers more confidence in the product.

Within this sense the first hypothesis is:

H1: authenticity reduces Information asymmetry

Information reliability:

It refers to how much one can depend on the information according to ones needs and requirements, in relation with information asymmetry the second hypothesis is:

H2: Information reliability reduces Information asymmetry

Information adequacy:

It refers to how sufficient to satisfy a requirement or meet a need the Information is, in relation to information asymmetry the third hypotheses will be

H3: Information adequacy reduces Information asymmetry

Credence of governmental third party:

A certification label has a strong positive meaning to the consumer in regard to food safety, and that itself is a signal to everyone involved in the food supply chain, be it growers or manufacturers or retailers, to intensify efforts to adopt clear and meaningful independent safety certification. Likewise, extra assurances are deemed necessary, such as that of a certification authority that will enhance credibility and reliability of the information provided, which leads to the 4th hypotheses:

H4: Governmental third party credence reduces Information asymmetry

Bounded rationality of consumers:

Early economists, led by Nicholas Bernoulli, John von Neumann, and Oskar Morgenstern, puzzled over the question of how consumers make decisions. Beginning about 300 years ago, Bernoulli developed the first formal explanation of consumer decision making. It was later extended by von Neumann and Morgenstern and called the Utility Theory. This theory proposed that consumers make decisions based on the expected outcomes of their decisions. In this model consumers were viewed as rational actors who were able to estimate the probabilistic outcomes of uncertain decisions and select the outcome which maximized their well-being.

However, as one might expect, consumers are typically not completely rational, or consistent, or even aware of the various elements that enter into their decision making.

In addition, though consumers are good at estimating relative frequencies of events, they typically have difficulty translating these frequencies into probabilities. This Utility model, even though had been viewed as the dominant decision-making paradigm, it had serious shortcomings that could not be explained.

Nobel Laureate Herbert Simon proposed an alternative, simpler model in the mid-1950s. This model was called Satisficing, in which consumers got approximately where they wanted to go and then stopped the decision-making process. An example of this would be in the search for a new apartment.

Under the Utility Theory, consumers would evaluate every apartment in a market, and form a linear equation based on all the pertinent variables, and then select the apartment that had the highest overall utility score. With Satisficing, however, consumers might just evaluate apartments within a certain distance to their desired location, stopping when they found one that was “good enough.” This theory, though robust enough to encompass many of the shortcomings of Utility Theory, still left significant room for improvement in the area of prediction. Simon and others have extended this area in the investigation of the field of bounded rationality.

The bounded rationality of consumers has three constraints:

availability of information

mind cognitive limitations

time (to make decisions)

When relating the information asymmetry to bounded rationality and consumers perceived risks, two hypotheses are developed:

H5: mitigating Information asymmetry positively affects the constraints of consumers’ bounded rationality.

H6: mitigating Information asymmetry positively affects consumers’ perceived risks

And to explore how consumer’s knowledge of traceability systems affects their willingness to pay (WTP) a price premium for traceable products, by linking their perceived risks to their willingness to pay, the following hypotheses is developed:

H7: mitigating consumers perceived risks positively affects their willingness to pay (WTP) a price premium for traceable products.

Measurement scales and operationalization of variables :

Measurement scales:

The theoretical model of the research under discussion is a model with different variables; in general vvariables are concepts in numerical form that can vary in value. They are things that we measure, control, or manipulate in research. They differ in many respects, most notably in the role they are given in our research and in the type of measures that can be applied to them.

Variables differ in “how well” they can be measured, i.e., in how much measurable information their measurement scale can provide. There is obviously some measurement error involved in every measurement, which determines the “amount of information” that can be obtained. Another factor that determines the amount of information that can be provided by a variable is its “type of measurement scale.” Levels of measurement are very important since they determine what statistical analysis to be used. Specifically Levels of measurement or scales of measure are classified as (a) nominal, (b) ordinal, (c) interval or (d) ratio.( Joseph F. Healey Steven G. Prus, 2009) .

Nominal scales: allow for only qualitative classification. That is, they can be measured only in terms of whether the individual items belong to some distinctively different categories, but we cannot quantify or even rank order those categories. Variables assessed on a nominal scale are called categorical variables; For example, all we can say is that 2 individuals are different in terms of variable A (e.g., they are of different race), but we cannot say which one “has more” of the quality represented by the variable. Typical examples of nominal variables are gender, race, colour, city, etc.

Ordinal scales: allow us to rank order the items we measure in terms of which has less and which has more of the quality represented by the variable, but still they do not allow us to say “how much more.” A typical example of an ordinal scale is the socioeconomic status of families. For example, we know that upper-middle is higher than middle but we cannot say that it is, for example, 18% higher. Also this very distinction between nominal, ordinal, and interval scales itself represents a good example of an ordinal scale. For example, we can say that nominal measurement provides less information than ordinal measurement, but we cannot say “how much less” or how this difference compares to the difference between ordinal and interval scales.

Interval scales: allow us not only to rank order the items that are measured, but also to quantify and compare the sizes of differences between them. Variables measured at the interval level are called “interval variables” or sometimes “scaled variables” as they have units of measurement (when we say the length is 10 meters, we mean it is 10 times of the predefined meter as a unit of measurement). For example, temperature, as measured in degrees Fahrenheit or Celsius, constitutes an interval scale. We can say that a temperature of 40 degrees is higher than a temperature of 30 degrees, and that an increase from 20 to 40 degrees is twice as much as an increase from 30 to 40 degrees.

Ratio scales: are very similar to interval scales; in addition to all the properties of interval scales, they feature an identifiable absolute zero point, thus they allow for statements such as x is two times more than y. Variables measured at the ratio level are called ratio variables, typical examples of ratio scales are measures of time or space. Most statistical data analysis procedures do not distinguish between the interval and ratio properties of the measurement scales.

Within this field another important step is the operationalization of variables; which is the process of strictly defining variables into measurable factors. Operationalization defines fuzzy concepts and allows them to be measured, empirically and quantitatively. In other words it aims to make the concept measurable and to understand it in terms of empirical observations. In a wider sense it refers to the process of specifying the extension of a concept describing what is and is not a part of that concept. To illustrate for example, a researcher may wish to measure the concept “anger.” Its presence, and the depth of the emotion, cannot be directly measured by an outside observer because anger is intangible. Rather, other measures are used by outside observers, such as facial expression, choice of vocabulary, loudness and tone of voice.

Since one of the measures of anger is loudness, the researcher can operationalize the concept of anger by measuring how loudly the subject speaks compared to his normal tone. However, this must assume that loudness is uniform measure. Some might respond verbally while other might respond physically. This makes anger a non-operational variable.

Operationalization is part of the empirical research process. The concepts and their relationship are important because it occurs within a larger framework of concepts. When there is a large empirical research question or purpose the theoretical model that organizes the response to the question must be operationalized before the data collection can begin. Most serious empirical research should involve operationalization that is transparent and linked to a conceptual framework. To use an example, the hypothesis Authenticity reduces information asymmetry is one way to connect (or frame) two concepts – Authenticity and information asymmetry. The process of moving from the construct authenticity to the set of questionnaire items that form authenticity scale is operationalization.

The research theoretical model operationalization:

The model presented in this study theorizes the extent at which food traceability systems provide consumers with reliable and adequate information, as well as support the authenticity of food products they are buying within the existence of governmental third party credence, will reduce consumer’s information asymmetry toward food products, especially with the existence of credence attributes in products and how this asymmetry if mitigated , will cause consumers perceived risks toward food products in terms of safety and quality to decrease, which will be a good cause for consumers to justify paying a price premium in return for traceable products.

Information asymmetry:

Refers to the study of decisions in transactions where one party has more or better information than the other creating an imbalance of power in transactions, which in our case is translated to producers knowing more than consumers about all what is related to food products from the early stages in farms to the arrival of products to the sale or consumption point by consumers, whether this information is related to raw materials, manufacturing processes, health control programs, packaging and storage conditions…..etc among huge amounts of details recorded in the supply chain.

Information asymmetry was measured through items related to:

Providing understandable information on food labels.

Facilitating reading all information.

Ability to communicate credence attributes.

Aiming toward becoming better informed by tracing food.

Authenticity:

Food authenticity is a term basically referring to whether the food purchased by the consumer matches its description. Misdescription can occur in many forms, from the undeclared addition of water or other cheaper materials, to the wrong declaration of the amount of a particular ingredient in the product, to making false statements about the source of ingredients i.e. their geographic, plant or animal origin.

Authenticity was measured through items related to:

The product matching its description

Providing Transparency and Disclosure

Believing producer’s claims about their products

Providing a real feel of the product

Providing disability of counterfeiting products

Information reliability:

Refers to how much one can depend on the information according to ones needs and requirements. A system for tracking every input and process to satisfy every objective would be enormous and very costly; firms determine the necessary breadth, depth, and precision of their traceability systems depending on characteristics of their production process and their traceability objectives. Breadth describes the amount of information collected. A recordkeeping system cataloging all of a food’s attributes would be enormous, unnecessary, and expensive. However the breadth will vary depending on the nature of the product, on farm practices or other food chain operations, customer specifications and legal or codes of practice requirements. as an example in a cup of coffee, the beans could come from any number of countries; be grown with numerous pesticides or just a few; be grown on huge corporate organic farms or small family-run conventional farms; be harvested by children or by machines; be stored in hygienic or pest-infested facilities; and be decaffeinated using a chemical solvent or hot water. The breadth of most traceability systems would exclude some of these attributes; few, if any, consumers would be interested in all this information. In this sense the amount of information whether including high or little details affect the reliability of itself, depending on the type of information being revealed according to different preferences among consumers.

On the other hand the precision of a traceability system reflects the degree of assurance with which the tracing system can pinpoint a particular food product’s movement or characteristics. In some cases, the objectives of the system will dictate a precise system, while for other objectives a less precise system will suffice which also has its effect on the reliability of the information.

Information reliability was measured through items related to:

Considering changes in requirements according to the country the product comes from (the information type requirement by consumers’ changes according to the origin of products).

Revealing quality and safety management systems existence.

Ensuring accountability of all partners in the supply chain.

Supporting transparency & disclosure

Supporting disability of counterfeiting Information.

Information adequacy:

Refers to how sufficient to satisfy a requirement or meet a need the Information is. The depth of a traceability system describes how far back or forward the system tracks the relevant information. The depth of the traceability system depends on where hazards and remedies can enter the food production chain. For some health hazards, such as Bovine Spongiform Encephalopathy (BSE, or mad cow disease), ensuring food safety requires establishing safety measures at the farm. For other health hazards, such as food borne pathogens, firms may need to establish a number of critical control points along the entire production and distribution chains.

Information adequacy was measured through items related to:

Having information available on: products, processes related to food products, environmental factors.

Facilitating reading all the relevant information of the product.

Tracing more than one step forward / backward.

Focusing on communicating only credence attributes

Ability to convert credence attributes into searchable attributes.

Ensuring accountability and the disability of practicing anything illegal, to consumers, environment, and animal welfare.

Governmental third party credence:

This refers to the act of granting credit or recognition by a governmental third party. A certification label has a strong positive meaning to the consumer in regard to food safety, and that itself is a signal to everyone involved in the food supply chain, be it growers or manufacturers or retailers, to intensify efforts to adopt clear and meaningful independent safety certification. When product information is provided by means of a label, consumers tend to consider it as less credible and reliable. Likewise, as addressed by Chrysochou et.al, 2009 extra assurances are deemed necessary, such as that of a certification authority that will enhance credibility and reliability of the product information provided.

This variable is measured by the items referring to its existence / absence.

Bounded rationality:

Refers to the notion that in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. It was proposed by Herbert Simon as an alternative basis for the mathematical modeling of decision making, as used in economics and related disciplines; it complements rationality as optimization, which views decision making as a fully rational process of finding an optimal choice given the information available.

Bounded rationality was measured through items related to:

Consumers’ willingness to read all relevant information on food products.

Providing useful information that saves time in buying decisions

Securing the buying decision by governmental credence over the information provided.

Reducing uncertainty related quality attributes in the buying process

Perceived risks:

The concept of risk is highly complex. Our understanding of the complexity of the concept has increased as specialists in different disciplines have investigated what we mean when we refer to risk. Risk communication initiatives must be designed to ensure that the messages target individual groups within the population. To do this one must first find ways of segregating individual differences and needs, and then include the real concerns of the public in the risk information provided. People tend to be particularly resistant to the idea that they are at risk from any particular hazard. Most people believe that they are in less danger than the average individual. For example, virtually all individuals believe that they drive their vehicles better than average; or that they have less likelihood of getting a heart attack than the average person. This unreal optimism is based on the information available and on a reasoning process that induces us to think that the hazard in question is not a real threat, even though it may affect persons known to us, (Canadian Food Inspection Agency).

All of this influences people’s response to risk. Perception is an important factor to be taken into account when communicating risks. Studies by anthropologists and sociologists have shown that risk perception and the acceptance of a risk have their roots in cultural and social factors. It has been argued that the response to a hazard is among the social influences transmitted by friends, family, colleagues, and respected public officials. In many cases, however, the perception of risk can be formed through a process of reasoning on the part of the individual himself/herself (Cembalo et.al, 2009; Fischhoff, B. 1995).

Perceived risk was measured through items related to

Being more secured with the existence of a safety management system such as HACCAP.

Reduction of consumer complaints on food contamination.

Ensuring objective information that reduces the information gaps between producers and consumers.

Ensuring accountability and disability of counterfeiting information.

Reducing uncertainty in the buying process.

Reducing illegal practices toward consumers, environment, and animal welfare.

Willingness to pay:

In economics, the willingness to pay (WTP) refers to the maximum amount a person would be willing to pay, sacrifice or exchange in order to receive a good or to avoid something undesired, such as pollution. In the past 15 years consumer demand for niche products (including organic, natural, and locally grown) has grown substantially (Dimitri and Greene). Consumers’ put high values on locally produced foods or foods produced with a particular technology because they perceive the products to be healthier, to be more environmentally friendly, or to be more supportive of small scale agriculture and local rural communities. This preference is considered a good cause that is translated to a willingness to pay a premium price for such products.

Reducing information asymmetry according to the model in this research is a good cause that might be translated to a willingness to pay for traceable food product.

The willingness to pay was measured through items related to

Readiness to buy traceable products with a price premium.

Preferring traceable products but without a price premium.

Declaring a percentage of the price of a product as a premium for being a traceable product. The following table summarizes the operationalization of the research model variables

Variable

Definition and Items

Literature

Information asymmetry

Deals with the study of decisions in transactions where one party has more or better information than the other.

operationalized in 7 items

McCluskey and Swinnen, (2004)

Verbeke et al.,(2007)

Verbeke and Ward (2006)

Grunert,(2002)

Hobbs, (2003)

Authenticity

refers to whether the food purchased by the consumer matches its description

operationalized in 5 items

Food standard agency, (2010)

Swaminathan , (2007)

McCluskey (2000)

Information reliability

Refers to how much one can depend on the information according to ones needs and requirements

operationalized in 7 items

Sodano, Verneau, (2004)

Grunert, (2002)

Salaun & Flores, (2001)

Golan,(2004)

Information adequacy

It refers to how sufficient to satisfy a requirement or meet a need the Information is,

operationalized in 5 items

Sodano, Verneau, (2004)

Grunert, (2002)

Golan,(2004)

Credence of governmental third party

The act of granting credit or recognition by a governmental third party

operationalized in 1 item

McCluskey ,(2000)

Dickinson and Bailey, (2002)

Hobbs, (2003)

Latvala, Kola, (2003)

bounded rationality

Refers to the constraints of availability, cognitive limitations of minds and time to make decisions.

operationalized in 4 items

Paola and Mariotti, (2009)

perceived risk

Refers to consumers discerning risks coming from food products in terms of safety and quality.

operationalized in 7 items

Fischhoff B. (1995)

Ralston et. Al(2002)

Cembalo et. Al(2009)

willingness to pay

Refers to consumers readiness toward paying price premiums in return of traceable products

operationalized in 3 items

Shapiro, K (1983)

Darby et.al (2006)

(Table xxx) summary of the operationalization of the research model variables