This thesis employed a mixed methods study design. A mixed methods study involves the combination of both quantitative and qualitative research methods in a single study and includes integrating the data at one or more stages in the research process (Creswell, Plano Clark, Gutmann, & Hanson, 2003; Johnson & Onwuegbuzie, 2004). The reason for choosing the mixed methods approach is due to its ability to improve research results through the provision of informative, complete, and balanced results to the extent that either quantitative or qualitative method alone does not permit (Brewer & Hunter, 1989; Tashakkori & Teddlie, 1998; Johnson, Onwuegbuzie, & Turner, 2007). This thesis used an exploratory sequential mixed methods approach, which is a type of design whereby qualitative and quantitative data are collected separately in two phases (Creswell et al., 2003; Creswell & Plano Clark, 2011). In the first stage, qualitative data is collected to explore common themes relating to perceptions, attitudes, preferences, and information search behavior of consumers. The second phase involved a quantitative data collection of sampled consumers. The data from the qualitative study provided useful information to support the development of the subsequent quantitative survey.
3.1.1 Qualitative data collection
Qualitative research is often used to understand a complex phenomenon that cannot be meaningfully reduced through a few discrete variables and linear cause-and-effect relationships (Patton, 2002). Qualitative research explains human behavior from the perspectives of participants, and thus, it is important for gaining a better understanding of social realities (Flick, Von Kardorff, & Steinke, 2004). Consequently, the use of qualitative studies to explore attitudes, beliefs, and opinions of a group of individuals and the factors that influence their consumption habits is increasingly being recognized (Munoz, 1998; Barrios & Costell, 2004).
Qualitative data collection methods comprise in-depth interviews, observation, and focus groups (Petty, Thomson, & Stew, 2012). Among these methods, focus groups and in-depth
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interviews are widely used in social science research. Therefore, this thesis used the focus group method since it is one of the most widely used and efficient techniques for gathering qualitative data in market research (Morgan, 1996; Barrios & Costell, 2004; Groves et al., 2009).
A focus group is a qualitative research method whereby a selected group of people, usually between six to twelve, are interviewed in a discussion setting under the guidance of a trained moderator and allows the moderator as well as participants to freely and openly discuss issues (Krueger & Casey, 2000; Neuman, 2000; Creswell, 2007). This helps to provide more information than could be obtained using one-to-one interviews. Focus groups are considered to be relatively low in terms of cost and a flexible and efficient way to get information from a group of people than other methods (Krueger & Casey, 2000; Finch & Lewis, 2003; Groves et al., 2009). In addition, they allow for honest, sincere, and detailed discussions. The interactional context provides a key opportunity to explore and explain the dimensions of differences and diversity of views that occur during discussions (Krueger & Casey, 2000; Finch & Lewis, 2003). Focus groups are also useful in the initial stages of a questionnaire design to learn what respondents know about the topic of the survey (Groves et al., 2009). In spite of the advantages, focus groups are not always easy to conduct as they also present some limitations. Importantly, they are not suitable for making statistical generalizations since samples are usually both small and unrepresentative (Morgan & Krueger, 1993; Casey & Krueger, 1994). Also, they are time-consuming in terms of identification and recruitment of participants (Morgan & Krueger, 1993;
Casey & Krueger, 1994). Despite these limitations, results from the focus groups assisted in identifying the relevant attributes for the choice experiment and developing and refining the survey instrument for the quantitative study.
3.1.2 Quantitative data collection
Contrary to qualitative research, quantitative methods are designed to measure phenomena (Flick, 2009). Quantitative methods are theory-based and are concern about the causalities between two constructs or testing a conceptual/empirical hypothesis to examine the degree of association between measured indicators (Neuman, 2000; Flick, 2009). Quantitative methods use techniques that produce data in the form of numbers, which can be used to empirically describe abstract concepts (Neuman, 2000). The data are collected in a standardized and uniform format and analyzed using statistical tools. The strength of the quantitative method is that results can be generalized to the entire population (Neuman, 2000). Methods used to collect quantitative data include face-to-face interviews, mail, telephone, and internet surveys (Groves et al., 2009; Szolnoki & Hoffmann, 2013). However, there are benefits and limitations
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associated with these survey methods, which vary across and within countries. Trading off the benefits and limitations of various survey methods, a face-to-face interview is chosen.
Face-to-face surveys involve an interaction between two persons in which one person (interviewer) meets with the other person (respondent) and conducts the interview (Loosveldt, 2008; Schröder, 2016). The interviewer asks questions using a questionnaire, and the respondent answers the questions. The questionnaire guides and standardizes the interaction between the interviewer and the respondent (Loosveldt, 2008; Schröder, 2016). Some of the common approaches used to conduct face-to-face interviews include paper-and-pencil interviewing (PAPI) and computer-assisted personal interviewing (CAPI) (De Leeuw, 2008;
Schröder, 2016). The use of CAPI, in which survey questions are displayed on a computer screen, allows automatic filtering and leads to a substantial reduction in errors and improvement in data quality (De Leeuw, 2008).
Face-to-face surveys have many advantages. Specifically, they are the most flexible form of data collection methods and are suitable for longer interviews with more complex tasks (De Leeuw, 2008; Loosveldt, 2008; Szolnoki & Hoffmann, 2013). They allow for the use of visual and auditory stimuli. Because they are characterized by personal interaction, the interviewer can give direct support to the respondent by explaining questions and tasks in more detail. In addition, the interviewer can probe for further information and encourage the respondent to answer every question (De Leeuw, 2008; Loosveldt, 2008). However, the presence of an interviewer does not only provide some additional advantages but also creates the risks of interviewer bias (Loosveldt, 2008; Schröder, 2016). A typical example of such bias is social desirability bias, which is the systematic over-reporting of socially approved behaviors and under-reporting of undesirable ones (Groves et al., 2009). In this case, respondents try to please the interviewer by providing answers that align with societal norms (Loosveldt, 2008).
Other shortcomings include geographical restrictions, high cost per respondent, and time pressure on respondents (Alreck & Settle, 2004; Holbrook, Green, & Krosnick, 2003; Szolnoki
& Hoffmann, 2013). Nevertheless, in a developing country context like Ghana, face-to-face interviews provide a more practical way to collect quantitative data than mail, phone, or internet surveys, due to limited infrastructure (e.g., limited and unreliable internet and mail services).
3.1.2.1 Discrete choice experiment
In this thesis, a DCE (usually referred to as choice experiment) is used to elicit consumer preferences and estimate WTP values for the selected product attributes. DCE is one of the stated preference methods widely used for examining choice behavior. DCE is conceptually
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rooted in Lancaster’s theory of consumer demand (Lancaster, 1966) and consistent with the random utility theory (McFadden, 1974). DCEs involve a stepwise process of identifying product attributes, specifying levels, generating experimental design, presenting choice alternatives to respondents, and estimating choice models (Hanley, Mourato, & Wright, 2001;
Hensher, Rose, & Greene, 2005; Kløjgaard, Bech, & Søgaard, 2012). Thus in DCEs, respondents are presented with a sequence of hypothetically constructed scenarios composed of two or more competing alternatives that vary in attributes. Respondents are then asked to make repeated choices between these alternatives. In doing so, respondents make trade-offs between the attributes (Hanley et al., 2001; Lusk & Schroeder, 2004; Carson & Louviere, 2011).
DCEs are widely applied in food preference studies to estimate the trade-off between different quality attributes (e.g., Lusk & Schroeder, 2004; Loureiro & Umberger, 2007; Pouta et al., 2010; Van Loo, Caputo, Nayga, Meullenet, & Ricke, 2011; Ortega, Hong, Wang, & Wu, 2016). The frequent use of DCEs could be explained by the fact that they are flexible since they can value multiple attributes simultaneously, unlike contingent valuation methods, and also the questions closely mirror real-life consumer purchasing decisions (Lusk & Schroeder, 2004).
However, DCEs are subject to hypothetical bias because respondents do not have to back up their stated choices with actual commitments (Lusk & Schroeder, 2004). Besides, fatigue or learning may affect respondents’ behavior as they are asked to respond to several repeated choice tasks (Bradley & Daly, 1994; Day et al., 2012). To address these limitations, a “cheap talk script” is used to minimize the hypothetical bias. Also, the choice tasks are blocked into two versions to avoid respondent fatigue.