Research and Methodology: Linking sources of consumer confusion to decision satisfaction

Critical Appraisal of the Article

The research questions or hypotheses and their theoretical and practical relevance, i.e., a rationale for why the research was important;  

The primary purpose of this research is to examine various sources and the perceived impacts of consumer confusion on the related decision-making processes. According to the article, the stipulated complications emanating from ambiguous information and choice overload can affect decision satisfaction. Per Wang and Shukla (2013), consumers are often prone to similarity confusion when they come across similar brands being promoted with similar messages. Wang and Shukla (2013) opine that when consumers face confusion similarity, their frustration is heightened and they get to make poor decisions at times and end up purchasing products of low-quality. Confusion similarity also contributes to an increase in overall evaluation costs. Furthermore, consumers tend to be less confident about the brands they desire to associate with whereby they are faced with similar brands that have same attributes. According to Wang and Shukla (2013), confidence is a subjective probability assessment on the accuracy of judgement.  In this research, Wang and Shukla (2013) provided seven hypotheses structured to address issues relating to consumer confusion, choice goals, and decision satisfaction. The authors based these working hypotheses on widely-accepted suppositions about the research subject. The seven hypotheses include:

  • H1: Higher levels of similarity confusion will (a) increase evaluation costs; (b) increase negative affect; and (c) reduce choice confidence
  • H2: Higher levels of overload confusion will (a) increase evaluation costs; (b) increase negative affect; and (c) reduce choice confidence
  • H3: Higher levels of ambiguity confusion will (a) increase evaluation costs; (b) increase negative affect; and (c) reduce choice confidence
  • H4: Higher choice confidence will (a) reduce evaluation costs and (b) reduce negative affect
  • H5: Higher choice confidence will lead to higher decision satisfaction
  • H6: Higher evaluation costs will lead to higher decision satisfaction
  • H7: Negative affect will have a significant influence on decision satisfaction

The above hypotheses encourage critical approaches to the research topic. Wang and Shukla (2013) used the hypotheses to develop and pursue a specific direction and comprehensive understanding of the subject matter of the research. Through these hypotheses, the authors managed to generate new knowledge and direction about the topic. According to Bryman and Bell (2015), predictions can enhance the accuracy and precision of different research activities or scientific investigations. In this study, Wang and Shukla (2013) used the hypotheses to develop a connection between different theoretical assumptions and systematic examination of the research topic. Also, the premises provide accurate responses to the research questions and problems.

Correspondingly, the main research questions include:

  1. When consumers are subject to various sources of confusion, how their choice goals are affected?
  2. How does the extent of fulfillment of their chosen goals influence their decision satisfaction?

In this type of study, research questions act as an effective guide to the development of pertinent knowledge and information about the selected topic and design.  Through accurate responses to the questions, the research generated reasonable conclusions to the research problems. Lastly, assumptions such as H1, H2, and H3 predict that consumer confusion can have significant impacts on the consumers’ choice goals.

The epistemological and ontological assumptions, which underpinned the research, and their consistency with research questions or hypotheses;

The epistemological assumptions can provide relevant information regarding certain theoretical knowledge based on specific procedures, validity, and scope. Specifically, the premises should provide ground for the development of accurate assumptions about the world. Epistemology in research is more concerned with how researchers make justifiable claims about their knowledge of things they wish to study. Thus, it is about how a particular individual knows something to be the case rather than what he or she just believes it to be. Coming up with epistemological assumptions in a research considers how a researcher might claim to know the truth about a given issue. Epistemology is associated but distinct from ontology. Ontology is often more concerned with reality such that researchers have to recognize in their work that their exists that nature, which is an independent entity in the social world (realism), or recognize that things on exist when they think about them (idealist). Most importantly, ontological assumptions examine a special kind of being or the perceived nature of existence. Arguably, epistemological and ontological assumptions in this research are consistent with the research questions and the identified hypotheses. For instance, the central epistemological assumption underpinning this research is that the choice goal attainment can influence the relationship between consumer confusion and decision satisfaction. The study further assumes that consumers are likely to feel less confident about different brands and choices when faced with commodities with similar attributes. The two epistemological assumptions can support various theorized and effects models predominant in this research (Charmaz, 2005). For instance, the assumption that the relationship between consumer confusion and decision satisfaction can affect goal attainment reinforces the consumption and choice theory. However, the research does not have clear statements on epistemological and ontological assumptions.

The research design and its strengths and limitations for answering the research questions or hypotheses;

The research has a coherent and explicit research design used to test the stipulated hypotheses. According to Bryman and Bell (2015), research design refers to the plan of investigation aimed at obtaining specific answers to the specified research objective and research questions. Specifically, the research design outlines various activities that the researcher will undertake, ranging from a comprehensive address of the research objective to data analysis. The descriptive survey is appropriate for this study because it allowed the researcher to relate the research variables (Locke, 2001). The research design is also useful in determining the subjective opinions or viewpoints of a specific population through oral or written questioning processes. The design is also appropriate in generating appropriate information about a research problem and in establishing a hypothesis (De Vaus, 2001). Notably, the common types of descriptive research design that can influence the collection of data and information include questionnaire surveys, observation, and case study analyses. These methods can generate a massive amount of data and information about research problems.

In survey research, data is collected about a particular issue using a questionnaire. Survey is the most popular research tool that many researchers prefer to use compared to other methods such as observational. The survey questionnaire needs to have a mix of questions, such as open-ended and close-ended questions for a researcher to collect quality data. Observational method is the most effective method that a researcher can use to conduct descriptive research. The method incorporates both quantitative observational method and qualitative observational method. Quantitative observational method is concerned with the collection of data based on numbers or values. Qualitative observational method, on the other hand, is not concerned with the identification of values or numbers, rather it is concerned with identification of the features that are associated with a particular group or sample of items selected for a given study. Another descriptive research method that many researchers use is case study analyses. Case studies involve an in-depth research and study of individuals or groups. By using the case study analysis approach, a researcher is able to develop hypotheses within a short time. Another benefit of integrating case study analysis into research project is that it gives a researcher a wide range of knowledge that an individual can use to critique a particular concept in detail. Although case study analysis has benefits, its disadvantage is that it can be biased as the findings tend to depend on a researcher’s opinions.

The main advantage of a descriptive research design is that it is useful in analyzing non-quantified topics and issues relating to the topic. The design also provides a viable opportunity for the researchers to make accurate observations of the research phenomenon by generating appropriate subjective data and information from the selected respondents (Silverman, 2000). Besides, a descriptive research design is also less time-consuming compared to other design methods and efficient in the collection of subjective data and information. The aggregated data, especially from a large population, is also essential in different decision-making processes and provides a holistic understanding of the research problems (Johnson and Duberley, 2000). Lastly, the design method can integrate various qualitative and quantitative research methods to collect accurate data and information from the respondents. However, descriptive research designs are not effective in the cause and effect experiments about certain phenomenon (Easterby-Smith, Thorpe, and Jackson, 2012). In addition, the design method is susceptible to biased or inaccurate responses from the respondents with hidden intentions (Barends and Rousseau, 2018). The bias is because descriptive research is not effective in facilitating the statistics verification of the research problems. The majority of the descriptive studies are not repeatable due to their observational nature.

The sampling strategy that was used to select the sources of data and an appraisal of the rationale for why particular sources/respondents were selected;

The used stratified sampling method seeks to aggregate relevant data and information from the respondents. Stratified sampling is a probability sampling technique whereby the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata. The method is appropriate in populations with mixed characteristics. The proportionate representation of the identified characteristics in the study sample can increase the authenticity of the study outcomes (Timmermans and Tavory, 2012). In this study, the researcher used stratified sampling to divide the population into specific strata based on pertinent characteristics. Through such subdivisions, the researcher was able to capture the respondents’ varied demographic and geographic data and information (Johnson and Duberley, 2000). In particular, the research attracted approximately 800 respondents (consumers) contacted by the researchers. The study also based the subdivisions on the number of respondents who had received phones as gifts from their organizations and those who had purchased similar phones. Notably, through such divisions, the research was able to measure choice confidence, negative effect, and decision satisfaction among the selected respondents. The sampling method offered greater precision and is effective in populations with different heterogeneous characteristics.

The high degree of representativeness in stratified sampling can also promote the generation of appropriate data and information from the selected participants. In this research, stratified sampling facilitated the subdivision of the respondents into groups based on their specific locations and time. Through such subdivisions, the researcher selected the best representative of the targeted population to respond to the survey questions (Bertrand and Fransoo, 2002). Notably, stratified sampling is effective in the selection of competent respondents capable of generating relevant data and information about the research variables. Using stratified sampling, Wang and Shukla’s (2013) research generated a response rate of approximately 44% (352 respondents). The response rate was fair because it included individuals with relevant knowledge about the research topic and related phenomenon. Although the use of stratified sampling in research has many benefits, it is also associated with some disadvantages. The choice of stratified sampling method tends to add some complexity to the analysis plan. Besides, the research process may take longer and prove to be more expensive due to the extra stage in the sampling procedure. The requirement for a researcher to be able to distinguish between strata in the sample frame may create difficulties, especially in the practical stages of the study.

The research methods that were used to generate the data and their strengths and weaknesses for addressing the research questions or hypotheses;

In this study, the authors used a structured questionnaire as the appropriate research methods to test the hypotheses and related questions. The structured questionnaires are relevant for this research because they are affordable and effective in generating appropriate quantitative data and information (Fontana and Frey, 2005). The researchers collected a massive amount of information cost efficiently. The structured questionnaires are also practical and efficient in gathering subjective data and opinions about the research variables. The survey questions can either be open-minded or multiple choices depending on the specific research questions (Gioia, Corley, and Hamilton, 2013). Another important strength of structured questionnaires is that they offer a quick way of generating relevant results from the respondents. In particular, the researchers can gain critical insight and answers about a specific study phenomenon (Tharenou, Donohue, and Cooper, 2007). Structured questionnaires also allow the researcher to collect data and information from large audiences. For example, the research can distribute the survey questions to respondents in different physical locations using various internet platforms such as email.

The collected data from the structured questionnaires is quantifiable and comparable. For instance, in this case, the structured questionnaire focused on essential constructs such as consumer confusion, choice goals, and decision satisfaction. Through such comparability, the researchers managed to develop unique correlations between different research variables. The aggregated data is also easier to analyze and visualize without engaging built-in statistical tools. In particular, the research can interpret and turn data from the structured questionnaires into valid results using charts and tables. Besides, a structured questionnaire offers clearer and actionable data that can generate accurate predictions and benchmarks (Potter, 2000). More importantly, the questionnaires ensured respondent anonymity and comfort, encouraging objective and subjective responses (Grbich, 2007). The respondents’ ease can also promote truthful responses to the research questions and hypotheses (Fontana and Frey, 2005). Besides, the structured questionnaire does not have time constraints. The selected respondents can complete and submit the questionnaires at their convenience or pleasure.

The structured questionnaires used in this study covered every aspect of the topic, research questions, and hypotheses. However, despite the associated advantages, the structured questionnaire can encourage dishonest responses to the survey questions. Notably, factors such as social desirability bias and desire for privacy may prompt some respondents to provide inaccurate answers. In such cases, the researchers should provide assurances that they will not violate respondents’ privacy (Gioia, Corley, and Hamilton, 2013). The possibility of unanswered survey questions may limit the generation of appropriate conclusions about the research questions and hypotheses. Moreover, the respondents are likely to provide varied understanding and interpretations of the survey questions (Easterby-Smith, Thorpe, and Jackson, 2012). Notably, such variances and subjective differences may result in skewed and uncertain results about the research topic.

The structured questionnaires do not capture the respondents’ feelings and emotional responses to different survey questions. Therefore, the researchers in this study may have failed to notice some important or useful data and information expressed through other nonverbal cues. Some responses the survey questions are difficult to analyze or quantify (Johnson and Duberley, 2000). For example, quantifying certain subjective responses to open-ended questions about consumer confusion and decision satisfaction can be challenging. Additionally, some respondents may provide biased or inaccurate information about the research questions, eliciting overtly negative perceptions about whole process. Other responses may be insensitive or unconscientiously damage the reputation of others. Accordingly, the researcher should use pre-screening questions to identify and eliminate respondents with hidden or destructive agenda (King, 2004). Lastly, structured questionnaires are unsuitable for respondents with visual or hearing impairments, as well as illiteracy problems.

The analytical strategy or strategies that were used to analyze the data together with an appraisal of their strengths and weaknesses; and g. the contribution of research to existing knowledge of the topic and to policy and practice

The research used an exploratory factor analysis (EFA) to reduce the aggregated data and information to smaller sets of summarized variables. EFA is a statistical method that many researchers use to determine the relationship between various variables within a large sample of items especially between the measured variables. For the unmeasured variables, researchers tend to come up with their own findings. Besides, EFA is also used by researchers in developing a scale to determine the relationships between the measured variables. Researchers largely use EFA when they do not have hypotheses about patterns or factors related to the measured variables. Measured variable is a factor affecting the outcome of a process that is measured as part of six sigma project or other process improvement initiatives. Measured variables may include factors such as height and weight of a particular item. EFA procedures tend to offer high precision results related to analysis of measured variables whenever it is incorporated in as study. In particular, the EFA method used in the study was relevant in exploring the underlying theoretical structures and relationships between various variables and respondents. The method is also effective in reducing data from a large set of variables or population to smaller or manageable summaries. EFA is also associated with various limitations. The usefulness of EFA depends on the researcher’s ability to develop an accurate set of product attributes. Therefore, if important attributes are missed, the value of the procedure is significantly reduced. Besides, considering that multiple attributes can be highly correlated with no apparent reason., researchers might get problem in identifying and naming the attributes of a particular sample or item. Furthermore, explanatory factor analysis cannot produce accurate results when a researcher analyses a set of attributes that are largely unrelated. Although EFA tend to assign a factor to a set of items that have highly similar attributes, but are different from each other, EFA often captures the variance of a single item amongst the available sample of items. EFA can only be effective if the data is valid; however, can be pose problems to researcher when he or she tends to conduct a study that relies of less valid and reliable data.

Correspondingly, the research used a confirmatory factor analysis (CFA) to test the hypotheses, produce relevant estimates, and develop regression and variance analyses. The CFA approach is effective in ascertaining whether the constructs are consistent with the researchers’ comprehension of the variables (Howard, 2016). The method further assumes that different observed variables could also offer complete and accurate explanation of the existing interrelationship between the research questions and hypotheses. A confirmatory factor analysis (CFA) is effective in streamlining data and information in the LISREL 8.80 statistical software package. The LISREL 8.80 was effective and relevant in the testing of the structural model on the perceived impacts of consumer confusion and choice goals constructs on consumer satisfaction (Schroeder, Sjoquist, and Stephan, 2016). In particular, the statistical model will help in the analysis of collected data using the selected research design. Subsequently, the research included the computation of descriptive statistics or data using the statistical software and methods to identify factors associated with the outcome variables (Thompson, 2004). The methods were also effective in providing accurate comparisons between the direct-effects and theorized models. The generate t-values indicated the perceived variations in the sample data collected from the research participants.

The main disadvantage of the confirmatory factor analysis is that it is susceptible to incidentally extreme correlations. The approach also depends on the researchers’ ability to identify and develop an accurate set of attributes or variables (Howard, 2016). The observed variables using confirmatory factor analysis are also completely unrelated making it hard for the researcher to draw accurate inferences. The LISREL 8.80 statistical software package is also not effective because it ignores ordinate data (Jöreskog and Sörbom, 2006). The statistical package is also limited to structural equation modeling and latent variables, further limiting the researchers’ ability to generate relevant conclusions.


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