Business Research Methods (MBA module 1 of 8)

Business Research Methods

• Business Research Methods is the systematic (step-by-step) objective (purpose, goals) process of generating information (data, analysis) based on an issue (problems, questions) or problem and developing business solutions (key emphasis of BRM)  to these issues.
• The application of scientific methods in searching for the truth about a business phenomena (Zikmund 2010).

There key steps in developing your topic area for research:

1. Brainstorm for ideas that you are interested in analysing
2. Read some general background regarding the topic area
3. Focus on a possible subject area in the subject matter
4. Make a list of useful keywords and items
5. Be flexible to create a specific problem or area of concern

The research process

• Identify well-defined field/debate
• Review available literature
• Select appropriate research design (method, instrument)
• Collect relevant data or generate novel data set
• Interpret and publish findings
• Generate recommendations and impact → repeat

Academic process of research looks at 3 areas of knowledge and understanding:

• Ontological: how things really are and how things are working in reality now - where are we now? PAST.
• (Theory of being) belief that the world is socially constructed and subjective
• Epistemological: what is the knowledge and information that is already available in various journals and articles in www - where we want to go? CURRENT/TRENDING.
• (Theory of knowing) belief that phenomena are observable through human interaction
• Methodology: what research tools were used to find out these knowledge and areas of excellence by the authors - how might we get there? SEARCH.
• (Theory of discovery) belief that discovering new knowledge involves the researcher as part of the observed

Quantitative research

• The focus of the research involves specific data and figures to analyse and evaluate; = deductive = top down = starts with a question = when you make comparison between one variable and another.
• Example (deductive): The approach that this paper will focus on is deductive in nature based on a top down approach. It is based on a question on Hypothesis and Flexible working arrangement will improve the productivity of employees in the healthcare sector. The paper will analyse both data and behaviour components developing into a mixed research.
• Primary data: these are usually using surveys, questionnaires, interviews and online methods of self administered instruments making use of a Likert scale as a way to get data. Data can also be collected by interviews with structured questions either face to face or telephone to get information and answers with a simple population - frequency, correlation, regression.
• Secondary data: these are available data and charts relevant to a topic or subject matter that can be found in public documents or articles, organisational documents, mass media and visual charts.
• Quantitative Pros
• Subjects not influenced by observations of experiments (Querios et al, 2017)
• “What-if” questions can be tested and affirmed (Querios et al, 2017)
• Low development time and cost effective (Querios et al, 2017)
• Quantitative studies use mathematical models and statistics for analysis, providing numerical results that are considered more objective (Fahmeena Odetta Moore, 2016)
• Controlled Results are valid and reliable (Dr. Mary Dowd, 2018)
• Data analysis is less time consuming as it uses the statistical software such as SPSS (Connolly, 2007)
• Data collection occurs rapidly with quantitative research. (LG, No date) (Fernando & Faria,2017)
• The samples of quantitative research are randomized. (Oppong, 2013)
• It offers reliable and repeatable information. (Evid Base Nurs, 2015)
• You can generalize your findings with quantitative research. (Apuke, 2017)
• The research is anonymous. (Blackstone, 2012)
• You can perform the research remotely. (Male, Trevor, 2015 ; Jason Mander, 2017)
• Information from a larger sample is used with quantitative research. (Destiny Apuke,2017)
• Quantitative Cons
• Difficult to replicate the same conditions of study (Querios et al, 2017)
• Difficult to control variables (Querios et al, 2017)
• Arising opportunities of ethical issues (Querios et al, 2017)
• Response time / response rate (Healey at el 2002)
• Less data, artificiality. limitation (Dpimm 1994)
• There are cases where the hypotheses are not detailed. (Fahmeena Odetta Moore, 2016)
• Lack of isomorphism between its measures and "reality" (CLAUDIA KRENZ 1986)
• You cannot follow-up on any answers in quantitative research. (Alshenqeeti, 2014)
• The characteristics of the participants may not apply to the general population. (Natasha, 2019)
• You cannot determine if answers are true or not. (Evid Base Nurs, 2015)
• It creates the potential for an unnatural environment. ( Jason Mander, 2017)

Qualitative research

• An exploratory research where insights to a problem is based on a social issue or depth of a problem = inductive = bottom up = general statement or subject.
• Example (inductive): The approach that this paper will focus on is inductive in nature based on a bottom up approach to explore the areas of product marketing and its relevance in retail businesses in Singapore. The paper will analyse on the qualitative aspects of product marketing and its value to retail businesses.
• Case studies: to identify a phenomenon or case based on past events and cases of comparisons and in depth analysis of historical data - work-life balance, transformation, change management, best practices
• Types of field notes
• Mental notes
• Jotted notes
• Full field notes
• Types of documents available for study
• Personal
• Public
• Organisational
• Mass media outputs
• Visual outputs
• Public domain documents
• Annual reports
• Mission statements
• Reports to shareholders
• Transcripts of chief executives’ speeches
• Press releases & advertisements
• Public relations material in print & on internet
• Non-public domain documents
• Company newsletters
• Organisational charts
• External consultancy reports
• Minutes of meetings
• Memos
• Internal & external correspondence
• Manuals for new recruits
• Policy statements
• Company regulations
• Action research: a real business problem that results in an actionable solution to a real issue or problem - bank credit vs. debit - more acceptable today?
• Ethnographic methods: are the social cases taking place in social groups or societies that require attention and articulation? Is religion still relevant in business today?
• Independent variables: are the main variables or subject area to be analysed and evaluated in a research.
• Dependent variables: are the subsets or sub variables that depend on the main variable and the need to see the correlation that exists.
• Hypothesis: to identify which element of TBL is the most important (REASONS/RATIONALE).
• Qualitative Pros
• Not bound by limitations of figures (Poppulo, 2019)
• Flexible and oriented to knowledge discovery (Poppulo, 2019)
• Suitable to for exploring new lines of research (Querios et al, 2017)
• Simplifying and managing data without destroying complexity and context (numbers / statistics) (Ochiend Pamela 2009) 
• Larger sample size, objective and accuracy - pros qualitative (PF.Liu ; debatin
• 1998)
• Produces the thick (detailed) description of participants’ feelings, opinions, and experiences; and interprets the meanings of their actions (Denzin, 1989).
• More cost effective and easier to consolidate (Jason Mander, 2017)
• Can capture changing attitudes within a target (Bradon, 2019)
• It provides more content that is useful for practical application. (Bradon, 2019)
• More speculative about what areas they choose to investigate, allows data capture to be prompted by a researcher’s instinctive or ‘gut feel’ for where good information will be found. (Yauch and Steudel, 2003)
• Qualitative Cons
• Heavily dependent on individual skills and impartiality of researcher (Anderson, 2010)
• Difficult to get concise and precise conclusions (Querios et al, 2017)
• Difficult to establish cause and effect connections (Querios et al, 2017)
• Statistical data could be based on assumptions and inaccurate (Ochiend Pamela 2009)
• Requires skills and commitment to gather or the results will not be useful (Ochiend Pamela 2009)
• Positivism cannot account for how the social reality is shaped and maintained, or how people interpret their actions and others (Blaikie, 2017)
• Sample size can be a big issue. (Oppong, 2013)
• Sample bias (Tuckett, A, 2004).
• It does not offer statistical representation. (Brandon, 2019)

Measuring the quality of the data

• Authenticity: whether it is truthful, correct, genuine information that is provided - peer reviewed, acceptance in the industry.
• Credibility: authors and history shown and number of citations and sources of data publication - e.g. guru in the knowledge industry.
• Representativeness: similar to others and common knowledge and understanding e.g. use the same variables, discussion, not outliers.
• Meaning: understanding the facts, terms, theories and independent and dependent variables used in the journals.

Methodology

• Laddering: a method to reduce the document and data to a more value based analysis of the knowledge and understanding based on the 4 criteria outlined above / Reduce data in more valued information.
• Analytical induction (AI): a research logic used to collect data, develop analysis and organise research findings. Also known as Explanation Build Up. We build up and confirm a set of cause and effect linked between events and actions and causes to a phenomenon / Organise and focus on key findings that are common among the respondents.
• Coding: create meaning through key words and explanation.
• Cause and effect model: relationships of variables.

Data selection and analytics

• Population: the total universe of units that is represented as part of the research - U Units e.g. 15,000.
• Sample: a segment of the population that is selected for the research - N Number of respondents e.g. 150 - 1% of U.
• Sampling types: there are 4 types of sampling methods that can be used:
• Sample frame: list of all the units that are selected - the N of the U.
• Sample bias: distortions in the representativeness of the population.
• Sample error: differences between the sample and population.
• Probability vs. non-probability sampling.
• Probability sampling
• Simple random
• Systematic sampling
• Stratified sample
• Multi-stage cluster sampling
• e.g. demographic sampling bio data - sex, income, age - chosen Ns.
• Non-probability sampling
• Convenient
• Snowball sample
• Quota sampling
• e.g. has higher error and biases in the result - random choice.
• Frequency distribution: to count the concurrence of values in a particular group or sample e.g. sex, age, income / pie chart, graph, histogram.
• Descriptive statistic: to provide a clear summary of the sample, scale and variables used e.g. %’mode, median, mean.
• Cross tabulation: when you have 2 nominal variables that you want to compare and see relationships - DT - people, process / BS - profits, planet.
• Correlation: the statistical relationship and coefficient between 2 variables. Value based on the r value -1 to 1 as a score - input data to statistical software e.g. 0.7.
• Test the hypothesis: to test a statement or problem (RQ) that you have as the key question of the research of dissertation e.g. significance (Sig.) ½ side tail.
• Associated p-values allow you to test for significant relationships
• The R-squared statistic in the output shows the overall significance of the regression
• Statistical significance
• Chi-Square Tests
• Comparing means (t-tests)
• Tests of Correlation

Research stakeholder criteria

• Relevance: the relation of the practical application of these results and solutions e.g. directly, sector.
• Rigour: the logic and strict description of the results and cause and effect - who will it affect.
• Impact: the major effects, thinking, actions that affect the audience - results influence.
• Ethics: the observance of strict guidelines of research including issues like honesty, objectivity, openness, respect for IP and confidentiality.