BUSI 600 Week 3 Group DB Forum 2: Part 1 Example

Group Discussion Board Forum 2: Part 1

BUSI 600 Business Research Methods, Group Discussion Board Forum 2: Part 1 (two threads)

Thread by [Student Name]

The two forum questions and the company examples are composites standing in for the course's own prompts; the research and Scripture cited are real, and the requirements follow Liberty's public course guide for BUSI 600.

Thread 1: When Should a Researcher Choose Qualitative Methods Over a Survey?

A researcher should choose qualitative methods when the study needs to discover how or why something happens and the concepts involved are not yet understood well enough to measure. A survey can only count answers to questions someone already knew to ask; interviews and observation can reveal the questions themselves.

Consider a composite software company whose free-trial users stop logging in after their first week. Managers have theories, a confusing setup screen, a missing integration, a price shock at the end of the trial, but no evidence. A survey built on those theories would ask users to rate the setup screen, the integrations and the price, and it would produce tidy percentages. It could not tell the company that the real reason was something no one listed, for example that trial users were evaluating the product for a manager who never saw it. Worse, the precise-looking numbers might persuade managers that they had found the cause, so that a redesign of the setup screen would go ahead while the actual problem continued.

Edmondson and McManus (2007) describe this as methodological fit. When prior theory on a question is nascent, research needs open-ended data collected in the field, such as interviews and observation, because the aim is to identify new constructs and the relationships among them. When theory is mature, with established constructs and measures, quantitative surveys and statistical tests are the better fit. Mismatches produce weak studies: a survey in a nascent area forces respondents into the researcher's categories, while an interview study in a mature area rediscovers what is already known. The software firm's churn problem is nascent for that company, even if churn in general has been studied, because no one yet knows which of many possible causes apply to its users.

Qualitative work is sometimes dismissed as anecdotal, but it can be rigorous. Gioia et al. (2013) describe a systematic approach in which the researcher first codes informants' own terms, then groups those first-order concepts into researcher-defined themes and finally into aggregate dimensions, presenting the resulting data structure so that readers can see how each conclusion was reached. Applied to the software firm, twenty interviews with users who abandoned the trial, coded this way, could produce a short list of explanations grounded in what users actually said, along with quotations that show managers what the problem sounds like from the customer's side.

The two approaches work best in sequence: interviews discover the categories, and a survey then measures how common each one is. Mixed-methods designs of this kind are common in applied business research because each phase covers the other's weakness. Once the qualitative phase identifies the likely reasons for churn, a survey of several hundred trial users can estimate their frequency, and the company can decide which to address first.

Proverbs 20:5 compares the purposes in a person's heart to deep water that a person of understanding draws out. Qualitative research is that drawing out: patient questions that let people explain their reasons in their own words. It also honors the people being studied, treating them as informants with knowledge rather than as sources of ratings on a scale.

Thread 2: What Can a Field Experiment Show That a Survey Cannot?

A field experiment can establish cause and effect in a real business setting, which a survey cannot do no matter how large its sample. Schindler (2019) lists three conditions for inferring causation: the variables must vary together, the cause must come before the effect, and other explanations must be ruled out. Surveys usually satisfy only the first.

The question of whether working from home improves productivity shows the difference. A survey of remote and office workers might find that remote workers report higher productivity, but that pattern has several explanations. Productive employees may be the ones allowed to work from home, people who prefer it may rate themselves more favorably, and managers who permit it may differ from those who do not. Bloom et al. (2015) avoided these problems at Ctrip, a large Chinese travel agency, by asking call-center employees who volunteered for home work and met eligibility rules to take part in a lottery: those with even-numbered birthdays worked from home for nine months while the others stayed in the office. Because a lottery decided who worked where, the two groups were alike at the start, and differences afterward could be attributed to the arrangement. Home workers' performance rose by 13%, mostly because they logged more minutes per shift, taking shorter breaks and fewer sick days, and quits among home workers dropped by roughly 50%. When the trial ended and employees could choose, about half of the home group asked to return to the office, citing isolation, and the performance gain among those who chose home work rose to roughly 22%.

Surveys also face a measurement problem that experiments reduce. Podsakoff et al. (2003) reviewed the evidence on common method bias, the distortion that arises when the same people report both the predictor and the outcome at the same time, through tendencies such as consistency motifs and social desirability. Their review showed that such bias can inflate or deflate observed relationships substantially. In the Ctrip study, performance came from the company's own call records, not from employees' ratings of themselves, so the outcome could not be colored by how workers felt about the policy.

Field experiments have costs. They require a firm willing to randomize, a period long enough to see effects and a setting in which the treatment can be held steady. Their results may not generalize, since call-center work is easier to measure than design or management work, and a volunteer sample at one firm may differ from the wider workforce. Ethics also matter: employees assigned to the less attractive condition must be treated fairly, and the firm should be ready to extend a benefit to everyone if the trial proves it works. A survey remains useful for describing attitudes across a large population quickly. The design choice follows the question: if the decision depends on whether a policy causes an outcome, only an experiment, or a design that approximates one, can answer it.

In 1 Thessalonians 5:21, believers are told to examine all things and keep only what proves good. An experiment is a disciplined form of testing, one that guards a manager against believing what is comfortable or popular and directs the organization toward practices that actually serve its people and customers.

References

Bloom, N., Liang, J., Roberts, J., & Ying, Z. J. (2015). Does working from home work? Evidence from a Chinese experiment. The Quarterly Journal of Economics, 130(1), 165-218. https://doi.org/10.1093/qje/qju032

Edmondson, A. C., & McManus, S. E. (2007). Methodological fit in management field research. Academy of Management Review, 32(4), 1246-1264. https://doi.org/10.5465/amr.2007.26586086

Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15-31. https://doi.org/10.1177/1094428112452151

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879

Schindler, P. S. (2019). Business research methods (13th ed.). McGraw-Hill Education.