BUSI 453 Week 7 Discussion Board Forum 4 Example

Discussion Board Forum 4: Reviewing the Research Process Through an Ethical Lens

BUSI 453 Applied Marketing Analysis, Discussion Board Forum 4 Thread and Replies

Thread by [Student Name]

The forum prompt and the classmates are composites; the textbook, the research and the Scripture cited are real.

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Malhotra (2015) describes marketing research as a six-step process: defining the problem, developing an approach, formulating the research design, collecting data, preparing and analyzing data, and preparing and presenting the report. Reviewing the course through an ethical lens shows that each step carries its own temptation. A problem can be framed to justify a decision already made. A design can hide the sponsor's interest from respondents. Questions can lead. Sampling can pressure people to take part. Data can be handled carelessly. And reports can show only the results the client hoped for. Most of these risks are familiar from the chapters, where Malhotra (2015) closes many topics with their ethical side. Two of them deserve particular attention now, because changes in technology and in how research is done have made them more pressing.

The first is data collection. Marketing research increasingly relies on customer data gathered as people shop, browse and use apps, not only on surveys they knowingly complete. Martin et al. (2017) examined data privacy and found that customers who felt vulnerable about how firms used their data responded with lower trust and more negative behavior toward the firm, and that giving customers transparency about data use and control over it reduced those effects. Respondents are not simply sources of data; the textbook treats them as stakeholders with a right to privacy and to know how their information will be used.

The second is analysis and reporting. Simmons et al. (2011) showed through simulations and experiments that common, seemingly harmless choices, such as collecting more data after peeking at results, trying several measures of the same outcome or dropping certain cases, can raise the rate of false-positive findings far above the 5 percent researchers assume. A researcher does not need to lie to report a false finding; trying enough analyses and reporting the one that works is enough. In a commercial setting, where a client hopes for a particular answer, the pressure toward selective analysis is strong.

The remedies follow from the course. Defining hypotheses and analyses before collecting data, as the QMR Report's staged drafts require, limits analytic flexibility. Reporting every test run, including those that were not significant, lets readers judge the evidence. On the collection side, telling respondents what data are gathered and how they will be used, and collecting only what the research needs, respects them as people rather than data points. Malhotra (2015) also stresses obligations to the client: the researcher should not overstate the precision of findings, should disclose the method's limits and should not reuse one client's data for another. And there are obligations to the public, since research reported in advertising or the news shapes what people believe. A survey of 50 friends reported as "most consumers prefer" misleads everyone who reads it.

In Proverbs 11:1, a crooked scale draws the Lord's disgust while a true one wins his approval (English Standard Version Bible, 2001/2016). Research is a kind of weighing. A researcher who reports only the favorable results tilts the scale as surely as a merchant with a false weight, and the client who relies on that report makes decisions on a false measure. Honest research reports what the data show, including what disappoints.

Reply to Natalie

Natalie, your QMR study measures both employees' job satisfaction and their intention to stay with the same online questionnaire, and you found a strong correlation between them. That fits the theory, but I would check whether part of the correlation comes from the method itself.

Podsakoff et al. (2003) reviewed common method bias and explained that when the same respondents answer questions about both variables in the same survey, at the same time and in the same format, things the answers have in common, a passing mood or the wish to look consistent, can make the two variables seem more closely linked than they are. They recommended procedural remedies, such as separating the measures in the questionnaire and protecting anonymity, and statistical checks. Lindell and Whitney (2001) proposed a marker-variable technique: include an item theoretically unrelated to the main variables, and treat its correlation with them as an estimate of method variance to subtract. If the marker correlates near zero with satisfaction and intention to stay, method bias is probably small; if it correlates noticeably, the adjusted correlation gives a more honest estimate.

Since your data are already collected, you could note the risk as a limitation and report whether satisfaction correlates with any unrelated item, such as commute length. In Ephesians 4:25, Paul tells the church to drop lying and tell one another the truth (English Standard Version Bible, 2001/2016). Acknowledging a limitation openly is part of speaking truthfully about research. Does your survey include an item that could serve as a marker? If not, the most honest step is a sentence in the limitations noting that both variables came from the same respondents at the same time, so the true relationship may be somewhat weaker than reported.

Reply to Joshua

Joshua, you argued that collecting more customer data always improves marketing because it allows better personalization. I agree that data make personalization possible, but the research suggests that how data are collected affects whether personalization works.

Aguirre et al. (2015) studied what they called the personalization paradox: highly personalized ads increased click-through when firms collected information openly, but when information was collected covertly, personalization made customers feel vulnerable and reduced click-through. Tucker (2014) studied a change in Facebook's privacy settings that gave users more control over their personal information, and found that afterward users were nearly twice as likely to click on personalized ads. Giving customers control did not weaken personalization; it made it more effective. Both studies point the same way: customers accept the use of their data when they understand it and feel they have a say, and resist it when it happens out of sight. For a marketer, transparency is not only an ethical duty but a condition of the personalization working at all.

Malhotra (2015) emphasizes respondents' right to privacy throughout the research process. Matthew 7:12 asks us to treat others as we would want to be treated (English Standard Version Bible, 2001/2016). Most of us would want to know what a company collects about us and to have some say in it. Which data in your company's program could be collected openly with a clear explanation of how it will be used? A short preference center, where customers choose the topics they want offers about, could gather better data than tracking alone, because the customer supplies it knowingly and has a reason to keep it accurate.

References

Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34-49. https://doi.org/10.1016/j.jretai.2014.09.005

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114-121. https://doi.org/10.1037/0021-9010.86.1.114

Malhotra, N. K. (2015). Essentials of marketing research: A hands-on orientation. Pearson.

Martin, K. D., Borah, A., & Palmatier, R. W. (2017). Data privacy: Effects on customer and firm performance. Journal of Marketing, 81(1), 36-58. https://doi.org/10.1509/jm.15.0497

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

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359-1366. https://doi.org/10.1177/0956797611417632

Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546-562. https://doi.org/10.1509/jmr.10.0355