MHRM 6801 Week 7 Causal Argument Paper Example

Claims Follow the Estimate Gap, Not the Crew: A Cause Argument That Tests the Owners' Training Explanation Against Bellamy's Records, Rules Out Crew Skill With Matched Comparisons and Keeps One Rival Partly Alive

Student Name

Walden University

MHRM 6801: Organizational Effectiveness and Leadership

Instructor Name

Month Day, Year

Claims Follow the Estimate Gap, Not the Crew: A Cause Argument That Tests the Owners' Training Explanation Against Bellamy's Records, Rules Out Crew Skill With Matched Comparisons and Keeps One Rival Partly Alive

The Claim and the Explanation It Must Beat

Bellamy's owners believe damage claims come from crews who need more training, and they have budgeted for a new training program. This paper argues a different cause: claims follow the gap between the volume an estimator predicts and the volume a crew finds at the house. When the load runs well past the estimate, a crew sent with one truck and three people for a day must either pack faster and tighter or make a second trip the schedule does not allow. Damage follows. To be persuasive, this argument must do three things: show that claims rise with the gap, show that the pattern is not simply a matter of which crews get which jobs and deal honestly with rival explanations. Shadish et al. (2002) describe ruling out plausible alternative explanations as the core of causal argument when experiments are not possible.

Claims Rise With the Gap

The claims rate quadruples between moves with accurate estimates and moves with large gaps, and it rises steadily in between, which is the pattern a real cause would produce. Moves with gaps over 15 percent are about a fifth of all moves but produce roughly half of all claims.

Table 1

Claims Rate by Gap Between Estimated and Loaded Volume, July 2024 to December 2025

Composite organization. About 9,000 moves.

Ruling Out Crew Skill

The obvious objection is that newer crews get the bad jobs, as the Week 5 assessment showed, so the gap may simply be standing in for crew skill. The test is to compare the same crews on jobs where the estimate matched and jobs where it did not.

On accurate-estimate jobs, newer and experienced crews have nearly the same claims rate, less than a point apart. On large-gap jobs, both crews' rates jump, the newer crews somewhat more. Crew experience makes a small difference; the estimate gap makes a large one. The owners' training explanation predicts that newer crews would have much higher claims everywhere, and the records do not show that.

Table 2

Claims Rate by Crew Tenure and Estimate Gap

Composite organization.

Where the Gap Comes From

Estimators are paid partly by commission on booked revenue. A lower estimate means a lower quote, which wins more bookings. The share of each estimator's estimates that fell short by more than 15 percent ranges from 9 to 34 percent, and the three estimators with the most bookings have the largest gaps. Kerr (1975) made this mismatch famous in management writing; Bellamy pays for bookings and hopes for accuracy. This link explains why the gap persists, though it is not needed to show that the gap drives claims.

The Rival That Stays Partly Alive

Long summer days are a rival explanation, since tired crews may handle furniture less carefully. Among large-gap moves, those that ran over eleven hours had a claims rate of 19 percent, against 13 percent for those that did not. Fatigue appears to add to damage. But long days are partly a result of the gap, since an underestimated load takes longer to move, so the two causes are tangled. The fairest conclusion is that the estimate gap is the main cause and fatigue an aggravating one.

Conclusion

The estimate gap, not crew training, best explains Bellamy's claims. Training may still help newer crews at the margin, but a training program alone would leave most claims untouched. Pfeffer and Sutton (2006) warn that organizations often invest in the explanation that feels right; the records point elsewhere.

Limits

Crew chiefs enter loaded volume themselves and may round up after a hard day, which would exaggerate the gap on exactly the jobs that went worst. A check against weight tickets on a sample of moves would confirm the gap measure. The analysis also covers eighteen months only; a second year of records would show whether the pattern holds as the newer depots mature and their crews gain experience.

References

Kerr, S. (1975). On the folly of rewarding A, while hoping for B. Academy of Management Journal, 18(4), 769-783. https://doi.org/10.2307/255378

Pfeffer, J., & Sutton, R. I. (2006). Hard facts, dangerous half-truths, and total nonsense: Profiting from evidence-based management. Harvard Business School Press.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.