HRMG 4201 Week 3 AMO Workforce Profile Example

A Workforce Profile of Three Rivers Auto Care Built on the AMO Model: Five Job Groups Rated on Ability, Motivation and Opportunity, and the Gap Between Diagnostic Skill and the Work That Reaches It

Student Name

Walden University

HRMG 4201: Strategic Human Resource Management

Instructor Name

Month Day, Year

A Workforce Profile of Three Rivers Auto Care Built on the AMO Model: Five Job Groups Rated on Ability, Motivation and Opportunity, and the Gap Between Diagnostic Skill and the Work That Reaches It

Why the AMO Model

The ability-motivation-opportunity model holds that employees perform when they are able to do the work, motivated to do it and given the opportunity to use their ability (Appelbaum et al., 2000). A meta-analysis by Jiang et al. (2012) found that HR practices in all three areas relate to employee and organizational outcomes, with opportunity-enhancing practices often the most neglected. For Three Rivers, whose shops promise to fix older cars right the first time, the model shows where each job group helps or hinders that commitment.

The Grid

Table 1

Three Rivers Auto Care Workforce Profile on the AMO Model

Composite company; headcounts and descriptions drawn from the case roster.

What the Grid Shows

Ability is not the main problem. The chain has 40 diagnostic technicians with the skills its first-visit commitment needs, about one for every two shops' worth of electrical work, and a third of general-service technicians hold some certification. The gap is in opportunity. Because advisers assign repair orders to the next open bay without classifying job type, many electrical complaints reach general-service technicians, who are less equipped to trace intermittent faults. The diagnosticians' ability is not reaching the work that needs it.

Motivation reinforces the gap. Flat-rate pay credits a technician the labor guide's hours for each job, so a general-service technician who replaces a battery in twenty minutes is paid well, while a diagnostic technician who spends an hour tracing an intermittent short may be paid little if no repair follows. Neither group is rewarded for first-visit accuracy. Advisers earn commission on parts and labor sold, which gives them no reason to route jobs carefully.

The Evidence Behind the Opportunity Gap

The case's repair records support this reading. Across the chain, roughly 9 percent of repair orders return inside a month with an unchanged complaint. For electrical complaints the rate is about 14 percent, against about 5 percent for brakes, suspension and routine engine work. At the three shops where foremen informally steer electrical jobs to a diagnostic technician, the electrical comeback rate is about 8 percent. The comparison is not a controlled test, since those shops may differ in other ways, but it points in the direction the grid suggests: the same workforce produces better first-visit results when its diagnostic ability is matched to the work. Each electrical comeback also costs the chain the second repair for free, under the customer promise, and often the customer.

Implications

The profile points to three questions for the rest of the course. Should the chain hire for diagnostic ability, given that it already has more than it uses? How should pay change so that diagnostic time and first-visit accuracy are rewarded? And how should work be routed so that ability meets opportunity? The AMO model suggests that fixing ability alone, through training or hiring, would waste money unless opportunity and motivation are addressed too. The order of the questions matters as well: changing routing first would show quickly whether the chain's existing ability is enough, before money is spent on new hires or training.

Limits

The grid summarizes groups, not individuals, and some general-service technicians are as capable as the diagnosticians. It also relies on the case roster rather than direct observation of how jobs are assigned on a busy day, which a later assignment examines. Turnover among lube and tire technicians, while high, matters less to the first-visit commitment than the routing of electrical work (Boxall & Purcell, 2016).

References

Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. L. (2000). Manufacturing advantage: Why high-performance work systems pay off. Cornell University Press.

Boxall, P., & Purcell, J. (2016). Strategy and human resource management (4th ed.). Palgrave Macmillan.

Jiang, K., Lepak, D. P., Hu, J., & Baer, J. C. (2012). How does human resource management influence organizational outcomes? A meta-analytic investigation of mediating mechanisms. Academy of Management Journal, 55(6), 1264-1294. https://doi.org/10.5465/amj.2011.0088