Month: May 2025

A good friend of mine who works in the tech industry recently made a big decision to leave his job at a mid-sized software company, and compensation was a clear motivator. He’d been with the company for several years, and while the base pay was competitive at first, his responsibilities kept growing leading projects, managing a small team, but his salary barely budged. Despite consistent high performance, raises were minimal and bonuses inconsistent. What pushed him over the edge was when he found out a new hire with similar experience was brought on at a significantly higher salary. According to him, that broke his sense of fairness and respect in the company.

This aligns with what we learned in class about distributive justice and equity theory when employees perceive their compensation as unequal compared to their peers with similar input, motivation and job satisfaction drop sharply (Week 8: Introduction to Compensation). It wasn’t just about the money; it was about the message. The undervaluation of his work signaled to him that the company didn’t truly recognize or reward loyalty and growth.

When he accepted a new offer elsewhere, the base salary was only moderately higher, but the incentive structure, equity options, and transparent pay bands made him feel like he was joining a place that valued his contributions. It wasn’t just a financial move it was about regaining a sense of worth and fairness. His story is a clear reminder of how critical compensation design is not only for attracting talent, but also for retaining and motivating it.

Reflecting on my experience working in the IT department of a federal university, I’ve participated in several training sessions, some highly effective, others not so much. One training that stood out as particularly beneficial was a cybersecurity awareness workshop. This session was well-structured, following the Analyze, Design, Develop, Implement, Evaluate (ADDIE) model highlighted in this week’s materials. The workshop covered real-world threats, phishing simulations, and hands-on exercises that mirrored potential breaches we might encounter. It didn’t just provide information; it simulated actual attack scenarios, allowing us to apply what we learned in real-time. This hands-on experience reinforced the knowledge, making it both relevant and memorable. Additionally, the training was evaluated using the Pre-/Post-Measure method, which allowed us to see our progress and areas for improvement. This level of structured evaluation ensured the training was not only effective but also measurable.

In contrast, one training that fell short was an IT ticketing system workshop. Unlike the cybersecurity session, it was purely lecture-based with minimal interaction. The material was delivered through PowerPoint slides with little real-world application or practical engagement. According to this week’s readings, effective training requires opportunities for hands-on practice and a clear link to job tasks. This session lacked both, making it challenging to connect concepts with actual work tasks. Furthermore, there was no follow-up or evaluation to measure effectiveness, so any learning that did occur was not tracked or reinforced.

If I could advise on improvements, I would suggest integrating simulation exercises for the ticketing system training, similar to what was done in the cybersecurity workshop. This would create an opportunity to practice ticket resolution in a risk-free environment, enhancing learning retention and job readiness. Proper evaluation, like Pre-/Post-Measures, would also ensure knowledge transfer and identify gaps early on. This approach would align with best practices for effective training design and significantly improve learning outcomes.

Reflecting on my interview at a dairy farm service company, where I already have relevant dairy experience, I noticed both strengths and weaknesses in their selection process. The interview was informal and largely unstructured: a few general questions about my background and whether I could “handle early mornings.” While this created a relaxed atmosphere, it lacked structure, which significantly reduced reliability and validity. As discussed in this week’s materials, unstructured interviews are prone to bias, lack consistency across candidates, and rarely predict job performance well.

If I were advising the company, I’d recommend transitioning to a structured interview format. This would involve standardized, job-related questions based on a job analysis, paired with clear scoring criteria. For example, instead of asking “Can you work under pressure?” they might use a situational question like: “Tell me about a time you managed a calving emergency on your own.” That taps into actual experience and provides more predictive validity.

To increase utility, I’d also suggest incorporating work samples, for instance, having candidates perform a basic equipment check or simulate troubleshooting a milking system issue. Though more time-intensive, work samples offer high validity and directly align with job performance expectations.

Ultimately, a more systematic approach would lead to better person-job fit, more equitable evaluations, and higher long-term performance, especially in a labor-intensive, skill-based field like dairy services.