Addressing Organizational Problems at Tech Lab, Inc.
Your assignment is to use data and your knowledge of organizational behavior to inform a series of recommendations to Tech Lab, who have hired you as an outside consultant to address two major problems plaguing their company. You will be assessed in terms of your ability to (a) draw upon the organizational behavior literature to provide evidence-based recommendations, (b) use data to inform your decisions, and (c) use creativity in developing and presenting these recommendations to enhance the extent to which Tech Lab is likely to use them. Particular emphasis will be placed on (a).
Identifying Top Talent
In your meetings with Tech Lab, they have voiced concerns that they can’t seem to hire the right people for the job. They are concerned that their recent hires seem talented in the beginning but ultimately turn out to be mediocre, and either turn over or have to be fired for poor performance. They keep track of the information they gather prior to making a hiring decision and want to know if they can improve their decision-making in the future (see “Tech Lab hiring data.sav”). The current protocol calls for applicants to meet education standards (a university degree or better), undergo on-site interviews (with three different managers), and complete an online “cultural values” test that assess whether their personal values match the company’s values. They are planning to hire a large number of new data scientists using their current protocol, but want to know if they should change it. The job description for the data scientist role is attached (see Appendix A: Data Scientist job description).
In your recommendation, it is essential that you (a) provide guidance as to which procedures they should keep and which procedures they should drop, and (b) offer insight into which other procedures might be adopted as part of a new talent identification process. Your recommendations should be supported by evidence from the organizational behavior literature (and your own analyses of the information provided) whenever possible. Guidance on data analysis is also attached (see Appendix B: Tips on Using Multiple Regression and ANOVA).
Improving Satisfaction and Motivation
A further issue of great importance with Tech Lab is improving satisfaction and motivation across their five main departments: accounting, human resources, research and development, consulting, and sales. An outside survey company recently conducted an employee satisfaction survey, but did not produce any analyses of the data (see “Tech Lab employee satisfaction data.sav”) or insights on what to do to improve satisfaction and motivation. The survey asked employees to rate the extent to which they were satisfied with their job overall, their coworkers, their supervisors, the job environment, and their pay and benefits. They have asked you to have a look at these data and offer a number of actionable recommendations as to what they should do. It is essential that you (a) identify the departments and aspects of the job that are experiencing the lowest levels of satisfaction, and (b) offer insight into what specific steps can be taken to improve satisfaction and motivation across the company. Your recommendations should be supported by evidence from the organizational behavior literature (and your own analyses of the information provided) whenever possible. Guidance on data analysis is also attached (see Appendix B: Tips on Using Multiple Regression and ANOVA).
Things to Remember when Developing Your Recommendation
• Your report should include the following:
o A professional-looking title page
o An executive summary
o Key recommendations (executive summary and recommendations are not to exceed 1,000 words):
▪ Explain why you’re recommending a course of action
▪ Make sure to provide evidence for your recommendations
▪ Refrain from making changes that are not improvements
▪ Based on your look at the data, what ideas come to mind about further data they should collect? What would you like to know that would help you make a more informed decision about what to do next?
o Any relevant tables, charts, or analyses (not counted in the word total)
▪ Explain your results in simple language that is easily understood to a lay person
▪ Don’t bury the reader in statistical jargon—communicate the essence of results clearly and concisely
• References page—all references should follow AMJ style guidelines: aom.org/publications/amj/styleguide/
• You should make an effort to present your recommendations in a report that is clear, compelling, and visually pleasing to the eye. Charts, tables, and/or analyses need to jump out to the reader and be easy to understand.
• Any statistical analyses need to be included, but presented in a way that is understandable to someone unfamiliar with the technique.
• You should cite relevant research in organizational behavior (such as the assigned readings), and refrain from using sources that merely reflect management fads or published opinions.
• This is not a stats course and you will not be marked down for performing incorrect analyses. You will, however, be expected to do as much with the data as you can, given the knowledge and resources at your disposal. The important thing is being able to make defensible recommendations based on whatever analyses you choose to conduct.
• Your recommendations should consider practical aspects as well as scientific ones. Practical aspects include matters such as costs, feasibility of implementation, and the likelihood of Tech Lab accepting the proposed solution. Scientific aspects include matters such as ensuring reliability, validity, and fairness.
Appendix A: Data Scientist Job Description
As a Data Scientist, you will evaluate and improve Tech Lab’s products. You will collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems. This position will bring analytical rigor and statistical methods to the challenges of measuring quality, improving consumer products, and understanding the behavior of end-users, advertisers, and publishers.
Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive understanding of data structures and metrics, advocating for changes where needed for both products development and sales activity.
Interact cross-functionally with a wide variety of people and teams. Work closely with engineers to identify opportunities for, design, and assess improvements to products.
Make business recommendations (e.g. cost-benefit, forecasting, and experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
Research and develop analysis, forecasting, and optimization methods to improve the quality of user facing products; example application areas include ads quality, search quality, end-user behavioral modeling, and live experiments.