Why Should We Care About Theories of Management

A study that has been presented at The Association for Research in Vision and Ophthalmology ARVO 2020 annual meeting aimed to investigate statistical analyses that are common in the ophthalmic literature as well as to measure the statistical knowledge, attitudes, and practices (KAP) of Australian optometrists. The study looked at 376 (55%) articles published in 2018 in the Clinical and Experimental Optometry (CEO), Ophthalmic and Physiological Optics (OPO), Optometry and Vision Science (OVS), and Ophthalmology. The results showed that 90% of articles used Descriptive statistics methods, while only 10% employed Inferential statistics methods divided into t-tests (33%) and contingency tables (32%). Non-parametric tests were employed in 21% of the studies and ANOVA in 17% of the studies. Finally, 15% of studies did not use any statistical method. The study conclusion emphasized the importance for Optometrists to increase their statistical knowledge to successfully appraise the ophthalmic literature.

Good statistical knowledge is important for both ophthalmic literature and practice management. Professor Clayton Christensen emphasized that “Theory often gets a bum rap among managers because it’s associated with the word “theoretical,” which connotes “impractical.” But it shouldn’t. Because experience is solely about the past, solid theories are the only way managers can plan future actions with any degree of confidence.” He always referred to the theory of gravity that tells us why by simply dropping an object we are 100% sure that it will fall on the ground. A theory is a statement of causality it shows us what causes what to happen and why.

If we want to have a clear idea about the future we need to be able to employ theories that show us with absolute certainty that if we do such or such thing we will get to such result. We are avid consumers of theories without even being aware of them. Every time we follow a plan or try to implement a business model we rely on a set of theories that tell us it is going to be successful.

Descriptive statistics don’t allow us to generalize or demonstrate a causal effect relationship. Inferential statistics aim to figure out or reveal if a causal effect relationship exists. Newton’s gravitational principle is based on inductive reasoning that pertains to inducting the truth from observations. We come up with theories following inductive reasoning based on events or cases from which we induct a truth. To empirically test a theory we conduct deductive reasoning that leads to test how practical the theory is through hypothesis testing. If a theory seems to be robust because it has been able to prove its practicality throughout numerous empirical attempts to test it, it slowly becomes used by a number of scholars, and then it starts to become a paradigm. But with time anomalies appear; anomalies are good indicators that a theory needs to adjust itself to become more general and answer those anomalies. However, when anomalies are significant and the theory has been widely known as a paradigm a scientific revolution occurs.