Teaching evidence-based management with a focus on producing local evidence

We present an approach to teaching evidence-based management (EBMgt) that trains future managers how to produce local evidence. Local evidence is causally interpretable data, collected on-site in companies to address a specific business problem. Our teaching method is a variant of problem-based learning, a method originally developed to teach evidence-based medicine. Following this method, students learn an evidence-based problem-solving cycle for addressing actual business cases. Executing this cycle, students use and produce scientific evidence through literature searches and the design of local, experimental tests of causal hypotheses. We argue the value of teaching EBMgt with a focus on producing local evidence, how it can be taught, and what can be taught. We conclude by outlining our contribution to the literature on teaching EBMgt and by discussing limitations of our approach

Editorial—The Anna Karenina Bias: Which Variables to Observe?

The opening of Count Lev Nikolayevich (Leo) Tolstoy's novel inspired linguist, molecular physiologist and biogeographer Jared M. Diamond's eponym for the Anna Karenina principle (Diamond 1997). The principle suggests that no one property guarantees success but many guarantee failure. The Anna Karenina (TAK) bias is a logical consequence. TAK bias is more insidious than the kindred Survivor bias, which cautions that measured variables for passively observed survivors often differ from easily overlooked nonsurvivors. TAK bias, in contrast, cautions that the observed variables themselves might differ for survivors. The most revealing variables might exhibit negligible variation among survivors because survivors are necessarily alike. Perhaps variability is inversely related to the variable's importance for survival. TAK bias is more problematic for descriptive research, in contrast to normative (i.e., prescriptive) research, which seeks the true causal variables. Normative research only offers conditional aid to the decision maker on specific variables. To avoid TAK bias, we must not passively let accountants decide which variables we observe. We must actively collect data guided by predictions from deductive theory.Anna Karenina bias, Survivor bias, descriptive research, inferential research, active data collection, statistical biases

Product line bundling: why airlines bundle high-end while hotels bundle low-end

Product lines are ubiquitous. For example, Marriott International manages high-end ultra-luxury hotels (e.g., Ritz-Carlton) and low-end economy hotels (e.g., Fairfield Inn). Firms often bundle core products with ancillary services (or add-ons). Interestingly, empirical observations reveal that industries with ostensibly similar characteristics (e.g., customer types, costs, competition, distribution channels, etc.) employ different bundling strategies. For example, airlines bundle high-end first class with ancillary services (e.g., breakfast, entertainment) while hotel chains bundle ancillary services (e.g., breakfast, entertainment) at the low-end. We observe, unlike hotel lines that are highly differentiated at different geographic locations, airlines suffer low core differentiation because all passengers (first-class and economy) are at the same location (i.e., same plane, weather, delays, cancellations, etc.). In general, we find product lines with low core differentiation (e.g., airlines, amusement parks) routinely bundle high-end while product lines with highly differentiated cores (e.g., hotels, restaurants) routinely bundle low-end. High-end bundling makes the high-end more attractive, increasing line differentiation (less intraline competition) while low-end bundling decreases line differentiation. Therefore, bundling allows optimal differentiation given a differentiation constraint (complex costs). Last, firms may use strategic bundling for targeting in their core products; e.g., low-end hotels bundle targeted add-ons unattractive to high-end consumers such as lower-quality breakfasts and slower Internet

Comments on Competitive Responsiveness

features many and diverse articles that analyze competitive responsiveness. Although recent editorials (e.g., Shugan 2002) suggest that competitive responsiveness is only a part of a comprehensive competitive marketing strategy, it remains a vital part. For that reason and many others, is particularly proud of this special issue edited by David J. Reibstein and Dick R. Wittink. Before introducing and vigorously applauding both the editors and authors of this excellent special issue, we emphasize that competitive responsiveness raises numerous issues, including whether one can forecast outcomes of new policies based on past observations made under old policies (i.e., the Lucas critique) and decisions regarding which variables should be considered endogenous (Shugan 2004), i.e., determined within the model. Perhaps, normative models are inherently perishable—evolution in market structure requires modifications over time. Also, although complete consistency within the world of the model is aesthetically pleasing, imposing industry-specific exogenous constraints (that might appear unrelated to the modeling assumptions) is sometimes necessary.