This volume in the series has big objectives: describe the bad science practices now in use in most studies in business-to-business marketing strategy and describe a true paradigm shift to good science practices by replacing the variable-based linear-symmetric null hypothesis testing (NHST) approach
in theory construction and testing—with case-based asymmetric models with somewhat precise outcome testing (SPOT). Whether the question refers to success or failure, wise executives ask, how did we get here? What’s in store for the next decade? Unfortunately, the majority of scholarly
articles examining the causes of success and failure offers scant useful information that is accurate in forecasting success or failure strategy outcomes. The majority of studies on strategy performance outcomes focus on variable relationships and testing for the directionality (positive or
negative relationships) and effect size of relationships—using multiple regression analysis and structural equation modeling (MRA/SEM) using null hypothesis statistical testing (NHST). Research on the value of NHST indicates that such studies are worse than useless: such research does not
focus on case-based outcomes and achieving a statistically significant relationship greatly depends on the sample size of firms in the studies. Researchers using NHST are answering the wrong questions in examining the net effects of independent variables on dependent variable of interest (e.g., net
earnings per revenue). Here are the right questions to ask. What configurations of antecedent conditions combine to generate positive outcomes for our firm and similar firms? What configurations of antecedent conditions combine to generate negative outcomes for firms in our industry? Sound
reasoning and empirical evidence supports the wisdom of business executives ignoring the scholarly empirical literature on forecasting successful and unsuccessful management strategies using the NHST of the size and directionality of relationships. Good science practice relies on the complexity
theory tenets covered in the chapters in this volume. Good science practice includes matching case-focused theory with case-focused data analytic tools and using somewhat precise outcome tests (SPOT) of asymmetric models. Good science practice achieves requisite variety necessary for deep
explanation, description, and accurate prediction. The fear of submission rejection is another reason for rejecting case-based asymmetric modeling and SPOT. Overcome such fear by learning to apply complexity theory tenets, constructing separate case-based, mid-range, models of successful versus
unsuccessful outcomes, and testing for accuracy via SPOT. This volume provides tools necessary for you to accomplish this task.
Where do brilliant executive wisdom and actions come from? Making Tough Decisions Well and Badly (MTDWB) assesses the literature that examines executives’ conscious and non-conscious actions in decision making, implementation and assessment of outcomes. MTDWB includes anecdotal histories of
good and bad decisions and the executives who made them. This volume uncovers the common threads in framing, forecasting, decision making and actions, looking at Steve Jobs, Martin Luther King, Jr, Senator Wayne Morris, Winston Churchill, Abraham Lincoln, Sam Walton, Mahatma Gandhi, and Bill
Gates. Authors discuss how common threads could be useful for achieving superior competences MTDWB assesses ten valuable decision making tools such as checklists and coaches; and tools to avoid such as use of product portfolio paradigms and use of fit-only regression analysis, that appear often in
the popular business and academic literature on making tough decisions. MTDWB closes with ten recommendations for those responsible for making tough decisions.