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This chapter outlines the major analytical efforts performed as part of the overarching research project with the aim to investigate the organizational and environmental circumstances around the extreme negatively skewed performance outcomes regularly observed across firms. It presents the collection and treatment of comprehensive European and North American datasets where subsequent analyses reproduce the contours of performance distributions observed in prior empirical studies. Key theoretical perspectives engaged in prior studies of performance data and the implied risk-return relationships are presented and these point to emerging commonalities between empirical findings in the management and finance fields. The results from extended analyses of more fine-grained data from North American manufacturing firms uncover the subtle effects of leadership and structural features, and computational simulations demonstrate how the implied adaptive processes can lead to the empirically observed performance distributions. Finally, the findings from the analytical project activities are set in context and the implications of the observed results are discussed to reach at a final conclusion.

The global environments that surround contemporary business activities are uncertain, fast-changing, and frequently exposed to abrupt unexpected events with the potential to inflict extreme impacts where the ability to respond and adapt the organization effectively becomes a primary strategic concern. However, various firms that operate across diverse industry contexts approach this adaptive challenge in distinct ways that lead to quite diverse outcomes with many negative performers and some high performers with positive risk features. The heterogeneous approaches appear to consistently form extreme left-tailed performance distributions with inverse risk-return features but we are not really able to explain why and how these regularly observed phenomena come about. Hence, we want to study these organizational artifacts by collecting an extensive updated dataset to test the proposed relationships, explore alternative explanations, and learn from the extreme exemplars often referred to as outliers. There are extensive literatures in (strategic) management and finance that have dealt with the distribution of firm returns from slightly different angles but with some emerging commonalities that can inspire further analyses of the performance data. As a precursor for this, we discuss the odds of effective strategic adaptation in complex dynamic environments and introduce resilience as a proper outcome when simple solutions are scarce, and consider conditions that may facilitate these aims. The premises for the ensuing analyses are laid out and the main contents of the following chapters are presented.

This chapter explores other theoretical explanations to the commonly observed phenomenon of negatively skewed performance outcomes and inverse risk-return relationships in empirical firm data. The analysis conducted in many prior studies have implicated direct causal dependencies between performance and risk, or vice versa, with the possibility of simultaneous two-way relationships that are harder to discern. It is also shown how spurious artifacts deriving from the arithmetic links between mean and variance associate left-skewed distributions with negative mean variance correlations. However, the heterogeneous display of response capabilities among firms that compete in the same industry contexts may provide an alternative explanation for the observed performance characteristics. This is expressed as strategic responsiveness where performance outcomes with high negative skewness and excess kurtosis derive from heterogeneous adaptive processes among firms as they respond to a dynamic environment with different degrees of success. We test these results in different simulated competitive contexts disrupted by major unexpected events and find robust results across different environmental scenarios. The analysis looks at two different response processes, one modeled as conventional adaptive planning following an annual budget cycle, and another modeled as interactive updating where executives have frequent informative budget discussions with operating managers in the firm. The computational simulations show that interactive updating generates outcomes with higher returns and lower performance risk for moderate learning levels and restructuring costs. However, the resulting performance distributions are not as left-skewed as those observed in the empirical data that show higher resemblance to the adaptive planning outcomes.

This chapter introduces empirical studies of firm performance and related risk outcomes conducted in the management and finance fields presenting underlying theoretical rationales as they have evolved over time. Early finance studies of market-based returns predominantly found positively skewed return distributions that conform to assumptions about higher returns associated with more risky investments. Subsequent studies found that performance outcomes measured as accounting-based financial returns generally display left-skewed distributions that reflect negative risk-return relationships. This artifact was first observed by Bowman (1980), thus often referred to as the “Bowman paradox” because it contravened the conventional assumptions in finance. The management studies have largely confirmed the inverse risk-return observations but often following rather confined research streams. A contingency perspective inspired by prospect theory and behavioral rationales have investigated the lagged effects of performance on risk outcomes and vice versa. Another stream has focused on the spurious relationships between negatively skewed performance distributions and the inverse risk-return associations. A third approach considered the performance and risk outcomes as deriving from the firms responding in distinct ways to exogenous changes. These studies reach comparable results but underpinned by very different rationales. The finance studies observe deviations from the pure doctrine of positive risk-return associations embedded in the widely adopted capital asset pricing model (CAPM) and note deficiencies with alternative interpretations that even question the validity of CAPM. A more recent strain of studies in behavioral finance observes how many (even professional) investment managers have biases that lead to inverse relationships between perceived risk and return outcomes. While these diverse fields of study have different starting points, they uncover an increasing number of interesting commonalities that can inspire the ongoing search for explanations to observed left-skewed financial returns and negative risk-return correlations across firms.

In this chapter, we perform more detailed analyses and present the distribution characteristics and risk-return relationships of accounting-based financial returns (ROA) across different industry contexts and between periods with different economic conditions. We first display the frequency diagrams of the return measure (ROA) and its two components, net income and total assets, that show entirely different contours in the density graphs that must be reconciled. This is partially accomplished by analyzing the skewness, kurtosis, cross-sectional, and longitudinal risk-return characteristics of each of the three variables. The analyses further considers potential effects of accounting manipulation, and different organizational and executive traits, that identifies significant effects on the accounting-based return measures. We find extremely left-skewed return distributions with high negative correlations between the average return and risk measures, which reproduces the “Bowman paradox” as originally conceived. The same analysis is performed on net income and operating cash flows, the latter being less susceptible to accounting manipulation, which should display similar effects even though these performance distributions show positive skewness. We find negative but insignificant cross-sectional risk-return relations that nevertheless reappear in analyses performed within the specific industry contexts. The study further uncovers effects from prevailing economic conditions where left-skewness and kurtosis as well as negative risk-return correlations are much more significant during periods of high economic growth and business expansion where competition is more pronounced.

In this chapter, we first examine the distribution characteristics of firm performance across different competitive industry contexts and periodic economic conditions of growth, recession, and recovery. There is mounting evidence that the contours of accounting-based economic returns consistently display (extreme) left-skewed leptokurtic distributions with negative risk-return relationships, which implies the existence of many negative performance outliers and some positive outliers. We note how negative skewness, excess kurtosis, and inverse risk-return relationships prevail in industries with more intense competition and in economic growth scenarios where more innovative initiatives compete. As the study of outliers typically is ignored in mainstream management studies, we extract a total of 23 extreme performers using a conventional winsorization technique that identifies 16 negative and 7 positive outliers. We study the performance trajectories of these firms over the full period and find that negative performers typically operate in capital-intensive innovative industries whereas positive performers operate in activities that cater to prevailing demand conditions and expand the business in a balanced manner. The firms that under- and over-perform as measured by the financial return ratio both constitute smaller firms compared to the total sample and show how relative movements in the ratio numerator and denominator affect the recorded return measure. However, the negative outliers generally use their public listing to access capital for investment in more risky development efforts that require a certain scale to succeed and thereby limits their flexibility. The positive outliers appear to expand their business activities in incremental responses to evolving market demands as a way to enhance maneuverability and secure competitive advantage by honing their unique firm-specific capabilities.

The aim of this study is to gain important insights on integration oriented servitization identifying essential dimensions of effective structures, coordination approaches and management controls adopted by manufacturing firms that integrate forward towards distribution, sales and services.

The study adopts a theory-guided qualitative abductive methodology to conduct a comparative case-study of two manufacturing firms in the same industry integrating forward to enhance servitization but with significantly different performance outcomes. The findings are uncovered from a broad spectrum of primary and secondary data spanning two decades.

The consistently high-performing firm puts equal emphasis on production and downstream distribution, sales and services and motivate individuals to engage in entrepreneurial efforts to develop combined product-services offerings that are valued by customers. The underperforming firm prioritizes operating efficiency driven by engineering prowess and managed through planning, standardization, authority and central controls.

The study is based on two representative firms operating in a specific industry context, which has ramifications for the generalizability of results and calls for replication studies to substantiate and extend findings.

Forward integration from manufacturing into distribution, sales and services represents a specific servitization strategy that needs structure and particular coordination approaches to be effective in complex dynamic product-markets. The characteristics of the outperforming case company provide useful insights on effective integrated servitization efforts.

Forward integration is a commonly adopted strategy among manufacturing firms that constitute the backbone of modern economies and effective governance of these integration oriented servitization efforts has important implications for societal value creation.

This study builds on rationales from management science including economic theory, corporate strategy and different micro-foundational lenses and thereby hone recent calls for broader theoretical foundations to enlighten studies of the servitization puzzle.

This chapter outlines how the comprehensive North American and European datasets were collected and explains the ensuing data cleaning process outlining three alternative methods applied to deal with missing values, the complete case, the multiple imputation (MI), and the K-nearest neighbor (KNN) methods. The complete case method is the conventional approach adopted in many mainstream management studies. We further discuss the implied assumption underlying use of this technique, which is rarely assessed, or tested in practice and explain the alternative imputation approaches in detail. Use of North American data is the norm but we also collected a European dataset, which is rarely done to enable subsequent comparative analysis between these geographical regions. We introduce the structure of firms organized within different industry classification schemes for use in the ensuing comparative analyses and provide base information on missing values in the original and cleaned datasets. The calculated performance indicators derived from the sampled data are defined and presented. We show how the three alternative approaches considered to deal with missing values have significantly different effects on the calculated performance measures in terms of extreme estimate ranges and mean performance values.

This chapter takes a closer look at outliers and extreme outliers identified in the data derived from a complete case treatment of missing values in the European and North American datasets and consistently observe significant negatively skewed distributions with high excess kurtosis across all industries. We then plot the density functions for return on assets (ROA) across different industries in the two datasets and find pervasive observations in the tails where negative returns and outlying observations constitute a frequent and recurring phenomenon. We analyze the persistency of outliers and find noticeable percentages of outlying over- and underperformers hovering around 3–6% dependent on industry context. We further analyze potential size effects associated with extreme negative skewness but do not find that (even sizeable) elimination of extreme values reduce the phenomenon. Finally, we analyze the percentage of firm observations that must be eliminated to reach at distributions that fulfill the characteristics of a normal distribution and reach at a substantial percentage of around 5–10% dependent on industry. To conclude, the often-assumed normally distributed performance outcomes are typically wrong and discards the substantial number of outliers in the samples.

This chapter first analyzes how the data-cleaning process affects the share of missing values in the extracted European and North American datasets. It then moves on to examine how three different approaches to treat the issue of missing values, Complete Case, Multiple Imputation Chained Equations (MICE), and K-Nearest Neighbor (KNN) imputations affect the number of firms and their average lifespan in the datasets compared to the original sample and assessed across different SIC industry divisions. This is extended to consider implied effects on the distribution of a key performance indicator, return on assets (ROA), calculating skewness and kurtosis measures for each of the treatment methods and across industry contexts. This consistently shows highly negatively skewed distributions with high positive excess kurtosis across all the industries where the KNN imputation treatment creates results with distribution characteristics that are closest to the original untreated data. We further analyze the persistency of the (extreme) left-skewed tails measured in terms of the share of outliers and extreme outliers, which shows consistent and rather high percentages of outliers around 15% of the full sample and extreme outliers around 7.5% indicating pervasive skewness in the data. Of the three alternative approaches to deal with missing values, the KNN imputation treatment is found to be the method that generates final datasets that most closely resemble the original data even though the Complete Case approach remains the norm in mainstream studies. One consequence of this is that most empirical studies are likely to underestimate the prevalence of extreme negative performance outcomes.