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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.

Missing data are a pervasive problem in special education research. The purpose of this chapter is to provide researchers with an overview of two “modern” alternatives for handling missing data, full information maximum likelihood (FIML) and multiple imputation (MI). These techniques are currently considered to be the methodological “state of the art”, and generally provide more accurate parameter estimates than the traditional methods that are still common in published educational studies. The chapter begins with an overview of missing data theory, and provides brief descriptions of some traditional missing data techniques and their requisite assumptions. Detailed descriptions of FIML and MI are given, and the chapter concludes with an analytic example from a longitudinal study of depression.

The objective of this research is to examine, validate, and recommend techniques for handling the problem of missingness in observational data. We use a rich observational data set, the Nielsen HomeScan data set, which allows us to effectively combine elements from simulated data sets: large numbers of observations, large number of data sets and variables, allowing elements of “design” that typically come with simulated data, and its observational nature. We created random 20% and 50% uniform missingness in our data sets and employed several widely used methods of single imputation, such as mean, regression, and stochastic regression imputations, and multiple imputation methods to fill in the data gaps. We compared these methods by measuring the error of predicting the missing values and the parameter estimates from the subsequent regression analysis using the imputed values. We also compared coverage or the percentages of intervals that covered the true parameter in both cases. Based on our results, the method of single regression or conditional mean imputation provided the best predictions of the missing price values with 28.34 and 28.59 mean absolute percent errors in 20% and 50% missingness settings, respectively. The imputation from conditional distribution method had the best rate of coverage. The parameter estimates based on data sets imputed by conditional mean method were consistently unbiased and had the smallest standard deviations. The multiple imputation methods had the best coverage of both the parameter estimates and predictions of the dependent variable.

Companies in all industries are searching for new sources of competitive advantage since the competition in their marketplace is becoming increasingly intensive. The resource-based view of the firm explains the sources of sustainable competitive advantages. From a resource-based view perspective, relational based assets (i.e., the assets resulting from firm contacts in the marketplace) enable competitive advantage. The relational based assets examined in this work are brand image and corporate reputation, as components of brand equity, and customer value. This paper explores how they create value. Despite the relatively large amount of literature describing the benefits of firms in having strong brand equity and delivering customer value, no research validated the linkage of brand equity components, brand image, and corporate reputation, simultaneously in the customer value–customer loyalty chain. This work presents a model of testing these relationships in consumer goods, in a business-to-business context. The results demonstrate the differential roles of brand image and corporate reputation on perceived quality, customer value, and customer loyalty. Brand image influences the perception of quality of the products and the additional services, whereas corporate reputation actions beyond brand image, estimating the customer value and customer loyalty. The effects of corporate reputation are also validated on different samples. The results demonstrate the importance of managing brand equity facets, brand image, and corporate reputation since their differential impacts on perceived quality, customer value, and customer loyalty. The results also demonstrate that companies should not limit to invest only in brand image. Maintaining and enhancing corporate reputation can have a stronger impact on customer value and customer loyalty, and can create differential competitive advantage.

We use Cross-National Equivalent File (CNEF) data from the United States and Great Britain to investigate the association between adults’ health and the income inequality they experienced as children up to 80 years earlier. Our inequality data track shares of national income held by top income percentiles from the early 20th century. We average those data over the same early-life years and merge them to CNEF data from both countries that measure self-reported health of individuals between 1991 and 2007. Observationally, adult men and women in the United States and Great Britain less often report being in better health if inequality was higher in their first five years of life. Although the trend in inequality is similar in both countries over the past century, the empirical association between health and inequality in the United States differs substantially from the estimated relationship in Great Britain. When we control for demographic characteristics, measures of permanent income, and early-life socio-economic status, the health–inequality association remains robust only in the U.S. sample. For the British sample, the added controls drive the coefficient on inequality toward zero and statistical insignificance.

This paper focuses on the role of manufacturer brands for resellers within retail channels. This topic is important because of the strategic value of manufacturer brands and the increasing influence of resellers within channels of distribution. Much of the branding research emphasizes a customer-brand knowledge perspective; however, emerging perspectives suggest that brands are also relevant to other stakeholders including resellers. In contrast, channels research recognizes the manufacturer sources of market power, but does not consider the impact of manufacturer “push and pull” strategies within channels. Existing theoretical frameworks, therefore, do not address the reseller perspective of the brand. As a result, the research approach is a multi-method design, consisting of two phases. The first phase involves in-depth interviews, allowing the development of a conceptual framework. In the second phase, a survey of supermarket buyers on brands in several product categories tests this framework. Structural equation modeling analyzes the survey responses and tests the hypotheses. The structural model shows very good fit to the data with good construct validity, reliability, and stability. The findings show that manufacturer support, brand equity, and customer demand reflect the manufacturer brand benefits to resellers. A key contribution of this research is the development of a validated scale on manufacturer brand benefits from the point of view of a reseller. This research shows that the resources that relate to the brand, not just the brand name itself, create value for resellers in channel relationships.

All firms operating in the global economy are exposed to a multitude of risks including financial crisis, cyberattack, social instability, governance failure, extreme weather events, etc. As a consequence, international organizations assume many (new and evolving) exposures that must be addressed, where some firms are able to adjust and thrive against these adverse odds, whereas many others fail. It appears like some (a few) firms are able to repeatedly outperform the market, where a great many of them struggle, and quite a few register negative returns every year. As a consequence, the authors typically observe leptokurtic negatively skewed distributions of financial returns with extreme negative tails of poor performing firms, where the performance data fall way beyond the requirements of a normal distribution. The authors investigate this phenomenon based on a comprehensive dataset of European firms retrieved from Compustat Global for the 25-year period 1995–2019. The analysis shows that there is indeed a consistent pattern of many underperforming firms across different industry classifications and time intervals and a few outperformers. This provides evidence of a regularly observed phenomenon that often is overlooked in mainstream management studies. The results have implications for academic research that often relies on assumptions of data normality in statistical analysis and for corporate management that has to deal with a risk-prone business environment.