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This chapter examines the concepts of race and racism, critically reviewing their historical and contemporary applications in everyday life as well as in academic and policy debates. Racism has been extensively researched, with various theories and conceptualisations developed across social science. However, there is a great deal of disagreement regarding its nature, contemporary significance and empirical validation. This chapter examines these and attempts to synthesise some of the common definitions of racism provided in the literature. It explores related concepts and underlying themes pertaining to expressions of race and racism. Furthermore, it unpacks current knowledge about racial issues and discusses recent advances in the conceptual understanding of various forms of racism. It also elucidates the social, political and analytical applications of racism as a concept and the significance of racism in contemporary societies. The chapter concludes by highlighting how racism is a dynamic phenomenon, continuously evolving with the social, political and technological transformations in contemporary societies.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

Prior research suggests a chief executive officer’s (CEO) background is highly predictive of the strategic predisposition. This paper aims to focus on the need for accuracy in the categorization of CEO background and the impact that modest, nuanced changes in coding definitions yield.

This study evaluates the use of biographic and demographic information of CEOs to provide a more nuanced and expansive approach to understanding the influence of legal education and experience on business strategy. Propositions as to more nuanced coding definitions are developed. Building upon Fligstein (1987), a proof-of-concept example is developed using CEO information available for 2010. That data is then reexamined using an altered method (Modified Fligstein) to discern changes in the number of CEOs contained within the background categories.

The two categorizations performed reveal that substantial differences in the number of CEOs coded into a category can come from relatively small changes in categorical definitions. In comparing the first categorization to the second, each of the vocational categories experienced a change, ranging from a decrease of 11.1% to an increase of 142.9%.

This study informs both theory and practice by increasing the efficacy of the use of biographic and demographic information to assess the strategic orientation of executives. It postulates and demonstrates that simple changes in the categorical definition produce significant changes and can skew empirical results that reduce the utility of prior studies.

Sustainability indicators that allow integrated farm assessments have received increasing attention. However, limited evidence is available for the use of the same when assessing the agricultural sustainability of farming systems, especially in regions practising incessant monoculture. Therefore, this study aims to develop a holistic index to assess alternative farming systems based on a stock-and-flow framework.

A composite metric was developed by aggregating the economic, social and environmental indicators. The methodology involved estimation, normalisation, hierarchical weighting and progressive aggregation of indicators to form the Comprehensive Farm Assessment Index (CFAI). The CFAI was applied to assess the farming practices of 88 organic and 90 conventional farming plots across three agro-climatic zones over two cropping seasons in selected districts of Punjab, India.

Results showed statistical mean differences between the organic and conventional farming systems in terms of key production costs and income for wheat, rice and cotton crops. The normalised values of the selected social indicators were higher for the organic farming system. Similarly, in the environmental dimension, more biodiversity and less water contamination were found in organic farms. Except for paddy cultivation in the North–East region, the CFAI for organic farming is higher than that for conventional farming, even under the mono-cropping system with a single-crop rotation.

The CFAI has ubiquitous applications and can be used to assess alternate sustainable approaches and practices across crops and regions. It provides a perspective on the social viability and ecological sustainability of agriculture, which would enable contextual and effective policy analysis and implementation.