Factor analysis, first developed over a century ago (Spearman 1904, 1927), attempts to explain the covariation and variance of a set of observed variables using a more parsimonious number of latent variables. Many observed variables in the social sciences, such as responses to items, are correlated. The correlation that these variables share can be explained using a theoretical latent variable, sometimes referred to as a factor. For example, suppose you develop a test comprised of constructed-response items purported to measure algebra skills. After you administer the test to a large sample of eighth graders, you notice that many of the items are correlated, indicating that students who have better algebra skills tended to score higher on each item compared to those who have lesser algebra skills. This correlation among the items is an indication that the items may be associated with a common latent variable represented by math proficiency in algebra. In other words, the observed variables, referred to as indicators, are correlated because they are influenced by a common factor, which is unobservable (such as math proficiency, anxiety). Factor analysis was developed to capture this common relationship shared among a set of observed variables.

Confirmatory factor analysis (CFA) provides a convenient framework for evaluating measurement invariance. In CFA we specify a measurement model that defines the factorial structure underlying the observed data (e.g., item responses). The factorial structure includes the number of latent variables (i.e., factors) and the pattern of factor loadings (see Chapter 5 for a more detailed description of CFA). If the CFA model adequately captures the factorial structure, then group membership will not provide any additional information about observed variables above and beyond that explained by the latent variable. In fact, DIF has been framed as a dimensionality issue (Ackerman, 1992) and CFA makes the role of dimensionality in the analyses more explicit.

This chapter begins by defining ideology in a descriptive and amoral fashion. In doing so, it shows that Greimas’s semiotic square provides a useful means by which to conceive, and delineate, the ideology of infinite times from three alternatives that I respectively term the future primitive ideology, the ideology of extinction and the singularity ideology. In line with this framing, the chapter then completes two main tasks. First, it provides a recapitulation of the infinite times ideology; of the role it plays in bringing Alphabet’s diverse activities together; and of the role it plays in aligning the megacorporation’s interests with those of its users worldwide. Second, it explains how Alphabet is directly and indirectly encouraging developments that are consistent with the future primitive, extinction and singularity ideologies that are all, whatever else their differences, similarly opposed to the ideology of infinite times. Given these discussions, the chapter concludes by emphasizing that, as the ideology of infinite times is as finite as any other ideology, it provides one potential source of Alphabet’s ultimate demise.

The introduction identifies the book’s two main contributions – i.e. the explication of the megacorporate concept and of the infinite times ideology – and situates the work with reference to current discussions of Big Tech. In doing so, it is first emphasized that whereas current discussions of Big Tech often adopt a critical, and even a moralizing, tone, the present work strives to comply with an ideal of amoral analysis. The following sections then detail two supplementary contributions that the book makes to the scholarly fields of business and society and organization studies. The first of these domain-specific contributions relates to the book advancing a philosophical perspective, and the second to its demonstrating that corporations can shape social considerations of much broader importance than is commonly recognized. After this, the book’s very simple method of construction, and its three-part structure, are described. The chapter concludes with a brief summary.

Along with the masses of information that Alphabet is collecting on specific individuals, it is currently digitizing existing, and creating entirely new, data sets of broad social relevance. As a result, and as the first section of this chapter outlines, Alphabet is contributing to a world in which the problem is not too little information, but too much. The chapter then proposes that those that want to use this information to make sense of, or construct, our social pasts, can employ one of two approaches – termed the massive and mélange approach to historical analysis respectively – to ensure that they are not overwhelmed by our ever-growing archives. Having done so, the chapter concludes by emphasizing that, by creating and maintaining this ‘great library’, Alphabet is already in a strong position from which to decide – like the ongoing winner of some never-ending war – who can, and cannot, write history.

The desire to maintain the sustainable development of humanity is widespread. In the present chapter, it is proposed that Alphabet’s capacity to shape this concern far outstrips that of most other organizations combined. Nevertheless, the megacorporation’s potential to sustain humanity’s development is not universally regarded as a net positive. In recognizing thus, the chapter posits that Alphabet’s current impact on our social futures should be conceived as simultaneously having a more authoritarian, and a more autonomous, element to it. Whilst the exact nature of Alphabet’s impact on our social futures remains to be seen, the chapter’s concluding summary emphasizes – in anticipation of the discussions that begin the book’s third and final part – that the megacorporation’s interest in sustaining our future existence is not just consistent with, but positively enabled by, the custodial role it plays with regards to our personal and social pasts.