The origins of modern technological change provide the context necessary to understand present-day technological transformation, to investigate the impact of the new digital technologies, and to examine the phenomenon of digital disruption of established industries and occupations. How these contemporary technologies will transform industries and institutions, or serve to create new industries and institutions, will unfold in time. The implications of the relationships between these pervasive new forms of digital transformation and the accompanying new business models, business strategies, innovation, and capabilities are being worked through at global, national, corporate, and local levels. Whatever the technological future holds it will be defined by continual adaptation, perpetual innovation, and the search for new potential. Presently, the world is experiencing the impact of waves of innovation created by the rapid advance of digital networks, software, and information and communication technology systems that have transformed workplaces, cities, and whole economies. These digital technologies are converging and coalescing into intelligent technology systems that facilitate and structure our lives. Through creative destruction, digital technologies fundamentally challenge existing routines, capabilities, and structures by which organizations presently operate, adapt, and innovate. In turn, digital technologies stimulate a higher rate of both technological and business model innovation, moving from producer innovation toward more user-collaborative and open-collaborative innovation. However, as dominant global platform technologies emerge, some impending dilemmas associated with the concentration and monopolization of digital markets become salient. The extent of the contribution made by digital transformation to economic growth and environmental sustainability requires a critical appraisal.
The current discontent with the dominant macroeconomic theory paradigm, known as Dynamic Stochastic General Equilibrium (DSGE) models, calls for an appraisal of the methods and strategies employed in studying and modeling macroeconomic phenomena using aggregate time series data. The appraisal pertains to the effectiveness of these methods and strategies in accomplishing the primary objective of empirical modeling: to learn from data about phenomena of interest. The co-occurring developments in macroeconomics and econometrics since the 1930s provides the backdrop for the appraisal with the Keynes vs. Tinbergen controversy at center stage. The overall appraisal is that the DSGE paradigm gives rise to estimated structural models that are both statistically and substantively misspecified, yielding untrustworthy evidence that contribute very little, if anything, to real learning from data about macroeconomic phenomena. A primary contributor to the untrustworthiness of evidence is the traditional econometric perspective of viewing empirical modeling as curve-fitting (structural models), guided by impromptu error term assumptions, and evaluated on goodness-of-fit grounds. Regrettably, excellent fit is neither necessary nor sufficient for the reliability of inference and the trustworthiness of the ensuing evidence. Recommendations on how to improve the trustworthiness of empirical evidence revolve around a broader model-based (non-curve-fitting) modeling framework, that attributes cardinal roles to both theory and data without undermining the credibleness of either source of information. Two crucial distinctions hold the key to securing the trusworthiness of evidence. The first distinguishes between modeling (specification, misspeification testing, respecification, and inference), and the second between a substantive (structural) and a statistical model (the probabilistic assumptions imposed on the particular data). This enables one to establish statistical adequacy (the validity of these assumptions) before relating it to the structural model and posing questions of interest to the data. The greatest enemy of learning from data about macroeconomic phenomena is not the absence of an alternative and more coherent empirical modeling framework, but the illusion that foisting highly formal structural models on the data can give rise to such learning just because their construction and curve-fitting rely on seemingly sophisticated tools. Regrettably, applying sophisticated tools to a statistically and substantively misspecified DSGE model does nothing to restore the trustworthiness of the evidence stemming from it.