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What can machine learning do? Workforce implications

Digital computers have transformed work in almost every sector of the economy over the past several decades. We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a general purpose technology, like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities, there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be suitable for ML (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent end of work as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.

An Explanatory Study on the Co-evolutionary Mechanisms of Business IT Alignment

Business IT Alignment is considered an enduring topic in academic and practitioners’ literature. The interest in the subject is justified by the link, demonstrated by several studies, between alignment and corporate performances. However, alignment research has not yet been translated into practices, theoretically demonstrated in literature and applied to companies. The interpretation of alignment as a function of independent factors and the underestimation of the complex nature of alignment process are considered key barriers in alignment achievement. The present study is based on a multi-case study analysis carried out in two companies that implemented alignment processes. We conceptualise alignment as a co-evolution process and derive four mechanisms and three types of parameters and explain their role in alignment implementation. The contribution is theoretical, since we analyse and describe mechanisms and factors that govern alignment, and for the practitioners, since knowledge of these mechanisms is precondition for an effective alignment implementation.

Boundaries and Boundary Objects: An Evaluation Framework for Mixed Methods Research

While mixed methods research is increasingly established as a methodological approach, researchers still struggle with boundaries arising from commitments to different methods and paradigms, and from attention to social justice. Combining two lines of work—social learning theory and the Imagine Program at the University of Brighton—we present an evaluation framework that was used to integrate the perspectives of multiple stakeholders in the program’s social interventions. We explore how this value-creation framework acts as a boundary object across boundaries of practice, specifically across quantitative and qualitative methods, philosophical paradigms, and participant perspectives. We argue that the framework’s focus on cycles of value creation provided the Imagine Program with a shared language for negotiating interpretation and action across those boundaries.

Analytics, Innovativeness, and Innovation Performance

Based on organizational information processing theory, this paper develops and tests a research model to deepen the understanding about the conditions under which the use of data analytics contributes to innovation performance. This paper suggests that firm innovativeness, as an organization cultural concept, should moderate the relationship between data analytics use and innovation performance. The results of a moderation analysis based on data from cross-sectional survey support this account. The findings indicate a significant inversely U-shaped effect of innovativeness on the relationship between data analytics use and innovation performance. The effect of data analytics use on innovation performance is strongest under medium levels of innovativeness but comparatively weaker when firms have a low or a high level of innovativeness. These insights contribute to the IS literature by clarifying the important role of firm cultural factors in shaping information needs and deployment of information processing capabilities.

Financial governance: accounting for social learning in a regional network in Africa

The value created by learning in communities of practice or networks is not always easy to articulate in ways that make sense to participants, sponsors, and stakeholders. Yet it is something that needs to be done, not only for monitoring and evaluation, but also for optimizing the learning of the community. We have developed a “value-creation framework” that focuses on how social learning makes a difference in the world via its effect on practice. The framework helps structure convincing accounts of the value of social learning by framing learning in terms of different cycles of value creation and loops between them. It integrates quantitative and qualitative data and can be used by professional evaluators as well as participants. In this paper we demonstrate the use of the framework in a project supported by the World Bank in Southern and Eastern Africa where it was used both for evaluation and for strategic renewal of a regional network of members of parliament and their clerks.

A Synthesis of Enterprise Architecture Effectiveness Constructs

Companies throughout the world use Enterprise Architecture (EA) because of benefits such as the alignment of business to Information Technology (IT), centralisation of decision making and cost reductions due to standardisation of business processes and business systems. Even though EA offers organisational benefits, EA projects are reported as being costly, time consuming and require tremendous effort. Companies therefore seek to ascertain ways to measure the effectiveness of EA implementation because of the money and time being spent on EA projects. EA Effectiveness refers to the degree in which EA helps to achieve the collective goals of the organisation and its measurement depends on a list of constructs that can be used to measure the effectiveness of EA implementation. Currently, there exist no comprehensive list of constructs that are suitable to measure the effectiveness of EA implementation. The paper reports on the results of a study that explored the development of a compreh ensive list of constructs suitable for measuring the effectiveness of EA implementation. The artefact developed in this research study is called Enterprise Architecture Effectiveness Constructs (EAEC). The EAEC consists of 6 constructs namely: – alignment