Business intelligence and analytics value creation in Industry 4.0: a multiple case study in manufacturing medium enterprises

Abstract

Medium enterprises (MEs) form a majority of enterprises in several regions, yet they are underrepresented in the literature. The digital transformation known as Industry 4.0 (I4.0) brings new data streams into enterprises. MEs and large enterprises will both leverage business intelligence and analytics (BI&A) to improve their business performance, so the study of the conditions favouring value creation of BI&A is of interest. However, there are few studies focusing on the impact of business intelligence and analytics (BI&A) on value creation in I4.0 and none focusing on MEs. Using a multiple case-study design, this paper explores factors linked to BI&A business value creation in manufacturing MEs that are undergoing an I4.0 transformation. Findings suggest enterprises resources and capabilities are not sufficient to predict business value: organizational learning and organizational culture have a non-negligible influence for MEs.     

Creating a sense of mission

Mission is still a relatively neglected area of management, and there is no clear agreement on what it encompasses. The Ashridge Strategic Management Centre conducted a 2-year research project designed to fill this gap. The research found that if mission is more clearly defined it can be managed better, and developed a model of mission that includes four elements--purpose, strategy, behaviour standards and values. The project identified companies where, in addition to strong links between these elements, employees also showed an emotional commitment to their company which Campbell has called a 'sense of mission'. This commitment was deepest when there was a match between the employee's values and the company's values.     

Biogeographical Analysis of Chemical Co‐Occurrence Data to Identify Priorities for Mixtures Research

A challenge with multiple chemical risk assessment is the need to consider the joint behavior of chemicals in mixtures. To address this need, pharmacologists and toxicologists have developed methods over the years to evaluate and test chemical interaction. In practice, however, testing of chemical interaction more often comprises ad hoc binary combinations and rarely examines higher order combinations. One explanation for this practice is the belief that there are simply too many possible combinations of chemicals to consider. Indeed, under stochastic conditions the possible number of chemical combinations scales geometrically as the pool of chemicals increases. However, the occurrence of chemicals in the environment is determined by factors, economic in part, which favor some chemicals over others. We investigate methods from the field of biogeography, originally developed to study avian species co‐occurrence patterns, and adapt these approaches to examine chemical co‐occurrence. These methods were applied to a national survey of pesticide residues in 168 child care centers from across the country. Our findings show that pesticide co‐occurrence in the child care center was not random but highly structured, leading to the co‐occurrence of specific pesticide combinations. Thus, ecological studies of species co‐occurrence parallel the issue of chemical co‐occurrence at specific locations. Both are driven by processes that introduce structure in the pattern of co‐occurrence. We conclude that the biogeographical tools used to determine when this structure occurs in ecological studies are relevant to evaluations of pesticide mixtures for exposure and risk assessment.     

Identifying Childhood Age Groups for Exposure Assessments and Monitoring

The purpose of this article is to describe a standard set of age groups for exposure assessors to consider when assessing childhood exposure and potential dose to environmental contaminants. In addition, this article presents examples to show how the age groups can be applied in children's exposure assessments. A consistent set of childhood age groups, supported by an underlying scientific rationale, will improve the accuracy and comparability of exposure and risk assessments for children. The effort was undertaken in part to aid the U.S. Environmental Protection Agency (EPA) in implementing such regulatory initiatives as the 1997 Presidential Executive Order 13,045, which required all federal agencies to ensure that their standards take into account special risks to children. The standard age groups include: birth to <1 month; 1 to <3 months; 3 to <6 months; 6 to <12 months; 1 to <2 years; 2 to <3 years; 3 to <6 years; 6 to <11 years; 11 to <16 years; and 16 to <21 years. These age groups reflect a consideration of developmental changes in various behavioral, anatomical, and physiological characteristics that impact exposure and potential dose. It is expected that the availability of a standard set of early-life age groups will inform future analyses of exposure factors data as well as guide new research and data collection efforts to fill knowledge gaps.     

Policy learning and cross-national diffusion in social long-term care insurance: Germany, Japan, and the Republic of Korea

Recently, many developed countries have moved toward comprehensive Long-Term Care (LTC) systems. Along with the conventional explanations of these policy changes based on domestic factors, learning from abroad should be considered. We focus on the social insurance model of LTC, particularly how Japan learned from Germany and the Republic of Korea learned from Japan. Some approaches for how to think about policy learning in general as well as cross-border learning are suggested.