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Measures are important to healthcare outcomes. Outcome changes result from deliberate selective intervention introduction on a measure. If measures can be characterized and categorized, then the resulting schema may be generalized and utilized as a framework for uniquely identifying, packaging and comparing different interventions and probing target systems to facilitate selecting the most appropriate intervention for maximum desired outcomes. Measure characterization was accomplished with multi-axial statistical analysis and measure categorization by logical tabulation. The measure of interest is a key provider productivity index: “patient visits per hour,” while the specific intervention is “patient schedule manipulation by overbooking.” The paper aims to discuss these issues.

For statistical analysis, interrupted time series (ITS), robust-ITS and outlier detection models were applied to an 18-month data set that included patient visits per hour and intervention introduction time. A statistically significant change-point was determined, resulting in pre-intervention, transitional and post-effect segmentation. Linear regression modeling was used to analyze pre-intervention and post-effect mean change while a triangle was used to analyze the transitional state. For categorization, an “intervention moments” table was constructed from the analysis results with: time-to-effect, pre- and post-mean change magnitude and velocity; pre- and post-correlation and variance; and effect decay/doubling time. The table included transitional parameters such as transition velocity and transition footprint visualization represented as a triangle.

The intervention produced a significant change. The pre-intervention and post-effect means for patient visits per hour were statistically different (0.38, p=0.0001). The pre- and post-variance change (0.23, p=0.01) was statistically significant (variance was higher post-intervention, which was undesirable). Post-intervention correlation was higher (desirable). Decay time for the effect was calculated as 11 months post-effect. Time-to-effect was four months; mean change velocity was +0.094 visits per h/month. A transition triangular footprint was produced, yielding 0.35 visits per hr/month transition velocity. Using these results, the intervention was fully profiled and thereby categorized as an intervention moments table.

One limitation is sample size for this time series, 18 monthly cycles’ analysis. However, interventions on measures in healthcare demand short time cycles (hence necessarily yielding fewer data points) for practicality, meaningfulness and usefulness. Despite this shortcoming, the statistical processes applied such as outliers detection, t-test for mean difference, F-test for variances and modeling, all consider the small sample sizes. Seasonality, which usually affects time series, was not detected and even if present, was also considered by modeling.

Obtaining an intervention profile, made possible by multidimensional analysis, allows interventions to be uniquely classified and categorized, enabling informed, comparative and appropriate selective deployment against health measures, thus potentially contributing to outcomes optimization.

The inevitable direction for healthcare is heavy investment in measures outcomes optimization to improve: patient experience; population health; and reduce costs. Interventions are the tools that change outcomes. Creative modeling and applying novel methods for intervention analysis are necessary if healthcare is to achieve this goal. Analytical methods should categorize and rank interventions; probe the measures to improve future selection and adoption; reveal the organic systems’ strengths and shortcomings implementing the interventions for fine-tuning for better performance.

An “intervention moments table” is proposed, created from a multi-axial statistical intervention analysis for organizing, classifying and categorizing interventions. The analysis-set was expanded with additional parameters such as time-to-effect, mean change velocity and effect decay time/doubling time, including transition zone analysis, which produced a unique transitional footprint; and transition velocity. The “intervention moments” should facilitate intervention cross-comparisons, intervention selection and optimal intervention deployment for best outcomes optimization.

Technical knowledge is a key factor in firm innovation. This study aims to construct a theoretical framework of technological boundary-spanning search, exploratory innovation and exploitative innovation to help firms adjust their search strategies and improve the effect of external resources on internal innovation.

The study uses questionnaires to collect data and conducts empirical analysis using SPSS25 and AMOS24.

Technological boundary-spanning search is positively correlated with ambidextrous innovation. Additionally, knowledge base positively moderates the effect of technological boundary-spanning search on ambidextrous innovation and knowledge distance negatively moderates the effect of technological boundary-spanning search on ambidextrous innovation. When a firm’s knowledge base is robust, its ambidextrous innovation can benefit more from technological boundary-spanning search. Additionally, when the knowledge distance is less, a firm’s ambidextrous innovation can benefit more from technological boundary-spanning search.

Considering organizational ambidexterity, this study divides firm innovation into exploratory innovation and exploitative innovation and presents a theoretical framework for the effect of technological boundary-spanning search on ambidextrous innovation. Additionally, it provides a comprehensive understanding of the crucial roles of knowledge base and knowledge distance in the relationship between technological boundary-spanning search and exploratory and exploitative innovation.

The purpose of this paper is to bring together an overview of the basic definitions and functions that indicators and indices have in sustainability and environmental debates. Indicators and indices are widely and increasingly used within environmental and sustainability debates; they provide “evidence” to demonstrate policy effects; they are used for communication of state and condition and to benchmark performance. However, the statistical basis of many indicators and indices is not well defined; so in one sense, they are simple arithmetic rather than inferential tools. This special issue opens up further debate around the creation and utility of indicators and indices and discusses some of the research challenges, including a sound statistical and inferential framework for indicator development.

This short paper brings together an overview of the basic definitions and functions that indicators and indices have in sustainability and environmental debates.

The paper summarises very broadly the rationale for and construction of indicators and indices. It also highlights areas where further work is required to ensure that the indicators are not simply arithmetic summaries but are generalisable.

This paper and the papers of this issue seek to enhance the debate concerning the development of reliable and robust indicators.

To develop a model to assist in the analysis of enterprise resource planning (ERP) systems based upon a validated approach to aligning information systems with enterprise strategies.

This paper presents a proposed analytic network process (ANP) model built on the strategic alignment model (SAM) to support the alignment of ERP investment decisions to strategies. The model developed is generic enough to be applied to a wide range of enterprises and applications while being based on the well‐documented SAM. A small illustrative example is provided to show the use and applicability of the model.

The SAM is found to provide a useful framework for investment analysis. ANP provides a conceptually easy to use but decisionally robust model which can be used to operationalize the SAM for investment analysis.

The model provides practitioners a flexible tool for making investment decisions not only for ERP systems but also for other IS investments. The paper provides academics and researchers with a methodology for the important area of IS alignment.

The paper represents a novel use of the multi‐attribute decision‐making method of ANP. It is also a new applied tool within the area of strategic IS evaluation.

The purpose of this study is primarily conceptual, presenting the notion of a polyphonic model as a tool for analyzing the interactions of diverse stakeholder voices in generating corporate social responsibility (CSR) narratives and practices.

The theoretical basis for this model is Bakhtin’s notions of voice, polyphony, dialogic relations and placement within dialogue.

To demonstrate possible applications of the model, this study considers CSR narratives that are emerging from some countries of the Arabian Gulf, given that this region’s historical trajectory and socio-political context differ substantially from those of more widely studied geopolitical contexts. This application reveals valuable use of the model.

The contribution of this paper is its pioneering development of a polyphonic model that serves to reveal the diverse voices that shape CSR narratives. This provides a tool particularly useful for investigating the creation of CSR narratives in less researched organizational and national contexts.

This paper considers threats to the internal validity of field studies that utilize survey data. Compared to laboratory experiments and field experiments, field surveys should be strong in realism, practical significance, and normative quality. However, there are substantial threats to internal validity that fall into the general categories of sampling and measurement. We consider these issues and how to deal with them. We pay special attention to the existence and impact of common method variance including strategies for avoiding it, methods for assessing it, and approaches to correcting for it. Our objective is to provide a road map for better use of survey methods.

Social evaluation functions used in policy impact analysis can be viewed as real-valued functionals of the underlying outcome distributions. Influence functions may be used to identify the sources of variation in social outcomes in terms of individual or household characteristics. This chapter sets forth in clear terms the definition of the influence function and recentered influence function, and catalogs these functions for a wide range of distributional statistics, including measures of central tendency, inequality, and poverty and also measures of the degree of pro-poorness of a shock- or policy-induced change in income levels.

Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact on the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference.