Organizing for Service Innovation: Best-Practice or Configurations?

In this paper we contrast the notions of best-practice and configurations contingent on environmental conditions. The analysis draws upon our study of 38 UK and 70 US service firms which includes an assessment of the organization, processes, tools and systems used, and how these factors influence variation in the development and delivery of new services. The best-practice framework is found to be predictive of performance improvement in samples in both the UK and USA, but the model better fits the USA than UK data. We analyze the UK data to identify alternative configurations. Four system configurations are identified: project-based; mass customization; cellular; and organic-technical. Each has a different combination of organization, processes, tools and systems which offer different performance advantages. The results provide an opportunity for updating the typologies of operations and adapting them to include services, and begin to challenge the notion of any universal 'best practice' management or organization of new product or service development.service industry, performance improvement, best-practice, alternative system configurations

A Guide to Measuring Advocacy and Policy

The overall purpose of this guide is twofold. To help grantmakers think about and talk about measurement of advocacy and policy, this guide puts forth a framework for naming outcomes associated with advocacy and policy work as well as directions for evaluation design. The framework is intended to provide a common way to identify and talk about outcomes, providing philanthropic and non-profit audiences an opportunity to react to, refine and adopt the outcome categories presented. In addition, grantmakers can consider some key directions for evaluation design that include a broad range of methodologies, intensities, audiences, timeframes and purposes. Included in the guide are a tool to measure improved policies, a tool to measure a strengthened base of public support, and a survey to measure community members' perceptions about the prioritization of issues

The Intuitive Appeal of Explainable Machines

Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties. Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the General Data Protection Regulation (GDPR), as well as techniques within machine learning, are focused almost entirely on the problem of inscrutability. While such techniques could allow a machine learning system to comply with existing law, doing so may not help if the goal is to assess whether the basis for decision-making is normatively defensible. In most cases, intuition serves as the unacknowledged bridge between a descriptive account and a normative evaluation. But because machine learning is often valued for its ability to uncover statistical relationships that defy intuition, relying on intuition is not a satisfying approach. This Article thus argues for other mechanisms for normative evaluation. To know why the rules are what they are, one must seek explanations of the process behind a model’s development, not just explanations of the model itself

E-Learning for Teachers and Trainers : Innovative Practices, Skills and Competences

Reproduction is authorised provided the source is acknowledged.Final Published versio

Human Resource Information Systems for Competitive Advantage: Interviews with Ten Leaders

[Excerpt] Increasingly, today\u27s organizations use computer technology to manage human resources (HR). Surveys confirm this trend (Richards-Carpenter, 1989; Grossman and Magnus, 1988; Human Resource Systems Professionals 1988; KPMGPeat Marwick, 1988). HR professionals and managers routinely have Personnel Computers (PCs) or computer terminals on their desks or in their departments. HR computer applications, once confined to payroll and benefit domains, now encompass incentive compensation, staffing, succession planning, and training. Five years ago, we had but a handful of PC-based software applications for HR management. Today, we find a burgeoning market of products spanning a broad spectrum of price, sophistication, and quality (Personnel Journal, 1990). Top universities now consider computer literacy a basic requirement for students of HR, and many consulting firms and universities offer classes designed to help seasoned HR professionals use computers in their work (Boudreau, 1990). Changes in computer technology offer expanding potential for HR management (Business Week, 1990; Laudon and Laudon, 1988)

Knowledge Transfer Needs and Methods

INE/AUTC 12.3