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White‐collar productivity measurement can be improved, according to results from group sessions conducted with 39 executives, managers and academics which elicited a list of eleven useful areas for measurement. There are three types of ways in which the measurements can be used: self‐improvement; performance appraisal, salary and promotion; and feedback, communication and work direction. Highly interactive jobs should be measured at group level, with individual performance judged on the basis of group results. Peer group ratings can also measure white collar productivity, with existing MBO systems providing information; and time management techniques are also appropriate.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

The aim of this paper is to find out how the productivity of public services can be measured in the operative level of organisations. In particular, the role of different output elements (tangible and intangible) is examined.

A qualitative research approach is used. The current knowledge of the issue is presented by examining literature on public service productivity, intellectual capital and performance measurement. In the empirical part, the findings of the literature review are applied and further examined in the context of two services of the City of Helsinki, Finland.

The paper identifies tangible and intangible output factors in two case services. It also illustrates how the factors can be measured in practice. The empirical examination suggests that the challenges in the operative level productivity measurement relate especially to defining measures. Identifying of different output factors is an easier task.

The study presents and applies a novel approach of designing productivity measures for public services. More empirical studies using the approach are called for.

The measurement approach presented here can be utilised as a basis for designing sophisticated productivity measures of public services.

A key challenge in examining public service productivity relates to the intangible nature of services. Despite the great potential and practical relevance of the topic there seems to be no understanding of how to capture this feature in order to design valid productivity measures. The paper adds to the current knowledge by describing the process of designing measures for disaggregated components of productivity, shares practical experiences related to the design process and highlights the main challenges.

The structure of the economy continues to change; where once they are dependent on the productivity of a manual workforce, companies increasingly depend on the productivity of knowledge workers. Today, knowledge workers account for more than two‐thirds of the workforce, and thus should be the focus of strategic plans to improve productivity. Currently there are no universally accepted methods to measure knowledge worker productivity, or even generally accepted categories. This paper provides a taxonomy of knowledge worker productivity measurements, and identifies a number of productivity dimensions that are used to categorize the findings of previous research. Also describes the relative density of discussions along these dimensions and identifies critical areas for future research.

The purpose of this paper is to measure service productivity using the Service Enterprise Productivity in Action (SEPIA) model. The research operationalises only one of the five stakeholder groups, the customer interface which incorporates service complexity (SC), customer interactions, customer channel, customer loyalty (CL) (new) as inputs, and CL (referred and repeat) and willingness to pay as output measures.

The research extends our understanding of existing service productivity models with the development of the SEPIA model. Data were collected from 14 organisations operating in the Australian travel and tourism industry, which was analysed using a data envelopment analysis input oriented variable return to scale method as applied to the SEPIA model customer interface.

Four key findings from the research include: customer choice and their ability to pay is a determinant of service productivity; service productivity is a two stage process when measured; SC is not categorical; and quality business systems do impact service productivity.

A limitation of this research is that only one (customer) of the five key stakeholders, customer, employee, manager, supplier and shareholder, was operationalised in this research paper.

The operationalisation of the SEPIA customer interface using transactional data and measuring non-financial, intangible factors of productivity provide managers with insights on what services to offer, when to invest in or promote the use of technology and whether to spend marketing effort on customer acquisition or customer retention.

The SEPIA model positions service firms within a social and service value network and provides a range of customer measures that extend the current capital (K), labour (L), energy (E), materials (M) and service (S), KLEMS measure of productivity and can be used to show the impact customers have on service productivity.

Both the improvement of productivity and the effectiveness of performance measures have been topics of extreme interest to managers and researchers in recent years. Looks at the potential advantages and disadvantages of using the traditional productivity measure, output/input, as a measure of performance at the firm, work‐unit, individual, product, or product‐line level. Considers the ability of productivity measures to adjust for price changes and to detect factor substitutions. The distortion of productivity measures by fixed expenses and the inability of productivity measures to consider quality changes are among the disadvantage cited. Also, criticisms that have been levelled at traditional cost accounting measures are found to apply to productivity measures. Proposes three criteria that can aid in determining when productivity measures are appropriate as performance measures. Makes recommendations for ways of using productivity measures effectively also.

The introduction of efficiency improvements and enterprise bargaining into local authorities has been severely hampered by a failure to agree on a method of measuring service productivity. This paper develops an outcomes measure of productivity which, it argues, meets both externally imposed cost efficiency requirements and clients’ needs for service effectiveness in terms of quality and equity in delivery. Applications of this measure to library and statutory planning services are provided.

Existing measures of productivity were designed to measure productivity in industries in which both input and output are tangible standardised quantities. They are inadequate for productivity measurement of professional services, where intangible and specialised factors of production are in use. This paper seeks to address the difficulties associated with the measurement of productivity of professional service firms and to propose a more adequate measure of productivity in these industries. This measure is tested on a sample of Swedish management consulting firms, and is assessed in relation to several performance indicators of these firms. The findings illustrate the inadequacy of the manufacturing‐based measurement procedures and demonstrate that a measure which acknowledges the unique characteristics of professional services correlates better with firms’ performance. As this field of research is in its infancy, these findings are only suggested as indications for direction in which future research is needed.