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Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

Although many studies explored the drivers of innovative development and the innovation performance of different countries, very few studies looked at the association of the country’s GII score with the qualitative indicators of innovation performance. The purpose of this paper is to contribute such an investigation by looking at the Republic of Kazakhstan (79th in 2019 GII ranking).

This study looks at eight dynamic variables, among which one dependent (the GII score) and seven independent (R&D spending, innovation grants, the total cost of innovative goods and services, the percentage of innovative organizations, the share of innovative goods and services in gross domestic product (GDP) and the number of R&D staff and R&D institutions) variables associated with innovation performance. Changes in variables were tracked over the period from 2010 to 2018..

The study found that the Kazakhstan’s GII score was reliant on variables, such as the percentage of innovative organizations, the value of innovative goods and services as a share of GDP, R&D spending and the cost of innovative goods and services. At the same time, the number of R&D institutions, innovation grants and number of R&D staff had no substantial impact on the GII score of Kazakhstan.

Using the proposed approach, this study proved that factors, which have no direct association with the country’s level of innovative development expressed in GII, could have a significant synergistic impact on this indicator.

Many cities across the world are actively investing in ways to excel in the innovation economy through the development of innovation districts as one of the most popular policy options. While innovation districts are among the leading drivers of innovation activities in cities, they are also high-cost and high-risk investments. Besides, holistic approaches for assessing these districts’ multifaceted performances are scarce. Bridging this knowledge gap is critical, hence, this paper aims to explore how innovation district performance can be assessed through a classification framework.

The paper introduces a multidimensional innovation district classification framework and applies it into Australian innovation districts with divergent features, functions, spatial and contextual characteristics. The study places 30 innovation districts from South East Queensland under the microscope of the framework to assess the multifaceted nature of innovation district performance. It uses qualitative analysis method to analyse both the primary and secondary data, and descriptive analysis with basic excel spreadsheet calculations to analyse the validity of the data.

The data analysis clusters 30 innovation districts from South East Queensland under three performance levels – i.e. desired, acceptable and unsavoury – concerning their form, feature and function characteristics.

The results disclose that the framework is a practical tool for informing planners, developers and managers on innovation district performances, and it has the capability to provide guidance for policymakers on their policy and investment decisions regarding the most suitable innovation district types and characteristics to consider.

This study focuses on the impact of green innovation on oil and gas firms’ performance in the MENA region from 2010 to 2020. Return on assets (ROA) was used to measure the financial performance of firms. However, green innovation was measured using two different scores, namely the environmental pillar and the innovation scores. Additionally, we introduced an oil price-moderated variable to examine its effect on the firm’s performance and the green innovation nexus. We collected data from the DataStream database. Regarding our empirical part, we use the generalized least squares method to carry out the analysis. Results showed a positive impact between green innovation scores and the firm’s performance in the MENA region. Also, we found that green innovation has a linear effect on firm performance. Finally, a negative, moderated effect of crude oil prices on green innovation and the firm’s financial performance nexus has been found.

The purpose of this paper is to explore whether innovation talent is predictive of business results. This question is important because companies exist to generate business results such as profitability and market expansion. To study this question, the authors conducted four phases of international research. They found that innovation talent is statistically predictive of business results. The Innovation Profiler (“the instrument”) is a web-based assessment tool based on the research. It was designed to detect the full array of specific innovation skills in individuals, skills that correlate with real-world business results.

The research presented in this paper follows four phases: a qualitative phase followed by two correlational studies; and finally, a validation research phase. The researchers wanted to answer the questions: “Is innovation talent predictive of business results?” “Which dimensions of innovation talent are most predictive of business results?” The research compares the attitudes, value and beliefs of innovators (both entrepreneurs and intrapreneurs) to the business results they achieved and compares innovators to the general population.

The research findings are that: innovation talent is highly correlated with positive business results. Innovators have significantly higher Innovation Profiler scores than the general population. Within the population of innovators, top scorers are associated with a larger number of positive business results than bottom scorers. Intrapreneurs, while sharing many characteristics with entrepreneurs, tend to score higher on innovation skills. The Innovation Profiler does not produce adverse selection bias with respect to gender or ethnicity.

Most psychographic instruments are normative, including the Innovation Profiler; they rely on scaled responses that measure the extent to which individuals consider statements to apply to them personally. Normative instruments are faked more easily than ipsative (forced choice) measures, which ask people to choose from two to four answer options that are usually perceived as equally desirable. However, it has also been argued that the relative standing of respondents (i.e. their relative scores) in the samples is relatively unaffected by normative instruments.

This study provides significant statistical support for the validity of the Innovation Profiler as a predictor of innovation talent and of business results from innovation. The authors hope that by identifying the innovation characteristics that correlate with business outcomes, the authors have contributed to the field. Companies can use this knowledge to accelerate their organizational transformation.

This research, and the Innovation Profiler based on it, enable companies to see and measure innovation talent for the first time. This talent is not held by the few and the privileged. In fact, women score as high as men and non-whites score slightly higher than whites. Innovation talent, as measured by the Innovation Profiler, can be an equalizer in the workforce. Finally, we hope that this paper helps companies attract more innovators into their workforce and to recognize and use more of their valuable skills.

To the authors’ knowledge, this is the first study to ask. “Can we predict the business results from innovation based on who is involved?” After extensive review of the literature, the authors have not found any other study asking this question. This study is also novel for: including intrapreneurs and entrepreneurs; and for including samples across the Americas, Europe, Asia and Africa. The study demonstrates a strong relationship between innovation talent and positive business results, with effect sizes that appear to exceed personality and other factors.

This chapter shows how to construct and test case-based macro models. The chapter makes use of national data to examine influences on quality-of-life of national cultures as complex wholes and entrepreneurship activities in Brazil, Russia, India, China, Germany, and the United States (the six focal nations) plus Denmark (a small-size, economically developed, nation). The study tests McClelland’s (1961) and more recent scholars’ proposition that some cultural configurations nurture entrepreneur startups, while other cultures are biased toward thwarting startups. The study applies complexity theory to develop and empirically test a general theory of cultures’, entrepreneurship’s, and innovation’s impact on quality-of-life across nations. Because culture represents a complex whole of attitudes, beliefs, values, and behavior, the study applies a set-theoretic approach to theory development and testing of alternative cultural configurations. Each of 28 economical developed and developing nations is scored for the level of the national cultures for each of six focal countries. The study selected for the study enables multi-way comparisons of culture-entrepreneurship-innovation-QOL among large- and small-sized developing and developed nations. The findings graphically present the complex national cultural configuration (x-axis) with entrepreneur nurture/thwart (y-axis) of the 28 nations compared to the six focal nations. The findings also include recognizing national cultures (e.g., Switzerland, the United States) nurturing entrepreneurial behavior versus other national cultures (e.g., Brazil and India) thwarting entrepreneurial behavior. The study concludes with a call to recognize the implicit shift in culturally implicit thinking and behavior necessary for advancing national platforms designed to successfully nurture entrepreneurship. Entrepreneur strategy implications include the observation that actions nurturing firm start-ups by nations low in entrepreneurship will unlikely to be successful without reducing such nations’ high levels of corruption.

The purpose of this paper is to illustrate the relationship between total quality management (TQM) and innovation and the way each impacts on the performance of agricultural research and development (R&D) organisations.

A gap score survey instrument based on the balanced scorecard approach, 25 most commonly reported TQM practices and two types of innovation were considered for data collection. Structural equation modelling was used for the analysis of the relationships between the surveyed constructs.

A positive relationship between TQM, innovation and performance was observed. A hypothesised model depicting the complex relationships between the investigated constructs was developed.

The model, which also predicts total effects of various organisational practices on performance, provides an opening for developing a TQM-innovation-performance framework for agricultural R&D organisations.

The survey instrument presents a novel approach for assessment of R&D policies and practices through determination of gap scores.

Investigates how organisations can progress from total quality (TQ) to business innovation and represents the first part of an EU sponsored research programme in total quality and innovation. First, definitions and underlying assumptions are analysed which enables a definition of TQ and innovation to be derived that can accommodate a natural organisational progression in terms of implementation. Second, TQ and Innovation are compared and contrasted by analysing models in each of the respective fields. The main findings were that, in general, innovation models were based more on organisational learning and appreciation of human capital than TQ models, which were based more on mechanistic process based continuous improvement. Finally, the results of a research study into innovation and total quality are presented and discussed. The study found that organisations which have a history of continuous improvement are more likely to go on and build a successful innovative culture.

The USA is the world’s largest economy, but is it a leading innovation nation? As economies mature and slow in growth, innovation will prove to be a key driver in maintaining transient advantage. This article presents a pulse on innovation in the USA as F1000 C-suite executives weigh in on their organization’s innovation health. It also compares the US score with proxy benchmark measures in other countries, and provides operational and strategic considerations to advance innovation platforms in US organizations. Managers will gain insight into common hurdles faced by some of America’s most prominent companies, as well as how to improve innovation practices in their own organization.

This current article reports on findings of innovation health in the USA based on responses from 1,127 F1000 executives (manager level and higher). F1000 executives report their innovation culture through completion of an innovation culture model survey developed by the authors. The F1000 is a listing created by Fortune magazine detailing the 1,000 largest companies in the USA based on revenues. This survey is considered one of the largest surveys on innovation culture in the USA to date.

One of the leading questions that this survey set out to answer is the current measure of innovation orientation amongst America’s largest organizations. Our findings suggest that US business is just beginning to catch the wave of innovation. Other major findings include: innovation amongst the F1000 is average at best; innovation is random and incremental; innovation strategy is missing in most organizations; there is an executive/employee innovation perception gap; innovation governance is missing; employees can not be blamed for a lack of innovation; and companies that fail to innovate will struggle even more.

There are a number of operational and strategic considerations presented to support the advancement of innovation in organizations. These include considerations around the leadership, resources, knowledge management and execution to strategically support innovation.

This is an original contribution in that it uses a scientifically developed model to measure innovation culture. It is the largest survey of innovation to date amongst the US Fortune 1000, and the finding present considerations to advance the innovation agendas of organizations.

The purpose of this paper is to address the key developments concerning innovation at universities at a macro level. It describes the key trends and changes in the governance of universities and the transformation of universities into organizational actors. This also affects the governance on academic research in the sense that it leads to a gradual evolution of the specific public science system in which research is being initiated and executed.

Cultural evolution involves social articulation and transmission of knowledge. What makes a culture distinctive is how it distributes interactions in the information-space.

The innovation policies of the European Union play a noticeable, but not yet dominant, role in the EU member states, at least not in the large member states. The wide gap between the North of Europe and the South and East of Europe in innovative performance is – despite the innovation policies of the European Union – still difficult to overcome.

The actual innovative performance of ten European countries has been evaluated. Northern European countries show a higher score on the Innovation Index, whereas countries in Southern Europe score relatively low. Can we relate this difference to cultural factors?