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The authors seek to better understand the relationships between science production, national wealth, inequality, and human development around the globe.

The chapter uses econometric models, including Granger causality, to test alternate hypotheses about whether more economic wealth is related to more science or if more science leads to more wealth.

The immediate result of our models is that a country’s wealth contributes to the conditions necessary for productive science. While large countries produce many research articles in the STEM+ fields more or less irrespective of their per capita GDP, with countries like the Soviet Union, China, or India being important contributors to world science, the most productive countries were the richer ones. GDP per capita values are important predictors for higher numbers of STEM+ research articles adjusted for population size. Nevertheless, human development and income equality also have a positive relationship with science productivity. While the effect of income equality is less strong, it has importantly and steadily increased over the last 50 years.

This chapter is among the first to show that countries with similar levels of human development that are more equal in income distribution are more productive in science, while countries of similar wealth that are more equal in income distribution are not necessarily more productive in science.

This paper aims to examine factors influencing the productivity in research and publication between science and social science.

A qualitative approach with interviews for 40 academics in four public universities in Indonesia was applied to get an in-depth understanding of the issues.

The results of this study demonstrated that individual factors instead of institutional factors that contributed to the productivity of academics in science as compared to academics in social science.

Despite there were influential effects of institutions in which the socializing process of internalizing the values, norms and scientific roles under the auspice of qualified supervisors or advisors, there seemed to be an individual capacity that comes in between. The implications of this study are discussed in the article.

The purpose of this paper is to study gender differentials in scientific productivity while looking at academic discipline and advisor practices. The natural sciences and the liberal arts are shown to constitute two organisational cultures which affect the ability of women to attain excellence on a par with men.

The empirical study is based on a sample of 660 doctoral students in two universities in Israel. Regression procedures were employed to predict productivity.

There is a slight gender gap in scientific productivity, but only in single‐authored papers. This suggests that publishing together with an advisor – which is the common practice in the natural sciences – is more conducive to gender parity. Students' reports suggest that their advisors evince little differential treatment of men versus women, thereby ruling out the possibility of overt advisor bias against women. Overall, the natural sciences appear to be more supportive of students' success while the liberal arts seem to challenge students to struggle on their own, putting women in greater jeopardy of suffering family‐work tensions.

Universities need to appreciate the disciplinary differences within them and help students to get greater support in the “natural selection” mechanisms that are often unconsciously employed in higher education.

This paper adds an important angle in appreciating currently dominant approaches to work‐family balances while focusing on unintended exclusionary mechanisms embedded in the standards and culture of different scientific disciplines.

Recent economic research has focused on the economic effects of the social environment. In the economic literature, important phenomena are considered, at least in part, as results of the individual's social environment. There is a similar revival of interest among economists who analyse the world of science and basic research. In this case as well, the environment plays a key role in the agent's behaviour. This paper aims at an empirical analysis of the influence of social interactions on scientists' productivity.

In the econometric analysis the authors investigate the aggregate importance of social interactions in science through the analysis of data on publications in four scientific fields of seven advanced countries. The paper builds a dynamic autoregressive model which provides long‐run multipliers. The model is estimated with a panel fixed effects methodology.

Social interactions among researchers have positive effects on a scientist's productivity, and there is a U‐shaped relation between the size of a scientific network and individual productivity. This result is interpreted as providing evidence for threshold externalities and increasing returns to scale.

Other better indicators of social interactions in science should be found and used in estimates. The set of countries and fields should be enlarged.

The paper represents the first econometric investigation of the issue at country level, and provides interesting results which are new for the economic literature.

This study aims to analyze Saudi scientific output in the field of computer science in Web of Science database, covering the years 1978 through 2012.

The study involved analyzing 998 publications in terms of the publication count and its growth, citation, share of international collaboration, research areas and researchers’ productivity.

The results show that the number of papers produced in computer science field has only increased after year 2007; this is because Saudi universities have applied a catch-up strategy to increase its research output. Also, our study reveals that the publication performance of Saudi scientists in computer science was domestic and suffers from low international visibility. Only two universities took the lead in the production of computer science research. Furthermore, computer science research trends in Saudi Arabia focused on engineering, followed by mathematics and telecommunications.

Studies on international academic publication productivity in the Middle East, particularly in Arab countries such as Saudi Arabia, are rarely found. In fact, bibliometric studies on Saudi researchers in the field of computer science are not available. Therefore, the originality of this study resides in being the first study to measure publication productivity of Saudi researchers in the field of computer science.

Using bibliometrics to examine eight core journals in the year 2000 for the disciplines of higher education and library science, characteristics of the authors were determined, including gender or sex; Carnegie Classification or institutional affiliation; and position of the authors. Characteristics of the articles were also examined, including the research methods used such as descriptive statistics, inferential statistics, or qualitative analysis. A content analysis of each article was performed to determine the subjects discussed in each literature. For both disciplines, it was learned that males publish more, the highest Carnegie Classification, extensive research institutions, were represented the most, and authors came from academic departments other than their own disciplines. In higher education, inferential statistics were used frequently; in library and information descriptive statistics were used frequently; both disciplines failed to use research methodologies regularly. From these findings, it appears that both disciplines are still emerging and are in their early stages of development.

The study identifies 621 authors who contributed 1,423 periodical articles and monograph publications to the geographical literature on Nigeria between 1901 and 1970. Publications/author relationship shows a perfect Bradford distribution comprising fifty‐three core authors having each six publications or more and among them contributing 38 per cent of all items. The significance of these results for retrospective bibliographical searches and their methodological implications for documentation studies in general are suggested and briefly discussed.

ANY system based on a standard unit capable of being divided by tenths or hundredths for any lesser value enormously simplifies calculations and through a saving of time and labour reduces costs. It is therefore a matter for satisfaction that this country, laggard as usual, will within a matter of days fall into step with other industrial nations. Indeed, as the official guide to the decimal system points out, almost every country in the world uses it. So Britain at last joins the great majority.

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.

A psychosocial, developmental perspective was used to examine the mentoring experiences of scientists. Little is known about the timing of when mentors first appear, the quality of these relationships, the specific mentoring support behaviors, or how scientists typically learn to mentor. The paper aims to discuss the above issues.

The author conducted 23, semi-structured interviews with Australian scientists. Questions focussed on mentor-like support scientists received and provided. Interviews were analyzed and themes were coded using Dedoose software.

Scientists who had mentors as undergraduates were more likely to report long-lasting relationships with their mentors and more positive interactions with their protégés. Scientists reported the following career mentoring behaviors: modeling how to do science, sponsorship, collaboration, and practical supervision. Important psychosocial mentoring behaviors were being approachable, building confidence and providing encouragement. Almost half of the scientists never had a mentor. Most (n=14) scientists learned to mentor by emulating their mentors. Findings highlight the prevalence of dysfunctional behaviors, even in supportive relationships.

The findings suggest that graduate program managers might consider investing resources to improve mentoring experiences of doctoral students as this is a critical period for their professional development. Further, activities involving collaboration deserve emphasis in mentoring relationships.

The study identified a “window” when mentoring support is important for scientists; highlighted specific behaviors that support career development in science; and clarified how some scientists learn to mentor others. Results add to the literature on dysfunctional mentoring relationships.