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Manufacturing firms are always faced with the problem of promoting operational performance and labor‐force management. The utilization of human resources is closely correlated with operations and production performance. This study investigates the correlation between human resource management (HRM) and business performance of large‐scale manufacturing firms in Taiwan. First, 16 subjects of HRM are designed to survey the importance level and achievement level of HRM by the sample firms. Productivity indices are also defined to measure business performance. Based on the survey, four critical HRM factors including 12 subjects are extracted by factor analysis. The difference between importance level and achievement level of subjects contained in each factor is examined. Furthermore, considering importance and achievement levels of HRM as features, fuzzy clustering analysis is employed to categorize the firms into four patterns. With various HRM characteristics, each pattern has different business performance in terms of productivity. Using a pattern approach, these findings can aid the firms in each pattern to improve their productivity by improving their HRM strategies.

Since a firm’s management performance can be evaluated in terms of financial ratios, efficient management using financial factors is proposed as the key element for upgrading a firm’s productivity. Investigates productivity in terms of certain financial factors of large‐scale manufacturing firms in Taiwan. First determines several influential financial factors using factor analysis. Based on these factors, employs fuzzy clustering approaches to categorize the manufacturing firms into several patterns with distinct characteristics of financial factors. Using the characteristics of productivity and financial factors for each pattern, makes two kinds of analysis, and proposes some suggestions to improve the firms’ productivity.

The purpose of this paper is to know whether the authors’ productivity pattern of library and information science (LIS) open access journals adheres to Lotka’s inverse square law of scientific productivity. Since the law was introduced, it has been tested in various fields of knowledge, and results have varied. This study has closely followed Lotka’s inverse square law in the field of LIS open access journals to find a factual result and set a baseline for future studies on author productivity of LIS open access journals.

The publication data of selected ten LIS open access journals pertain to authorship, citations were downloaded from the Scopus database and analysed using bibliometric indicators like authorship pattern, collaborative index (CI), degree of collaboration (DC), collaborative coefficient (CC) and citation counts. This study has applied Lotka’s inverse square law to assess authors’ productivity pattern of LIS open access journals and further Kolmogorov-Smirnov (K-S) goodness-of-fit test applied for testing of observed and expected author productivity data.

Inferences were drawn for the set objectives on authorship pattern, collaboration trend and authors’ productivity pattern of LIS open access journals covered in this study. The single authorship pattern is dominant in LIS open access journals covered in this study. The CI, DC and CC are found to be 1.95, 0.47 and 0.29, respectively. The expected values as per Lotka’s law (n = −2) significantly vary from the observed values as per the chi-square test and K-S goodness-of-fit test. Hence, this study does not adhere to Lotka’s inverse square law of scientific productivity.

Researchers may find an idea about the authors’ productivity patterns of LIS open access journals. This study has used the K-S goodness-of-fit test and the chi-square test to validate the authors’ productivity data. The inferences found out from this study will be a baseline for future research on author productivity of LIS open access journals.

This study is significant from the viewpoint of the growing research on open access journals in the field of LIS and to identify the authorship pattern, collaboration trend and author productivity pattern of such journals.

This study aims to analyze the productivity patterns of authors in Nigeria using publications indexed in Medline from 2008 to 2012 based on Lotka’s Law. Lotka’s Law of scientific productivity provides a platform for studying inequality in authors’ productivity patterns in a given field and over a specified period.

This study covers all the journal articles on HIV/AIDS pandemic in Nigeria over a period of five years (2008-2012) in Medline, of which 512 articles were reported to have been published during this period. In this paper, 306 articles that had HIV/AIDS in the title, published in 20 journals, and articles that had HIV/AIDS as author keywords were analyzed. Because no local database that indexed biomedical literature from Nigeria was available, Medline was used, which is not only a robust and flexible database that includes articles from Nigeria but is also the largest medical database that indexes over six-and-a-half million articles from 3,400 biomedical journals.

While HIV/AIDS can be considered a global pandemic, Nigeria has the second highest number of new infections reported each year, and an estimated 3.7 per cent of the population is living with the dreaded disease. This study presents a general picture of the distribution of papers as single-author papers, multiple-author papers and the measures of co-authorship. The findings of the study reveal that in the productivity distribution for authors on the subject of HIV/AIDS, only co-authors and non-collaborative authors’ categories fit in the Lotka’s Law, whereas all-authors and first-author categories differ from the distribution of Lotka’s inverse square law.

The empirical evidence used in this paper was based on only articles of HIV/AIDS pandemic in Nigeria that had HIV/AIDS the title. Therefore, the findings of this study might not be the generalized to other biomedical research studies.

The originality of this paper lies in the fact that the productivity pattern of each of the different author categories on the subject of HIV/AIDS is a first of its kind in the Nigerian context.

This study aims to investigate the research productivity in terms of publications count of the top four premiers Indian Institute of Management (IIM) institutions and to explore the current research trends.

Bibliometric techniques were employed to assess the performance in terms of research productivity of authors affiliated with IIMs. The Elsevier Scopus database was selected as a tool to extract the prospective publications data limiting the time frame for 2010–2021. The IIM-Ahmedabad, IIM-Bangalore, IIM-Calcutta and IIM-Lucknow have been selected for the study. The harvested data were analyzed by using the standard bibliometric indicators and scientometric parameters to measure the research landscape such as average growth rate, compound average growth rate, relative growth rate, doubling time, degree of collaboration, collaborative index, collaborative coefficient and modified collaborative coefficient. VOSviewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The research productivity of selected four IIMs has shown an upward trend during the study period from 2010–2021 and accrued 4,397 publications with an average of 366 publications per year. The authorship patterns demonstrate the collaborative trends as most of the publications were produced by the multiple-authors (81.03%). IIM-Ahmedabad has produced the maximum number of publications (32.20%). The research productivity of IIMs has come out in collaboration with the 125 nations across the world and the USA, the UK, Canada, Germany and China are the front runners with IIMs in the collaborative network. The high magnitude and density of collaboration are evident from the calculated mean values of the degree of collaboration (0.82). The mean values of the collaborative index (2.64), collaborative coefficient (0.51) and modified collaborative coefficient (0.51) demonstrated a positive trend, but indicate the fluctuation in the collaborative pattern as time proceeds.

The study is limited to the publications data indexed in the Scopus database, therefore the outcome may not be generalized across other databases available in the public domain like Web of Science (WoS), PubMed, Dimensions and Google Scholars.

The findings of the study may aid academics and library professionals in identifying research trends, collaboration networks and evaluating other academic and research institutions by using the current advancement in data analysis.

The present study is the first effort to evaluate the research productivity of IIMs. The expanding literature will make an important contribution to identifying patterns and evaluating current research trends on a worldwide scale.

This chapter investigates productivity and cost patterns in the all-cargo US air transport sector. We empirically test the productivity growth influence of changes in unexplained technology, air operations movement characteristics, and factor input prices. Findings show productivity trends depicting negative growth for the 1993–2001 sample, then shifting measurably such that productivity trends depict positive growth for the 2002–2014 sample. The post 2001 growth was fueled by changes in unexplained technological advancements. We interpret this finding as an indication of the importance of technological innovation as a performance enhancer in this transport sector. Findings also reveal a lack of productivity change associated with changes in input prices and movement characteristics. We interpret input price findings as indicating increases in factor input prices such as wages and fuel prices are commensurate with enhanced labor and fuel productivity. The movement characteristic findings are attributable to a lack of sustained increases in load factors, stage length, network size and carrying more volume over the network (density).

The objective of this paper is to investigate the effects and relative influence of: grid patterns; variability of foundation sizes; total surface area; and average surface area, on formwork labour productivity of isolated foundations.

To achieve this objective, a sufficiently large volume of productivity data were collected and analyzed at both levels; macro, and micro, using the linear regression method. As a result, the effects and relative influence of the investigated factors on formwork labour productivity are determined and quantified.

The findings show significant impacts of the buildability factors investigated on formwork labour productivity, and substantiate the importance of applying the rationalization and standardization concepts to the design stage of construction projects.

Further research into the effects of buildability factors on formwork, and other related trades of in situ reinforced concrete material, i.e. rebar fixing/installation and concreting, labour productivity, which are common to other structural elements and activities such as, grade/ground beams, columns, walls, beams, and slabs, is recommended, so that the related findings can ultimately be used to develop an automated “Buildability Design Support System” to formalize the buildability knowledge of reinforced concrete construction projects.

The outcomes of this research provide designers with feedback on how well their designs consider the requirements of buildability principles, and the tangible consequences of their decisions on labour productivity. In addition, practical recommendations deduced from the findings are presented, which upon implementation, can improve the buildability level of this activity, hence translate into higher labour efficiency and lower labour costs. On the other hand, the depicted patterns may provide guidance to construction managers for effective activity planning and efficient labour utilization.

The findings fill a gap in buildability knowledge and its influence on formwork labour productivity of an important, labour intensive, activity within the in situ reinforced concrete construction projects.

Within the service sectors, Knowledge Intensive Business Services (KIBS) play an important role in local and regional economies as sources of competitive advantages and providing knowledge-intensive inputs to the business process of small and medium-sized enterprises. This study aims to analyze the changes in financial performance of KIBS industry in Italy over the period from 2012 to 2017.

This paper examines the efficiency of the KIBS firms by applying data envelopment analysis (DEA) to compute the Malmquist Productivity Index for the period under investigation. The DEA-based Malmquist productivity analysis is applied at firm level using a sample consisting 1.674 companies, representative of the Italian KIBS sector and related to three different NACE activity code (72-computing services; 73-research and development; 74 other professional business activities). The efficiency measures are then used to characterize KIBS firm financial performance through the analysis of average productivity patterns grouped by Italian geographical regions. The Malmquist productivity measures are decomposed into two components: efficiency change and technical change index. The overall analysis is coupled with a financial ratio analysis approach, selecting return on equity (ROE) and leverage ratio as descriptor to validate the results and better characterize differences in efficiency patterns among geographic-based groups of KIBS companies.

Over the period 2015-2017, the results show that the average annual growth of the overall Malmquist productivity index was positive in nine Italian regions that represent only 17 per cent of the total KIBS firms selected. On the other side, a decrease of the average performance measure is observed for the five geographic areas that contribute to 75.7 per cent of the total sample. In general, the technological change component, as a measure of innovation, strongly limits the productivity growth behavior of KIBS industry for all geographic regions. The use of selected financial ratio does not provide additional insight to the performance investigation and further in-depth studies are needed to better evaluate the correlation between average productivity results and regional business dynamics.

The study investigates the applicability of DEA-based Malmquist indices to the analysis of the productivity behavior of KIBS industry at regional level. It will be of value to provide first evidence to the policymakers to understand industry growth pattern in time frame selected and relate them to additional business factors to detect specific industry constraints.

The analysis in this paper contributes to the existing body of knowledge on industry performance measurement by applying specific analytical techniques to the productivity of Italian KIBS companies. The paper also contributes to the limited body of academic literature investigating KIBS industry at national level proposing a methodological framework that constitutes a first attempt to track average productivity behavior at regional level.

The purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.

The authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.

The authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.

This study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.