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Describes how the idea of normalised impact factor came into being and the method of its determination. In all, five properties of the normalised impact factor have been identified and described. It is observed that the ranking of a journal in its own category is better revealed by the normalised impact factor and the average normalised impact factor seems to provide better indication of the comparative performance of a set of laboratories engaged in diverse areas of research.

Can the journal impact factors regularly published in the Journal Citation Reports (JCR) be shaped by a self‐fulfilling prophecy? This question was investigated by reference to a journal for which incorrect impact factors had been published in the JCR for almost 20 years: Educational Research. In order to investigate whether the propagation of exaggerated impact factors had resulted in an increase in the actual impact of the journal, the correct impact factors were calculated. A self‐fulfilling prophecy effect was not observed. However, shows that the impact factors for Educational Research published in the JCR were based on calculations that erroneously included citations of a journal with a similar title, Educational Researcher, which is not included in the JCR. Concludes that published impact factors should be used with caution.

The purpose of this paper is to refine and expand technology adoption theory for a healthcare logistics setting by combining the technology–organization–environment framework with a business process management (BPM) perspective. The paper identifies and ranks factors impacting the decision to implement instances of technologies in healthcare logistics processes.

A multiple case study is carried out at five Danish hospitals to investigate the bed logistics process. A combined technology adoption and BPM lens is applied to gain an understanding of the reasoning behind technology adoption.

A set of 17 factors impacting the adoption of technologies within healthcare logistics was identified. The impact factors perceived as most important to the adoption of technologies in healthcare logistics processes relate to quality, employee work conditions and employee engagement.

This paper seeks to understand how managers can use knowledge about impact factors to improve processes through technology adoption. The findings of this study provide insights about the factors impacting the adoption of technologies in healthcare logistics processes. Differences in perceived importance of factors enable ranking of impact factors, and prioritization of changes to be implemented. The study is limited to five hospitals, but is expected to be representative of public hospitals in developed countries and applicable to similar processes.

The study contributes to the empirical research within the field of BPM and technology adoption in healthcare. Furthermore, the findings of this study enable managers to make an informed decision about technology adoption within a healthcare logistics setting.

Mega-projects (MP) are important not only to the stakeholders involved in their development and construction, but also to the societies, economies, and environments impacted by them. Given the importance of MP and the prevailing trend toward poor performance, the purpose of this paper is to identify changes in project development and execution to increase the likelihood of success on MP.

The researchers performed an extensive literature review in the initial phase. Then, interviews with experienced mega-project professionals were conducted to gather information on factors contributing to success and failure on MP. A detailed survey was then carried out to quantify and assess the frequency and impact levels of these factors. Finally, case studies were performed to analyze the manifestations of these impact factors on MP and identify mitigation strategies.

After gathering data from more than 100 projects, the researchers identified and prioritized 34 factors that have high frequency and negative performance impacts on MP, categorizing them into five categories. These factors were then used to create a mega-project preparedness assessment process and a lessons learned repository around these factors, which includes factor descriptions, examples from actual projects, and recommendations. The result is a structured method for enabling comprehensive mega-project risk management and mitigation planning.

The research has gone beyond any previous generic lessons learned, to enable project teams to make structured assessments of critical mega-project impact factors. Assessing these impact factors will enable more accurate and thorough mitigation planning for them and, thus, improve mega-project performance.

The purpose of this paper is to select the main impact factors of environment change automatically and identify and analyze the potential environmental impact factors with time delay by computer simulation, analyzing the impact rate of environmental impact factors. Then, the environmental impact factors analysis decision support system based on self‐organizing data mining model is designed.

Applying data mining methods in the analysis and decision making of regional environmental impact factors will have broad perspective. Self‐organization data mining is a new modeling method of complex systems modeling with strong modeling capability. It was first presented by A.G. Ivakhnenko, based on the principle of self‐organization of biological cybernetics and Kolmogoorov‐Gabor polynomial function. In this paper, the impact factors of regional environment quality evolution based on self‐organization data mining method is studied, selecting the main impact factors of environment change automatically by computer simulation, analyzing the impact contribution rate of environmental impact factors.

The environmental impact factors analysis decision support system based on self‐organizing data mining model is designed.

Accessibility and availability of data are the main limitations affecting which model will be applied.

The paper has important theoretical and practical significance for the sustainable development of regional environment, resource, economy system and the constitution of environmental protection and management measures.

This paper not only exploits new application domains of self‐organization data mining, but also explores new ways for regional environment impact factors analysis.

The construction industry is a crucial industry for national development worldwide. Because the construction industry is tied to national and international economic activities, the COVID-19 outbreak has limited construction projects. Therefore, this study investigates the most influential factors regarding COVID-19 and their effects on the construction industry.

The potential impacts of COVID-19 on the construction industry were identified through a realistic literature review and interviews with professionals. A questionnaire was distributed via e-mail to architects, civil engineers and contractors who play vital roles during the construction processes. The data were analysed using SPSS 22 and LISREL 8.7 software to quantify the most influential pandemic-related factors faced by the construction industry.

Ten influential pandemic factors affecting the construction industry in Turkey were identified. Among them, “increased costs and price escalations due to shortage of raw materials and supply chain disruption” and “challenges with payment and cash flows” were determined as the most influential pandemic factors.

This research aims to advance comprehension of pandemic impacts and contributes an incipient assessment framework based on 10 determined pandemic factors. Therefore, contractors, architects and civil engineers may analyse their weaknesses and organise precise priorities so that their firms may remain competitive, thus minimising the adverse impact of COVID-19 and possible forthcoming waves.

Few studies have identified the effect of pandemics on the construction industry qualitatively, forcing management to make projections to the current situation. Moreover, no study has provided insights into the influential factors of pandemics using quantitative methods. Therefore, this study comprehensively and quantitatively determines the relevant COVID-19 pandemic factors using exploratory factor analysis (EFA) and utilises confirmatory factor analysis (CFA) and structural equation modelling to present a structural model of how pandemic factors affect the Turkish construction industry.

This paper aims to describe and debate a series of concerns that may affect, influence or manipulate the “Social Science Citation Index” (SSCI) and its impact factors.

A conceptual discussion of the SSCI and its impact factors are provided.

A series of concerns and potential biases of the SSCI and its impact factors are identified. These question the reliability of the SSCI and impact factors for identifying as tools the “best” journals in the marketing field.

The SSCI and its impact factors may cause both “win‐win” and “win‐lose” situations in and between stakeholders of the scholarly communities of the marketing discipline worldwide. The question is raised whether there may be a situation of the “prisoner's dilemma”.

The SSCI and its impact factors are gaining terrain and acknowledgement in scholarly marketing communities worldwide. This raises the crucial question of whether or not the SSCI and its impact factors will benefit the scholarly communities of the marketing discipline worldwide in the long‐term perspective.

A principal argument brought up for further debate is how the increasing acknowledgement and applications of the SSCI and its impact factors may influence the marketing discipline and its scholarly communities worldwide as a whole. Another principal argument brought up for further debate is that the current algorithm that underpins the impact factors of the SSCI may be affected, influenced (and at worst manipulated) in the self‐interest of the journal publishers, the editors and the editorial boards. Authors may also feel the pressure to match perceived expectations and potential requirements in their own self‐interest.

This study distinguishes the academic influence of different papers published in journals of the same subject or field based on the modification of the journal impact factor.

Taking SSCI journals in library and information science (LIS) as the research object, the authors first explore the skewness degree of the citation distribution of journal articles. Then, we define the paper citation ratio as the weight of impact factor to modify the journal impact factor for the evaluation of papers, namely the weighted impact factor. The authors further explore the feasibility of the weighted impact factor in evaluating papers.

The research results show that different types of skewness exist in the citation distribution of journal papers. Particularly, 94% of journal paper citations are highly skewed, while the rest are moderately skewed. The weighted impact factor has a closer correlation with the citation frequency of papers than the journal impact factor. It resolves the issue that the journal impact factor tends to exaggerate the influence of low-cited papers in journals with high impact factors or weaken the influence of high-cited papers in journals with low impact factors.

The weighted impact factor is constructed based on the skewness of the citation distribution of journal articles. It provides a new method to distinguish the academic influence of different papers published in journals of the same subject or field, then avoids the situation that papers published in the same journal having the same academic impact.

During the past decades, journal impact data obtained from the Journal Citation Reports (JCR) have gained relevance in library management, research management and research evaluation. Hence, both information scientists and bibliometricians share the responsibility towards the users of the JCR to analyse the reliability and validity of its measures thoroughly, to indicate pitfalls and to suggest possible improvements. In this article, ageing patterns are examined in ‘formal’ use or impact of all scientific journals processed for the Science Citation Index (SCI) during 1981‐1995. A new classification system of journals in terms of their ageing characteristics is introduced. This system has been applied to as many as 3,098 journals covered by the Science Citation Index. Following an earlier suggestion by Glnzel and Schoepflin, a maturing and a decline phase are distinguished. From an analysis across all subfields it has been concluded that ageing characteristics are primarily specific to the individual journal rather than to the subfield, while the distribution of journals in terms of slowly or rapidly maturing or declining types is specific to the subfield. It is shown that the cited half life (CHL), printed in the JCR, is an inappropriate measure of decline of journal impact. Following earlier work by Line and others, a more adequate parameter of decline is calculated taking into account the size of annual volumes during a range of fifteen years. For 76 per cent of SCI journals the relative difference between this new parameter and the ISI CHL exceeds 5 per cent. The current JCR journal impact factor is proven to be biased towards journals revealing a rapid maturing and decline in impact. Therefore, a longer term impact factor is proposed, as well as a normalised impact statistic, taking into account citation characteristics of the research subfield covered by a journal and the type of documents published in it. When these new measures are combined with the proposed ageing classification system, they provide a significantly improved picture of a journal‘s impact to that obtained from the JCR.

The purpose of this paper is to determine the relationship between the access mode of research articles [Open Access (OA) and Toll-Access (TA)] and their subsequent citation counts in Biological and Physical Sciences in three Impact factor zones (High, Medium and Low).

Three subjects each from Biological Sciences (Biochemistry, Cell Biology and Genetics) and Physical Sciences (Astronomy, Oceanography and Optics) were selected for the study. A comprehensive list of journals (TA and OA) in select subjects of Biological and Physical Sciences was prepared by consulting Journal Citation Report’s Master Journal List (for the compilation of both Open Access and Toll Access journal list) and Directory of Open Access Journals (for the compilation of Open Access journal list). For each journal, essential details like content language, format, year of publication, access mode (Open Access or Toll Access), etc. were obtained from Ulrich’s Periodical Directory. Web of Science (WoS) was used as citations indexing tool in this study. The data set was run on the WoS to collect the citation data.

The results of the study indicate that open mode of access is not a prerequisite for higher citation boost as in the majority of the cases in this study, TA articles have garnered a greater number of citations as compared to open access articles in different Impact factor zones in Biological and Physical Sciences.

A novel approach has been adopted to understand and compare the research impact of open access (OA) and toll access (TA) journal articles in the field of Biological and Physical Sciences at three Impact factor zone levels to reveal the citation metrics encompassing three parameters, i.e. citedness, average citation count and year wise distribution of citations in select subjects of Biological and Physical Sciences.

The peer review history for this article is available at: https://publons.com/publon/[DOI]/10.1108/OIR-01-2021-0029