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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 study aims to analyze factors impacting firms’ success and persistence in a digital platform competition using the structure-conduct-performance (SCP) framework. The study also includes real-life cases that are beneficial to academicians and practitioners to understand and develop strategies for success and persistence during uncertainty.

A literature review to identify the factors that impact success and persistence in a digital platform competition was conducted following Webster and Watson (2002). Findings were integrated into a SCP framework to examine and understand the identified factors’ relational impact.

While analyzing factors under the SCP framework, all factors were divided into three categories: those impacting positively, those impacting negatively and those with ambiguous impact on the success and persistence in digital platform competition. Digital platform firms can exploit the positively impacting factors to increase market share by being distinctive from other digital platform firms and becoming dominant by withstanding competition. On the other hand, negatively impacting factors increase barriers to entry, intensify competition and reduce the distinctiveness of digital platform firms. Lastly, a few factors may have either a positive or a negative impact depending upon the particular characteristics of the firm/industry.

The study opens the scope for future research on empirically testing the developed conceptual framework and relationships by developing propositions to posit the possible impact of these factors on digital platforms’ success and persistence.

The study contributed to the existing literature by using SCP framework to analyze the factors affecting firm’s success and persistence in a digital platform competition. Also, the study has discussed the relational impact of factors rather than their impact in isolation.

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.

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.

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.

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 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.