Systematic Literature Review Process Research Articles

Circular economy adoption in MSMEs: unveiling enablers and barriers

Purpose The study aims to explore the enablers and barriers to the adoption of circular economy (CE) practices in micro, small and medium enterprises (MSMEs) and examine how these factors differ between developed and developing countries. Design/methodology/approach The research uses a systematic literature review (SLR) methodology to identify key enablers and barriers to CE adoption in MSMEs. The SLR process involved a detailed search and analysis of relevant academic articles from the Scopus and Web of Science databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency. Findings The study identifies 19 enablers and 16 barriers to CE adoption in MSMEs. Technological upgrades are the key factor helping MSMEs successfully implement CE practices, while financial constraints are the main challenge they face, according to studies from both developed and developing countries. Originality/value This research contributes to the existing body of literature by not only identifying the primary factors that either support or impede the implementation of CE by MSMEs but also by classifying them according to developed and developing countries to provide policymakers and MSME stakeholders with valuable insights on enhancing the implementation of CE in both countries by taking into account the particular barriers and enablers faced by each group individually.

Initial surgical management of injuries to the lower extremities in patients with multiple and/or severe injuries - A systematic review and clinical practice guideline update.

Our aim was to develop new evidence-based and consensus-based recommendations for the initial inhospital management of lower-extremity injuries in patients with multiple and/or severe trauma. This guideline topic is part of the 2022 update of the German Guideline on the Treatment of Patients with multiple and/or severe Injuries. MEDLINE and Embase were systematically searched to May 2021. Randomised controlled trials, prospective cohort studies, and comparative registry studies were included if they compared interventions for the initial surgical and non-surgical management of fractures, dislocations or vascular injuries of the lower extremities in patients with multiple and/or severe trauma. We considered patient-relevant clinical outcomes such as mortality, complication rates, length of stay, and function. Risk of bias was assessed using NICE 2012 checklists. The evidence was synthesised narratively, and expert consensus was used to develop recommendations and determine their strength. Eleven studies were identified. They addressed time to definitive fixation (n = 10 studies) and amputation (n = 1). Two new recommendations were developed, one was modified. All recommendations achieved strong consensus. This systematic literature review and subsequent expert consensus process resulted in the following new key recommendations. It is recommended that isolated and multiple lower-extremity fractures are managed with primary definitive fixation in patients whose condition is stable. Patients condition is not considered stable should be managed with primary temporary fixation. In addition, it is recommended that dislocations of the lower extremities are reduced and immobilised as early as possible.

Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing-Assisted Intelligent Systematic Literature Review System.

Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task. This study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data. We followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning-based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus-associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines. Our Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks). Cutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously.

A Systematic Literature Review on Visualisation Skills for Understanding Science

Previous and recent research have documented the argument fuelled by credential scholars about visualisation skills in various subjects. However, whether this visualisation skill can fulfil students’ understanding has been debated. Meanwhile, despite its widely used in other subjects, the application of science subjects, particularly for understanding certain topics, is still sparse. This study identifies suitable visualisation skills to be incorporated into a science learning approach. Furthermore, a systematic literature review has been conducted to achieve the objective. This paper shows the rigorous systematic literature review process done on some selected journal articles and papers on visualisation skills. The analysis indicates that, while different subjects apply different visualisation skills, most share similar themes in implementing visualisation skills as proven necessity advocated in the 21st learning. Thus, these finding act as an enhancing tool to propose the development of various topic visualisation approaches.

The Automation of Science? Possibilities and Boundaries of AI Applications for Conducting Systematic Literature Reviews

Science automation, driven by advanced technologies, such as artificial intelligence (AI), is reshaping research in a variety of disciplines. The exponential growth of publications increases the need for automation in systematic literature reviews (SLRs). In this study, we investigated how AI applications support SLRs at different stages. After evaluating twenty-two AI applications, we identified the most useful based on their functionalities in the SLR process. This study provides important insights into the dynamic interplay between science automation, AI, and a systematic literature review. The main contribution is the identification and discussion of the relevance of the analyzed applications at different stages of the systematic literature review. We also discuss the potential for the automation and augmentation of research activities to address various scientific problems.

Systematic Literature Review on the Implementation of the Pesantren Muadalah Certificate Policy in Indonesia

This research explores how Islamic boarding schools are implementing and responding to the modern education system introduced by the Government through the equalization policy (mu'ilah). Considering the vast amount of existing research, a review is necessary to categorize the literature, ensuring the information is clearly and easily understood. The paper applies the Systematic Literature Review (SLR) method, focusing on public policy implementation studies from 2019 to 2023. The SLR process includes defining Research Questions (R.Q.), setting Inclusion and Exclusion Criteria, and conducting Quality Assessments (QA). The findings reveal that the Mujadi diploma policy has been aligned with the Government's formal school diploma. Islamic boarding schools issuing Mujadi diplomas are referred to as Islamic Boarding Schools, specifically those that run Muilah Education Units (SPM). Based on both the supporting and inhibiting factors identified from various sources, the Mujadi policy has proven to be effective in its implementation within the Mujadi Islamic boarding schools.

Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview

This study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs’ generative capabilities with the precision of real-time information retrieval. We review in detail the three key processes of the RAG framework—retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature search, literature screening, data extraction, and information synthesis. Future research aims to optimize the interaction between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with particular emphasis on the retrieval of information from individual scientific papers and the integration of these data to produce outputs addressing various aspects such as current status, existing gaps, and emerging trends.

Artificial intelligence methods used in various aquaculture applications: A systematic literature review

AbstractThis research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.

Do employees matter, or is everything a facade? Effective knowledge management targeting well-being

Purpose The competency trap can occur when organizations become resistant to change due to their existing competencies, leading to a culture of complacency and hindering adaptability and innovation. This paper aims to understand this trap and its hindrance to organizational learning and knowledge acquisition. The study aims to integrate employee well-being into knowledge management (KM) strategies to overcome obstacles and demonstrate its significant contribution to effective KM and improving overall organizational health. Design/methodology/approach A systematic literature review (SLR) process was used in this research, with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol used to scrutinize articles for review. Only 50 peer-reviewed articles from 2000 to 2023 that focused on KM and employee well-being were included for review. Findings Organizations can tackle the competency trap by managing knowledge effectively and prioritizing employee well-being. When considered for effective KM, the PERMA (positive emotions, engagement, relationship, meaning and accomplishment) facets of well-being strategically supports knowledge sharing and sustainable organizational change through KM. Practical implications Focusing on PERMA facets of well-being in KM, an organization can emphasize employees' sense of achievement, addressing the competency trap to build a culture of knowledge sharing. This approach benefits professionals in developing an effective KM system. Originality/value This paper addresses the challenge of the competency trap, which has gained less academic attention, and explores KM from a well-being perspective.

Practicing Netiquette in Online Communication Between Students and Professors in Higher Education: A Systematic Review

The main objective of this research is to perform an in-depth analysis of current online behaviors and desired netiquette rules among students and teachers in higher education institutions. The pervasive integration of technology into education, especially during the COVID-19 pandemic, has necessitated a shift from traditional face-to-face learning to online platforms. This transition highlights the importance of netiquette— a set of guidelines for etiquette on the internet. The systematic literature review design was performed to critically and analytically explore the findings of the previously published studies on the topic. A total of 8 peer-reviewed articles passed the inclusion criteria and were reviewed to extract data for the analysis. A PRISMA model was used to conduct the systematic literature review process. The review conducted in this study examines the extent to which these norms are understood and adhered to within higher education, particularly focusing on communication between students and professors. The research encapsulates the evolution of online communication practices and identifies the challenges and opportunities arising from the reliance on digital platforms in academic settings. By synthesizing existing literature, the study reveals gaps in knowledge and practice regarding netiquette, underscoring the necessity for more targeted research to develop effective strategies for fostering positive and productive online interactions. Ultimately, this research aims to contribute to the enhancement of communication practices in digital learning environments, thereby improving the overall quality of education delivered online.

Understanding behavioral strategy: a historical evolutionary perspective in “Management Decision”

PurposeThis article aims to trace the historical development of the behavioral strategy (BS) field, which implements psychology in strategic management. Mainly, it provides a contextual understanding of how this stream of research has historically evolved and what relevant future trajectories are. This work is part of the “over half a century of Management Decision” celebrative and informal Journal section.Design/methodology/approachWe consider BS literature produced in management decision (MD), the oldest and longest-running scholarly publication in management, as a proxy for the evolution of management thought. Through a Systematic Literature Review (SLR) process, we collected – via the MD website and Scopus – a sample of 97 BS articles published in MD from its foundation (1967) until today (2024). Regarding the analysis, we adopted a Reflexive Thematic Analysis approach to synthesize the main BS topics, then read from a historical perspective regarding three “eras” over which the literature developed. Selected international literature outside the Journal’s boundaries was considered to complement this historical analysis.FindingsHistorically, within the BS field, the interest passed from the rules to rationally govern strategic decision-making processes, to studying what causes cognitive errors, to understanding how to avoid biases and to being prepared for dramatic changes. The article also identifies six future research trajectories, namely “positive heuristics,” “context-embedded mental processes,” “non-conventional thinking,” “cognitive evolutionary triggers,” “debiasing strategies” and “behavioral theories for new strategic challenges” that future research could investigate.Research limitations/implicationsThe limitation of the study lies in its exclusive focus on MD for investigating the historical evolution of BS, thereby overlooking critical contributions from other journals. Therefore, MD’s editorial preferences have influenced results. A comprehensive SLR on the BS field is still needed, requiring broader journal coverage to mitigate selection biases and enhance field appraisal.Originality/valueThis contribution is the first to offer a historical evolutionary view of the BS field, complementing the few other reviews on this stream of research. This fills a gap in the study of the evolution of management thought.

Connecting the dots in green food purchasing behavior literature: A system thinking approach for systematic literature reviews

AbstractRecent years have been characterized by an ever‐growing interest in consumers' behavior while purchasing green food products. Although existing research has produced a great number of papers on this topic, the knowledge generated in the field appears fragmented and, in certain cases, ambiguous. The main reasons can be traced back to the lack of reference frameworks that clarify the most used concepts, thus providing a shared language in this research domain. Despite other literature reviews that have been carried out on the domain of green food purchasing behavior, these works mainly rely on narrative summaries and qualitative analyses, which can overlook the complexity and interdependencies inherent in this research domain. The complexity of interconnected factors within this research domain poses challenges in effectively understanding and representing the underlying dynamics. To overcome these limitations, this paper proposes the use of a System Thinking based approach as a valuable method to provide a complete view of dynamics occurring in the green consumption behavior domain. In particular, we propose the integration of causal loop diagrams as a powerful visual tool to augment the conventional Systematic Literature Review process to provide a view at a glance of dynamics and capture the complex interdependencies occurring in a specific research domain. We carried out a systematic literature review by analyzing a set of 67 papers. We find nine relevant themes in the investigated research domain. Besides a descriptive picture of the scientific activity, a map of the main dynamics occurring in green food purchasing behavior has been provided.

Sustainable systematic literature reviews

Context:Systematic Literature Reviews (SLR) have been recognized as an important research method for summarizing evidence in Software Engineering (SE). At the same, SLR still presents several problems, such as the high resource consumption (mainly human resources) and lack of effective impact on SE practitioners, although much research has already been done. Objective:The main goal of this paper is to explore the concept of sustainability in the SLR area, intending to contribute to understanding better and solving such problems in an integrated way. More specifically, this paper characterizes what sustainable SLR are, their core characteristics, critical factors (i.e., sensitive points in the SLR process), and guidelines for conducting such SLR. Methods:We performed a meta-ethnographic study to find key concepts of sustainable software systems and transpose them to sustainable SLR. For this, we systematically selected 16 studies about sustainable software systems and 14 distinguished studies about SLR. Following, we extracted the main keywords and metaphors, determined how both areas are correlated, and transposed them to obtain a set of core characteristics of sustainable SLR as well as critical factors and guidelines. Additionally, we validated them with specialists using the Delphi method. Results:We found 15 core characteristics that offer a broad view of sustainable SLR, 15 critical factors in the SLR process that should be carefully addressed when conducting and updating SLR, and also 16 guidelines to manage SLR from the sustainability perspective. Conclusion:The concept of sustainability in SLR can contribute to solving SLR problems in a more integrated way, while this work could change the mindset of the SLR community about the need to conduct sustainable SLR.

Artificial intelligence for literature reviews: opportunities and challenges

This paper presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates prior research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research. We highlight three primary research challenges: integrating advanced AI solutions, such as large language models and knowledge graphs, improving usability, and developing a standardised evaluation framework. We also propose best practices to ensure more robust evaluations in terms of performance, usability, and transparency. Overall, this review offers a detailed overview of AI-enhanced SLR tools for researchers and practitioners, providing a foundation for the development of next-generation AI solutions in this field.

Internet behavioral models for improving internet quality of service or user profiling: a systematic literature review

Internet behavior models have found applications across diverse domains, notably in internet addiction, customer satisfaction analysis, user purchasing behavior prediction, and optimizing internet of things (IoT) sensor performance. However, a notable gap exists in exploring these models in enhancing internet quality of service (QoS), specifically in campus settings, intricately linked to the nuances of students' online behavior. This study elucidates the strategic utilization of internet behavioral models for augmenting internet QoS and facilitating user behavior analysis. Creating datasets grounded in internet users' access behavior represents a pivotal phase, with explicit, implicit, and mixed methods emerging as the prevailing approaches. In this comprehensive literature review, we systematically scrutinized the methods, techniques, and inherent characteristics of constructing internet behavior models according to a systematic literature review process. The qualitative findings extracted from the systematic review encapsulated 1,046 articles, meticulously classified according to predefined inclusion and exclusion criteria. Subsequently, 35 articles were judiciously selected for in-depth analysis. This study culminated in identifying the most pertinent methodologies and salient features pivotal to construct robust internet behavior model for improving internet QoS and user experience.

Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed

BackgroundThe demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice.MethodsIn November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies.ResultsFrom 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs.ConclusionsAutomation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.

Assessing competitiveness through intellectual capital research: a systematic literature review and agenda for future research

Purpose The purpose of this study is to undertake a systematic literature review (SLR) on intellectual capital disclosure (ICD), focusing on its role in fostering competitive advantage. Design/methodology/approach Following the SLR process, the study identified 84 papers published in high-ranking journals over a 19-year span, providing insights into descriptive outcomes, research limitations and future research directions. Findings The results show that ICD research peaked in 2022, with the Journal of Intellectual Capital leading with the highest number of ICD publications. Resource-based theory was found to be the most applied theoretical framework, with developed country-specific research receiving the most attention. The use of small sample size, a lack of longitudinal studies, reliance on a single source of data, unsuitability of control variables and a lack of comparative studies with firms operating in developing countries are the main limitations that have been noted. Research limitations/implications This study faces constraints, primarily stemming from the selective keyword utilization and exclusive Scopus database reliance. It omits non-English papers, conference proceedings and books, potentially overlooking relevant insights. Practical implications The findings offer valuable insight for researchers, emphasizing the need for research on intellectual capital (IC) across diverse industries. Furthermore, our findings urge regulators to mandate global IC reporting to mitigate information asymmetry, while also prompting managers to enhance IC-related practices and reporting for more stakeholders’ trust. Originality/value This study provides a comprehensive overview of over two decades of ICD literature, synthesizing previous studies, identifying gaps and outlining potential directions for scholars and industry professionals in the context of competitiveness.

Project-Based Learning on Critical Thinking Skills in Science Learning: Meta-analysis

The development of information technology and the environment constitutes one of the most difficult challenges facing the 20th century. Quality education plays a crucial and strategic role in building quality human resources. We need to teach critical thinking to today's students. Critical thinking skills can be improved if the project-based learning model is applied to science education. The research method used was a literature review of relevant articles for five years (2019-2023). The systematic literature review process involves three stages: article search, research article filtering and selection analysis. Using Cohen's formula, the researchers have estimated the effect size. In the light of this study, the practical application of the project based learning method, Project Based Learning, aims to improve critical thinking skills in science education. Twenty research articles show that critical thinking skills essential to science education can be improved using Project Based Learning instruction.

Applying a Systematic Literature Review Process in a Grounded Theory Investigation

This paper offers a critique of the application of a systematic literature review (SLR) process during a grounded theory (GT) investigation. The place of the literature review (LR) within GT has long been discussed, contested, and frequently misunderstood. One of GT’s hallmarks is its inductive nature, allowing salient concepts to ‘arise’ rather than being deduced. Hence, there are differing perspectives on the role of the LR concerning enabling or constraining this inductive process. However, scant guidance is available on conducting LRs for GT studies. Wolfswinkel, Furtmueller and Wilderom (2011) offer a five-stage GT method for reviewing literature. This paper will provide insights into the advantages and disadvantages of their method. Though grounded in theoretical principles surrounding the LR’s purpose in GT, this paper gives practical guidance on operationalising this five-stage SLR process. This is done by demonstrating the application of their stages in a doctoral dissertation. A contemporaneous research audit trail was used to document each stage, reflect on challenges, and justify deviations from the original five stages. Excerpts from the audit trail will be used to illustrate how the process was applied. Based on the findings from applying these stages, tentative advancements to the model will be offered so that other researchers will have more detailed guidance on how a SLR may be conducted within GT studies.

Pricing and performance of initial public offerings: directions for future research

Purpose This study aims to outline the research field of initial public offerings (IPOs) pricing and performance by combining bibliometric analysis with a systematic literature review process. Design/methodology/approach The study uses over three decades of IPO publication records (1989–2020) from Scopus and Web of Science databases. An analysis of keyword co-occurrence and bibliometric coupling was used to gain insights into the evolution of IPO literature. Findings The study categorized the IPO research field into four primary clusters: IPO pricing and short-run behaviour, IPO performance and influence of intermediaries, venture capital financing and top management and political affiliations and litigation risks. The results offer a framework for delineating research advancements at different stages of IPOs and illustrate the growing interest of researchers in IPOs in recent years. The study identified future research potential in the areas of corporate governance, earning management and investor sentiments related to IPO performance. Similarly, the study highlighted the opportunity to test multiple theoretical frameworks on alternative investment platforms (SME IPO platforms) operating under distinct regulatory environments. Originality/value To the best of the authors’ knowledge, this paper represents the first instance of using both bibliometric and systematic review to quantitatively and qualitatively review the articles published in the area of IPO pricing and performance from 1989 to 2020.