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Considering the increasing interest devoted to knowledge hiding in the workplace and academic research, the aim of this study is to analyze the existing literature on knowledge hiding to understand and trace how it has evolved over time and to uncover emerging areas for future research.

The study used performance analysis and science mapping to analyze a sample of 243 studies published between 2005 and 2022. The study focused on analyzing the scientific productivity of articles, themes and authors.

The results of performance and science mapping analysis indicate that the concept of knowledge hiding behavior evolved recently and a majority of the studies have been conducted in the past decade. The study found that knowledge hiding is still in its infancy and has been studied in relation to other themes such as knowledge sharing, knowledge management, knowledge withholding and knowledge transfer. The study identified emerging themes, productive authors and countries, affiliations, collaboration network of authors, countries and institutions and co-occurrence of keywords.

Compared to the recent developments in the knowledge hiding behavior, the present study is more comprehensive in terms of the methods and databases used. The results of the study contribute to the existing literature on knowledge hiding and knowledge withholding.

In recent years, counterproductive knowledge behavior (CKB) and its types have received increasing interest in knowledge management as the degree of knowledge sharing and innovation in enterprises continues to increase. A rapidly growing number of studies have shed light on the important antecedents and consequences of employees’ CKB. However, the various labels, conceptualizations and operationalizations of CKB have fragmented this body of research. This study aims to systematically integrate the effects of the six types of organizational characteristics on CKB and further draws more general conclusions based on the results of previous studies.

Based on a survey of 103 effect values responsible for 52 CKB samples, the authors use the ABC theory to explore the effects of the six types of organizational characteristics on CKB. Moderator analysis were performed to resolve inconsistencies in empirical studies and understand the contexts under which CKB has the strongest or weakest effect.

The results showed that task interdependence and a positive organizational atmosphere, in general, negatively affect employees’ CKB in the moderation analysis. In contrast, workplace discomfort, negative organizational atmosphere, internal competition and time pressure positively and partly affect employees’ CKB. The direction and magnitude of these effects were affected by emotional factors, knowledge personnel types and sample sources. Discussing the theoretical, methodological and practical implications of these findings can offer a guiding framework for future research.

Better control of employees’ CKB is not achieved by adjusting organizational characteristics alone but by combining personal characteristics and mood changes with it to balance organizational characteristics and CKB. Furthermore, the large-sample joint study integrated the conceptual definition of CKB. The multivariate data study provided more reliable conclusions and a solid theoretical foundation for CKB research areas.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

This study examines the effects of financial literacy and financial risk tolerance on investor behavior by introducing social stigma as a mediator and emotional intelligence as a moderating factor.

Data is collected from 761 financially independent individual investors, with a minimum age of 25 years, a minimum of five years of stock market experience and residing in five selected major Indian cities. The collected data is subsequently analyzed using SmartPLS. Homogeneous purposive sampling followed by snowball sampling was employed.

The findings of the study demonstrate a strong and noteworthy impact of financial literacy on investor behavior. The research reveals that social stigma acts as a partial mediator and emotional intelligence plays a significant moderator with direct effects and indirect effects between financial literacy, financial risk tolerance, social stigma and investor behavior.

Exploring emotional intelligence in financial decisions enriches academic programs by integrating it into financial education. Collaboration between academia and financial institutions yields practical tools, infusing emotional intelligence into services. This prompts systemic shifts, reshaping education and societal discourse, fostering inclusive, emotionally intelligent financial landscapes, aiming to redefine both academic teachings and real-world financial practices.

Integrating emotional intelligence into government-led financial literacy programs can transform societal perspectives on financial decision-making. Customized services, destigmatizing workshops and collaborative efforts with academia foster an emotionally intelligent financial landscape, reshaping traditional paradigms.

Promoting open societal discussions about finances combats stigma, fostering a supportive space for risk-taking. Emphasizing emotional intelligence in awareness campaigns cultivates inclusivity and confidence. Normalizing financial talks empowers individuals, enhancing their well-being. Elevating both financial literacy and emotional intelligence enhances overall financial health, nurturing a community adept at navigating financial journeys.

This study marks a notable contribution to behavioral finance and social stigma theory by examining their intersection with emotional intelligence. It uniquely introduces social stigma as a mediator and emotional intelligence as a moderator, unexplored in this context. This novelty underscores the research’s significance, offering practical insights into financial well-being.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2023-0626

The study provides a comprehensive understanding of the issues and illegal activities related to cryptocurrencies and their negative repercussions. This study aims to identify and classify cryptocurrency downsides using grounded theory and in-depth interviews. The study also analysed investors’ reluctance to invest in cryptocurrency. This pioneering qualitative study illuminates a deep and multifaceted criminal aspect of cryptocurrency.

The study conducted in-depth interviews with respondents who have experience and knowledge of cryptocurrency investments. The interviews were recorded and transcribed. The analysis was performed using the NVivo 14 software in the study.

The study specified two major types of cryptocurrency’s negative aspects: barriers and illegal usage. Barriers to cryptocurrency investment include technological, security, trust, market-related and regulatory reasons. Terrorist funding, money laundering, fraud and ransom payments are all examples of illegal usage. The results of the word cloud analysis are consistent with the overall findings of the survey, which highlighted illegal usage as a prominent negative element of cryptocurrencies. It is a key reason why cryptocurrency is not included in investing portfolios by investors.

The study’s findings provide useful insights for policymakers to develop better methods for successfully mitigating risks and ensuring responsible and sustainable usage of cryptocurrencies. In addition, the study could serve as a stepping stone for more cryptocurrency-related studies, contributing to the development of a more complete and nuanced comprehension of this emergent technology and its societal effects.

The manufacturing industry has recently faced many environmental issues and pressures due to increased environmental awareness. Business leaders identified that green innovation (GI) is a crucial aspect of sustainable development (SD) and can provide a competitive edge. On the other hand, boosting SD through GI is a complicated network activity involving various partners, and there is a need to transmit or share knowledge equitably and acceptably. Following the natural resource-based view (NRBV), this research presents a novel framework for investigating how green knowledge sharing (GKS) and green dynamic capabilities (GDC) affect green creativity (GC) and GI directly and through GC. Besides, the moderation of mimetic pressure (MP) was also evaluated in these targeted relations, as it is a critical element for enhancing GI in the context of SD.

Data consisted 232 respondents from Pakistani manufacturing industries, and seven substantial hypotheses were verified regarding the direct, mediating and moderating effect of targeted variables in confounding ways using partial least squares structural equation modeling (PLS-SEM).

According to the empirical findings, GKS and GDC positively correlate with GC and GI. Moreover, GKS and GDC significantly and positively impact GI both directly and indirectly. Furthermore, GC partially mediates GI, whereas MP has a significant moderating effect in these relationships.

A significant correlation among these constructs was observed, demonstrating the conceptual framework for this study. These findings can assist practitioners and academics gain a better understanding of how to manage and evaluate GI in manufacturing projects at both the project and firm levels. Therefore, policymakers and project managers can enhance GI by focusing more on GKS, GDC, MP and GC.

Abusive supervision (AS) provides insights into the darker aspects of leadership behavior and its effects on employees. Understanding and addressing AS can contribute to creating healthier work environments and promoting employee well-being. The effect of abusive leadership (AS) on counterproductive work behaviors (CWB) in nursing staff is examined through the theoretical lens of the social exchange theory.

Data were collected from 302 nursing staff working at public and private hospitals through a self-administered questionnaire. Measurement scales were adapted from the literature and the data were tested for validity and reliability before performing hypotheses testing through structural equation modeling in SmartPLS 4.0.

AS positively affects CWB, and psychological contract breach mediates this relationship. However, employees with high Islamic work ethics (IWE) are less concerned with supervisors' dysfunctional behaviors and pay less attention to them; thus, IWE buffers the effect of AS on CWBs.

A positive and supportive organizational climate is crucial for attracting and retaining skilled healthcare professionals. When healthcare professionals are subjected to abusive behaviors, their ability to share knowledge, adopt safety protocols and provide the best patient care may be hampered. Therefore, addressing AS in hospitals is vital to promoting a positive work environment, enhancing employee well-being and improving patient care.

Increasing focus on socially responsible investments (SRIs) and green projects in recent times, coupled with the arrival of COVID pandemic, are the main drivers of this study. The authors conduct a post-factum analysis of investor choice between sin and green investments before and through the COVID outbreak.

A passive investor is introduced who seeks maximum risk-adjusted return and/or investment variance. When presented an opportunity to add sin and/or green investments to her initial one-asset market-only investment position, she views and handles this issue as a portfolio problem (MPT). She estimates value-at-risk (VaR) and conditional-value-at-risk (CVaR) for portfolios to account for downside risk.

Green investments offer better overall risk-return optimization in spite of major inter-period differences in return-risk dynamics and substantial downside risk. Portfolios optimized for minimum variance perform just as well as the ones optimized for minimum downside risk. Return and risk have settled at higher levels since the onset of COVID, resulting in shifting the efficient frontier towards north-east in the return-risk space.

The study contributes to the literature in two ways: One, it examines investor choice between sin and green investments during a global health emergency and views this choice against the one made during normal times. Two, instead of using the principles of modern portfolio theory (MPT) explicitly for diversification, the study uses them to identify investor preference for one over the other investment type. This has not been widely done thus far.

This paper aims to examine the state of research on environmental, social and governance (ESG) performance in the context of multinational business research. This paper discusses research progress as well as various issues and complexities associated with using ESG ratings in cross-country studies and for assessing the performance of multinational enterprises (MNE) and emerging market multinationals (EMNEs).

The paper identifies emerging literature that focuses on tracking the development and uptake of ESG ratings in the international context. It discusses three emerging research streams: Research examining the ESG-financial performance relationship in emerging markets, research tracking the ESG performance of multinationals in the various countries and regions they are operating, and frameworks for assessing ESG-related risks on a country level.

While the emerging body of work adds an important dimension to the identification and awareness of ESG issues globally, numerous unresolved issues become evident. ESG frameworks have been built to assess corporate sustainability as it relates to firms in their “home” countries (typically with a focus on developed countries), with limited applicability and transferability to emerging markets. International firm activities are often not captured in detail and not comprehensively mapped across firm subsidiaries and a firm’s corporate supply chain where ESG issues are prone to happen, and ESG scores do not comprehensively integrate views and voices from various local stakeholders that are impacted by firm activities, particularly indigenous communities.

Research on ESG ratings in the context of multinational business research is generally sparse and fragmented, thus creating opportunities for future research to expand on existing and emerging findings.

The paper creates awareness of issues to consider when using ESG ratings in cross-country studies and for assessing the ESG performance of MNEs and EMNEs: ESG scores can be subject to bias and are not weighted by materiality, which can be misleading for portfolio construction and performance measurement purposes. Managers need to be aware that ESG scores are often not capturing ESG issues occurring in supply chains and ESG issues affecting local communities.

This study enriches the understanding of ESG in the context of multinational business research practice.

Green innovation (GI) in megaprojects has become a significant research topic that attracts both megaproject management scholars' and practitioners' attention. Green transformational leadership (GTL) is acknowledged as an important antecedent to GI in the permanent context. However, limited research investigates the mechanism and condition of how GTL effectively affects GI in the temporary (i.e. megaproject) context. This study seeks to examine the mechanism and condition of GTL in improving GI by assessing the mediating role of green knowledge sharing (GKS) and the moderating effect of innovation climate (IC).

Regression analysis was performed on data obtained from 303 experts who have been involved in megaprojects.

GTL has a significant positive impact on two aspects of GI, including green product innovation (GPDI) and green process innovation (GPCI). Besides, GKS mediates the relationship between GTL and the two aspects of GI. Moreover, IC plays a significantly positive moderating role in the relationship between GTL and GKS and the relationship between GKS and the two aspects of GI.

This study adds knowledge to the theory and practice by unveiling the “black box” between GTL and GI in the temporary (i.e. megaproject) context. First, this study extends the continuing discussion on the direct effect of GTL on GI to the temporary (i.e. megaproject) context. Second, this study facilitates the understanding of the mechanism to generate better GI performance considering the mediating role of GKS and the moderating effect of IC in the temporary (i.e. megaproject) context. The results can illuminate megaproject practitioners on generating better GI performance.