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The Penrose effect, or the limitation of the growth rate during expansion due to managerial constraints, has been examined in the context of business diversification, withdrawal and MNE’s foreign direct investment, primarily in the for-profit sector. However, insufficient attention has been paid to its impact on professional service firms, particularly in the context of their expansion through service exports. The main purpose of this study is to examine the Penrose effect in the internationalization of professional service firms through service exports.

This study focuses on large Japanese patent firms as traditional professional service firms and constructs panel data for 48 large patent firms over the observation period from 2002 to 2010 to test our hypotheses.

Our results demonstrate a negative relationship between degree of internationalization and international business growth, thus confirming the Penrose effect. Furthermore, we found that the degree of internationalization has a curvilinear relationship with international business growth and that institutional distance does not have a negatively moderating effect on the relationship between the degree of internationalization and international business growth.

This study made a theoretical contribution to Penrose's growth theory and previous studies on international management and professional service firms and international management by showing that the Penrose effect can be observed in the international expansion of professional service firms through service exports. Moreover, this study identifies the factors that modify the Penrose effect, thereby making a significant theoretical contribution.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

This paper analyses the importance of leadership and culture in combating corruption in Hong Kong SAR, Japan, Malaysia, New Zealand, Singapore and Taiwan.

This paper is based on the comparative analysis of the effectiveness of the anti-corruption measures in the studies of six selected countries/regions in this special issue of Public Administration and Policy. The contributors in this special issue were invited because of their publications on combating corruption in the six countries/regions.

The critical variable ensuring the effectiveness of combating corruption is the strong political will of the leadership in changing the culture of corruption in the country/region by implementing a zero-tolerance policy toward corruption, as shown in Singapore and Hong Kong. In New Zealand’s case, leadership plays a less important role because of the population’s emphasis on equality and egalitarianism and its reliance on the Ombudsman and Serious Fraud Office to curb corruption. However, the corrupt leadership of Tanaka Kakuei in Japan, Najib Rajak in Malaysia, and Chen Shui-bian in Taiwan, demonstrates clearly their insidious impact of consolidating their kleptocratic rule in these countries/regions.

As the role of leadership and culture in combating corruption has not been given sufficient attention in the literature, this paper attempts to rectify this neglect by demonstrating that the political leaders in Singapore and Hong Kong, and to a lesser extent, New Zealand, have succeeded in minimising corruption while their counterparts in Japan, Taiwan and Malaysia, have failed to do so.

This paper aims to analyze how decision support systems manage Big data to obtain value.

A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.

The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.

As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

This study aims to investigate the impact of digital leadership (capabilities, experience, predictability and vision) and green organizational culture on firms' digital transformation and financial performance. Additionally, the research aims to evaluate the mediating role of digital transformation in the relationship between digital leadership and firms' financial performance.

A purposive sampling technique was employed to identify and select individuals with relevant expertise and experiences in the field of digital transformation. A total of 164 responses were collected, and the questionnaire was designed based on a five-point Likert-type scale. The data were analyzed using SmartPLS 4 (Statistical Software for Structural Equation Modeling).

The findings indicate that digital leadership capabilities, experience, predictability and vision do not directly impact firms' performance. However, there is an indirect influence on firms' performance through digital transformation. While both digital transformation and green organizational culture (GOC) positively influence firms' financial performance, GOC, leader predictability and leader vision positively influence digital transformation. The results confirm that digital transformation mediates the relationship between capabilities, experience, predictability and vision and firms' financial performance.

The study highlights that strategic capabilities can enhance value-added processes during digital transformation, contributing to sustainability in the digital era. Overall, this research significantly advances both theoretical understanding and practical applications in the context of digital leadership and its impact on firms. Limited digital transformation stages among Malaysian firms impact the research, with some entities cautious about data disclosure and having limited cooperation with researchers. Gathering data from diverse sources would have strengthened the findings and methodological rigor of this multilevel study. Despite these limitations, the research offers fresh insights into the role of GOC, different facets of digital leadership and their influence on digital transformation and financial performance. This enhances existing knowledge and challenges assumptions of the transformational leadership theory (TLT) framework.

The study opens the door to further research into distinct leadership components and their effects in a similar context. By highlighting the positive influence of capabilities, experience, predictability and vision on digital transformation, it expands the theoretical and empirical scope in the realm of digital leadership. These findings encourage critical examination, refinement and evolution of TLT, providing insights for leaders and managers as they navigate digitalization, financial performance and digital leadership within organizations. In an era of digital transformation, leaders play a central role in building a psychologically safe environment and nurturing digitally skilled teams capable of managing technological changes. Leaders should possess the digital capabilities, experience, vision and predictability necessary to drive digital transformation, mitigate potential threats and adapt to the dynamic digital landscape.

These findings support government initiatives to accelerate digitalization and Industry 4.0 implementation. Collaboration between the government and private organizations is essential to create policies and practices that facilitate broad participation in digital transformation programs. Policymakers must adopt a proactive approach to address issues related to Internet accessibility, trade barriers, financing access and resource reallocation. These policies aim to ensure a high-quality and affordable digital infrastructure, cultivate trust in digital technologies and equip organizational leaders with the necessary digital skills.

This research provides valuable insights for practitioners to enhance firms' digital transformation. As a practical contribution, this study’s findings can inform how firms can better manage their key digital leadership resources and GOC to foster digital transformation and improve their financial performance.

Intuitionistic linguistic fuzzy information (ILFI), characterized by linguistic terms and intuitionistic fuzzy sets (IFSs), can easily express the fuzzy information in the process of muticriteria decision making (MCDM) and muticriteria group decision making (MCGDM) problems. The purpose of this paper is to provide an overview of aggregation operators (AOs) and applications of ILFI.

First, some meaningful AOs for ILFI are summarized, and some extended MCDM approaches for intuitionistic uncertain linguistic variables (IULVs), such as extended TOPSIS, extended TODIM, extended VIKOR, are discussed. Then, the authors summarize and analyze the applications about the AOs of IULVs.

IULVs, characterized by linguistic terms and IFSs, can more detailed and comprehensively express the criteria values in the process of MCDM and MCGDM. Therefore, lots of researchers pay more and more attention to the MCDM or MCGDM methods with IULVs.

The authors summarize and analyze the applications about the AOs of IULVs Finally, the authors point out some possible directions for future research.

Continuous understanding of the best practices associated with new product development is a constant research opportunity to advance knowledge in the field, as far as changes in the business environment and the increasing turbulence level in different market segments create and reposition the importance of practices over time.

Based on a systematic review, the study aimed to analyze the 100 most relevant articles published in international journals on new product development (NDP), identifying new patterns on the best practices for new product development and the types of relationship involved in NPD.

Among the several practices observed in the literature, the analysis point to a larger group of studies that converge on the identification of a positive and significant relationship in integration – simultaneously – between supplier, company, customers and strategic alliances and the performance of NPD.

These results support integration as a cross-cutting and structural best practice for NPD, as long as it is constituted as a capacity, mainly applied in highly turbulent environments. This approach supported the proposition of a new framework.

Organizations will be able to implement the proposed framework to NPD strategy in order to prioritize resources in best practices, aiming to increase the performance of new product development.

The adoption of integration and co-creation practices for the development of new products expands the possibilities of economic and social development, based on the involvement of the actors in this network.

This model had not yet been proposed in the literature, filling a gap in the agenda for future studies.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

There is a gap in knowledge about the Gulf Cooperation Council (GCC) because most studies are undertaken in countries outside the Gulf region – such as China, India, the US and Taiwan. The stock market contains rich, valuable and considerable data, and these data need careful analysis for good decisions to be made that can lead to increases in the efficiency of a business. Data mining techniques offer data processing tools and applications used to enhance decision-maker decisions. This study aims to predict the Kuwait stock market by applying big data mining.

The methodology used is quantitative techniques, which are mathematical and statistical models that describe a various array of the relationships of variables. Quantitative methods used to predict the direction of the stock market returns by using four techniques were implemented: logistic regression, decision trees, support vector machine and random forest.

The results are all variables statistically significant at the 5% level except gold price and oil price. Also, the variables that do not have an influence on the direction of the rate of return of Boursa Kuwait are money supply and gold price, unlike the Kuwait index, which has the highest coefficient. Furthermore, the height score of the variable that affects the direction of the rate of return is the firms, and the accuracy of the overall performance of the four models is nearly 50%.

Some of the limitations identified for this study are as follows: (1) location limitation: Kuwait Stock Exchange; (2) time limitation: the amount of time available to accomplish the study, where the period was completed within the academic year 2019-2020 and the academic year 2020-2021. During 2020, the coronavirus pandemic (COVID-19), which was a major obstacle, occurred during data collection and analysis; (3) data limitation: The Kuwait Stock Exchange data were collected from May 2019 to March 2020, while the factors affecting the stock exchange data were collected in July 2020 due to the corona pandemic.

The study used new titles, variables and techniques such as using data mining to predict the Kuwait stock market. There are no adequate studies that predict the stock market by data mining in the GCC, especially in Kuwait. There is a gap in knowledge in the GCC as most studies are in foreign countries, such as China, India, the US and Taiwan.

Based on the theory of planned behavior (TPB) and stakeholder theory, the model proposes that responsible leadership (RL) is mediated by affective commitment (AC) on both outcome variables (organizational citizenship behavior [OCB] and patient satisfaction [PS]) while distributive justice (DJ) moderates the relationship among RL, OCB and PS through the mediator of AC.

Overall, data collected from 275 employees and patients in India’s healthcare sector support this model both in online and offline mode. SPSS 25, AMOS 22 and PROCESS macro were used to analyze the data.

The influence of RL, OCB and PS was seen insignificant in the Indian healthcare sector. This study examines the role of AC as a mediator which does not affect extra-role behavior and PS. The findings also show that the moderation-mediation effect of DJ through AC strengthened the link between RL and OCB, but not PS. Commitment does not affect extra-role behavior and PS.

Until now, there has been no research in the Indian context that has tested the effect of RL on extra-role behaviors and PS, as mediated by AC, according to researchers’ knowledge. Since RL and outcome variables are related through AC, the current study aims to understand how DJ acts as a moderator to that relationship.