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This study examines how the presence of labor unions affects a firm’s pay disparity between executives and employees and its financial statement comparability.

It uses firm-level labor union data in Korea and applies regression analyses to a sample of 1,776 firm-year observations from 2004 to 2008.

The authors find that unionized firms have a smaller pay disparity between executives and employees than non-unionized firms, suggesting that labor unions place pressure on the pay structure. Unionization also lowers financial statement comparability, which helps managers of unionized firms maintain information asymmetry. Further, this negative relationship between unionization and financial statement comparability is stronger in non-chaebol firms, implying that they are more motivated than chaebol firms to reduce their financial statement comparability in response to the presence of labor unions. In addition, the negative relationship between unionization and financial statement comparability is pronounced in profit-making firms, firms with less analyst following, firms with fewer foreign investors and firms in more competitive product markets.

The finding that firms adjust comparability in response to labor unions interests regulators and policymakers, who emphasize the role of comparability in providing usefulness to information users.

The findings add to the existing literature on the effect of labor unions on firms' pay structures and accounting choices.

While sustainable growth extends the use of resources, it is crucial to explore green growth (GG) that ensures growth sustainability through the adoption of renewable energy. Thus, this study is motivated to investigate the influence of renewable energy on GG in 19 emerging countries spanning a decade and a half (2000–2020). This study aims to provide a quantitative examination of how renewable energy contributes to sustainable economic growth.

This study uses advanced dynamic common correlated effect techniques to assess the long-term effectiveness of renewable energy on GG. Additionally, it uses Dumitrescu and Hurlin causality tests to identify synchronicity between the respective variables.

The findings of this study reveal that the adoption and utilisation of renewable energy effectively promote GG in emerging economies. However, in contrast, the significantly greater negative influence of trade openness on GG compared to renewable energy highlights the inadequacy and limited impact of cleaner energy alone.

To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on GG. To the best of the authors’ knowledge, this study would be the first to analyse this relationship in these emerging countries. Furthermore, previous estimation frameworks used in prior studies often overlook the crucial factor of cross-sectional dependence (CSD) among countries. Therefore, this study addresses this issue using a contemporary econometric approach that deals not only with CSD but other biases, like endogeneity, autocorrelation, small sample bias, etc.

This research uses a multifaceted approach to develop an MPA/MPP curriculum to support a data science track within the existing MPA/MPP programs by identifying the core and elective areas needed.

The approach includes (1) identifying a suitable structure for MPA/MPP programs which can allow the program to develop its capacity to train students with the data science and general public administration skills to solve public policy problems and leave explicit space for local experimentation and modification; (2) defining bridging modules and required modules for the MPA/MPP programs; and (3) developing of data science track thought to make suggestions for the inclusion of suitable data science modules into the data science track and benchmarking the data science modules suggested with the best practices developed by other professional bodies. The authors review 46 NASPAA-accredited MPA/MPP programs from 40 (or 22.7%) schools to identify the suitable required modules and some potential data science and analytics courses that MPA/MPP programs currently provide as electives.

The proposal includes a three-course (six–nine credits, not counted in the program but as prerequisites) bridging module, a nine-course (27 credits) required module and a five-course (15 credits) data science track/concentration.

This work can provide a starting point for the public administration education community to develop graduate programs focusing on data science to cater to the needs of both public managers and society at large.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

With the rapid development of social media, many organizations have begun to attach importance to social media platforms. This research studies the management and the use of social media in academic music libraries, taking the Center for Chinese Music Studies of the Chinese University of Hong Kong (CCMS) as a case study.

We conducted a sentiment analysis of posts on Facebook’s public page to analyze the reaction to the posts with some exploratory analysis, including the communication trend and relevant factors that affect user interaction.

Our results show that the Facebook channel for the library has a good publicity effect and active interaction, but the number of posts and interactions has a downward trend. Therefore, the library needs to pay more attention to the management of the Facebook channel and take adequate measures to improve the quality of posts to increase interaction.

Few studies have analyzed existing data directly collected from social media by programming based on sentiment analysis and natural language processing technology to explore potential methods to promote music libraries, especially in East Asia, and about traditional music.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

The authors revisit the traditional OLI paradigm with the objective to allocate politics within the set of internationalization advantages by building on the political strategy literature. The authors outline the specific role of political advantage that facilitates and propels the international expansion of state-owned multinational enterprises (SOMNEs) from emerging markets.

A conceptual paper which explains the role of political advantage in the internationalization of SOMNEs. The authors expand the scope of the OLI to capture the impact of firms' home governments' policies and relationships with host countries which are leveraged by SOMNEs in their internationalization.

The authors define political advantage as a new type of advantage which depends on and is sourced from external actors. The authors argue that P-advantage is a multifaceted and unstable part of POLI composition, which is contingent on political shifts and may be leveraged by various firms. The authors also assert that political capabilities have limitations in sustaining political advantage, which may be compensated via enhancing the political activity of firms.

The authors conceptualize the POLI-advantages paradigm for the internationalization of SOMNEs by proposing that in addition to the traditional ownership, location, and internalization advantages, firms can capitalize on their political advantage to enter markets where internationalization might have been difficult without their political connections.

The purpose of this study is to examine the effect of the coopetition strategy (CS) (the simultaneous pursuit of collaboration and competition) on sustainable performance (SP) through the serial mediation of knowledge sharing (KS) and open innovation (OI).

A structured questionnaire was used to gather data from corporate business enterprises, and partial least squares structural equation modeling was used for analysis.

Empirical evidence supports the coopetition strategy's role in enhancing KS, which in turn fosters OI, leading to improved SP. It has also been concluded that KS and OI have a significant serial mediation effect on the relationship between CS and SP.

Through the integration of KS and inward-outward open innovation, the coopetition model enables coopetitors leverage each other’s resources and capacities for mutual sustainability. To fully benefit from it, small and medium-sized enterprises (SMEs) in the Gulf Cooperation Council (GCC) must change their perception of free competition and actively engage in coopetition activities, particularly in the realms of knowledge and OI.

The most novel contribution of this study to the growing body of knowledge on SP is the establishment of empirical evidence regarding the crucial role of a serial mediation of KS and OI in the relationship between CS and SP. Unlike earlier research, this study provides a structured perspective and understanding of how and why CS, KS and OI were leveraged to enhance the SP of SMEs.

Within the context of an open innovation business environment, the frequent interaction and coordination activities among heterogeneous partners have a significant impact on enterprises' business model. Nevertheless, fewer empirical research has been made to explore how to match external partners and update organizational dynamic capabilities at an ecosystem level. Therefore, this paper attempts not only to investigate the direct impact of partner match on different business model innovation (BMI) themes (efficiency-centered BMI and novelty-centered BMI) but only to shed light on the pivotal mediating role of interfirm dynamic capabilities.

This paper utilized the methodology of Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the impact of diverse partner selection criteria and interfirm dynamic capabilities on two distinctive themes of BMI. More than 20 industry clusters with multiple industries were selected as representatives of the creative ecosystem, predominantly from the Yangtze River Delta region. Valid data were collected from 254 managers by both online questionnaires and offline interviews.

The findings of the study show that different partner match criteria have distinct direct impacts on BMI themes. Partner complementary and partner synergy, deriving from the “task-related criteria”, are significantly correlated with both EBMI and NBMI. Conversely, partner compatibility, deriving from “Partnering-related Criteria”, shows a positive correlation with EBMI but not NBMI. Furthermore, compare the indirect effect on EBMI, the paper’ results demonstrate interfirm dynamic capabilities as mediator can more maximize external benefits to promote NBMI.

The study findings effectively help enterprises implement different BMI themes. From a management perspective, whether pursuing EBMI or NBMI, enterprises should consciously seek partners who can provide complementary support or share mutual goals across diverse industries. This strategic approach can significantly enhance the opportunities for sustainable and innovative business development. Furthermore, to successfully accomplish NBMI, enterprises must cultivate interfirm dynamic capabilities encompassing a comprehensive range of cross-organizational innovation capacities, such as bolstering organizational learning capability, establishing interactive network platforms to enhance coordination capabilities and engaging in integrative activities to foster a collective mindset.

This paper contributes to the match theory by introducing three critical matching criteria, enabling enterprises to discern partners based on diverse organizational characteristics. Additionally, this paper broadens the scope of the dynamic capability literature by adopting a network perspective to strengthen interaction and relationship mechanisms. The authors primarily elucidate the concept of interfirm dynamic capabilities as a formative higher-order model formed by three sub-capabilities (absorptive capacity, coordination capability and collective mind). Finally, this paper combines matching theory with dynamic capacity theory to the field of BMI, which adds depth and complexity to the existing ecosystem innovation research.

Urban communities can be faced with many destructive events that can disrupt the daily functioning of activities and livelihood of people living in the communities. In this regard, during the last couple of years, many governments have put a lot of efforts into building resilient urban communities. Essentially, a resilient urban community has the capacity to anticipate future disasters, prepare for and recover timely from adverse effects of disasters and unexpected circumstances. Considering this, it is therefore important for the need to continuously review the existing urban community resilience indicators, in order to identify emerging ones to enable comprehensive evaluation of urban communities in the future against unexpected events. This study therefore aims to conduct a systematic review to develop and critically analyse the emerging and leading urban community resilience indicators.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRSIMA) protocol, 53 journal articles were selected using Scopus. The selected papers were subjected to thorough content analysis.

From the review, 45 urban community resilience indicators were identified. These indicators were grouped into eight broad categories namely, Socio-demographic, Economic, Institutional Resilience, Infrastructure and Housing Resilience, Collaboration, Community Capital, Risk Data Accumulation and Geographical and Spatial characteristics of community. Further, the results indicated that the U.S had the highest number of publications, followed by Australia, China, New Zealand and Taiwan. In fact, very few studies emanated from developing economies.

The outputs of this study will inform policymakers, practitioners and researchers on the new and emerging indicators that should be considered when evaluating the resilience level of urban communities. The findings will also serve as a theoretical foundation for further detailed empirical investigation.