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Implementing green innovation is crucial for firms to build or sustain competitive advantages within the context of the sustainable development goals. Academic research has broadly explored how firms can induce green innovation behavior (GIB), examining external factors, but few studies in the current literature have deeply investigated unabsorbed slack as an internal antecedent of GIB. Drawing upon the behavioral theory of the firm and integrating it with dynamic capabilities theory, this study aims to address this deficiency by investigating the impact of unabsorbed slack on GIB and the mediating roles of two dimensions of capability reconfiguration: capability evolution and capability substitution in the relationship between unabsorbed slack and GIB. Furthermore, this study also discusses the moderating effects of consumer green pressure on the relationship between unabsorbed slack and capability evolution/substitution.

Survey data were collected from 286 Chinese technology-intensive manufacturing firms to empirically test the relationships among the variables.

The results reveal that unabsorbed slack has a positive influence on GIB. Furthermore, capability evolution and substitution both play mediating roles in the relationship between unabsorbed slack and GIB. Comparative analysis showed that the mediating effect of capability substitution is stronger than that of capability evolution. Moreover, consumer green pressure strengthens the positive relationship between unabsorbed slack and capability evolution/substitution.

This study enriches the research on the driving forces of GIB and contributes to providing managerial implications for firms to launch green innovation activities.

Management innovation is one of the most vital practices underpinning economic growth and is considered to be one of the fundamental components of gaining a superior business position in market conditions that are continually fluctuating. Drawing upon neo-institutional theory as well as absorptive capacity, the current empirical study unpacks the relationships between external institutional forces (i.e. regulative, mimetic and normative pressures) and management innovation through investigating the role of absorptive capacity as a mediator.

The research model is tested using survey data from Australian organisations. The study used Partial Least Squares (PLS), a component-based structural equation modelling (SEM) method, in order to perform the data analysis.

The results confirm that the various dimensions of institutional forces have diverse influences on management innovation. The authors found mimetic and normative pressures have positive influences on realised and potential absorptive capacity of an organisation. In addition, realised absorptive capacity mediates the relations between institutional forces and management innovation.

Dissimilar to preceding studies, this research shows that organisations not only innovate to pursue higher performance but sometimes strive for legitimacy. In addition, the significant associations between absorptive capacity and management innovation and the mediation role clearly signify that institutional forces and absorptive capacity play significant roles in the adoption of management innovation.

This study aims to investigate the intricate relationship between intellectual capital and environmental innovations among manufacturing medium and large firms in Uganda, utilizing the SmartPLS methodology.

This research adopts a cross-sectional and quantitative approach, collecting data through a questionnaire survey from a sample of manufacturing medium and large (ML) firms in Uganda. The collected data underwent analysis to identify patterns and relationships using the SmartPLS structural equation modeling (SEM) technique.

The findings highlight a distinct pattern: structural capital is the strongest predictor of environmental innovations, with human capital being the next most significant factor. However, the positive relationship with relational capital did not attain statistical significance, suggesting the need for further exploration into inter-firm relationships.

For managers, investing in robust organizational structures and human capital development programs can enhance firms’ capacity to drive eco-friendly initiatives, aligning with global sustainability agendas. Policymakers are encouraged to create an enabling environment that nurtures IC and incentivizes environmental innovation through supportive policies such as tax incentives and funding mechanisms for green technologies.

This study enriches the intellectual discourse on IC and environmental innovation by employing SmartPLS methodology to highlight the nuanced impact of its components, emphasizing the multifaceted nature of IC and its role in driving EI.

Military and unmanned aerial vehicles (UAV) applications like rocket motor casings, missile covers and ship hulls use components that are made of maraging steel. Maraging steel has properties that are superior to other metals, making it more suitable for the fabrication of such components. A grey relational analysis (GRA) that is based on the Taguchi method has been utilised in the current study to optimise a laser beam welding (LBW) process. Further aspects such as GRA's optimum ranges and percentage contributions were also estimated.

A Taguchi L16 orthogonal array is utilised to design and conduct the experiments. Laser power (LP), welding speed (WS) and focal position (FP) are the three parameters are chosen for the process of welding. The output responses are the upper width of the heat-affected zone (HAZup), the upper width of the fusion zone (FZup) and the depth of penetration (DOP). The effect of the above key parameters on the responses was examined using an analysis of variance (ANOVA).

The results of ANOVA reveal that the parameter that has the most influence on the overall grey relational grade (GRG) is the FP. Finally, metallographic characterisation and a microstructural analysis are conducted on the weld bead geometry to demarcate the zone of HAZ and fusion zone (FZ).

As the most important criteria for LBW of maraging steels is the provision of higher DOP, higher FZ width and lower heat-affected zone, the study intended to prove the applicability of GRA technique in solving multi-objective optimisation problems in applications like defence and unmanned systems.

This systematic review comprehensively examines corporate political contributions (CPC), exploring their antecedents, evolving mechanisms and diverse organizational outcomes. It offers a holistic understanding of the business–politics relationship and proposes a managerial decision-making framework for strategic CPC engagement. The study also identifies gaps in the literature and suggests future research avenues.

This study employs a systematic review process to assess the CPC literature. Utilizing leading journals and databases like Web of Science, Scopus and EBSCO, we apply rigorous screening criteria to select 72 relevant papers critically analyzed using the “Antecedents-Phenomenon-Consequences” framework.

The research identifies two primary dynamics influencing CPC: “essential need” for firm survival and “elective choice.” It reveals that CPC strategies impact various firm performance metrics, including market returns, operational performance and policy outcomes. Research is concentrated in the US, with a limited focus on developing economies. Future research should focus on industry-specific studies, timing of contributions and cross-national comparisons.

This paper provides managers with a comprehensive framework for CPC engagement, helping them navigate political dynamics, optimize contributions and enhance firm performance while maintaining ethical and strategic considerations.

This paper systematically reviews the complex political strategy of CPC, providing a nuanced understanding of how CPC operates across different countries and contexts. It offers academics and professionals insights to develop robust theories and make informed decisions in a modern, complex business environment.

The purpose of this paper spans twofold. Firstly, to investigate Human Resource Management practices (HRMP) adopted by organisations during the pandemic. Secondly, to bundle similar HRMP into Human Resource Management (HRM) bundles that provided unhindered organisational support to employees during the crisis.

The authors conducted 39 in-depth interviews across industries using a semi-structured interview schedule. Thereafter, the authors transcribed the interviews verbatim and analysed them thematically using MAXQDA 2021.

The study identifies effective practices during times of uncertainty and how soft HRM practices helped organisations survive during a crisis. When bundled together, these practices enabled organisations to continue operations during the pandemic, keeping their employees engaged and motivated.

Based on the learnings from the COVID-19 pandemic, the study provides a toolkit of HRMP bundles that organisations can adopt for future crisis management, enhancing the organisations’ absorptive capacity.

The study investigates the practices incorporated during COVID-19, leading to the identification of soft HRM bundles. The study adds value to the existing domain of HRM by including a unique set of soft HRMP bundles that have not been discussed in earlier studies and could be of high utility to organisations during the crisis.

This study aims to investigate whether country governance, regulated industry and firm-level characteristics, namely, ownership structure and firm size, are associated with the likelihood of firms having a politically connected board (PCB). This study also examines whether country governance and concentrated ownership moderates the association between institutional ownership and PCB.

This study uses cross-country analysis using 20 countries and hand-collected PCB data from 574 firms and 1,701 firm-year. This study performs logit regression analyses to examine hypotheses.

The results document that countries’ accountability, industry type and institutional ownership are associated with the likelihood of firms having a PCB. This study also finds that country governance, especially accountability, moderates the relationship between institutional ownership and PCBs. The results thus indicate the importance of country governance, especially accountability, in determining institutional investors’ political strategies.

This study provides several implications. First, firms tend to elect PCBs as a non-financial strategy because it arguably delivers additional resources and improves their performance, especially in countries with lower accountability and regulated industries. Meanwhile, investors and management must also hire PCBs cautiously because PCBs are closely related to agency issues. Agency issues reflect on the finding that institutional investors tend to avoid PCBs. However, the relationship between institutional investors and PCBs is closely related to the country-level context, especially accountability. This study also advises policymakers that country governance, especially accountability, is crucial in regulating the relationship between business and politics.

This study uses a relatively large number of new PCB and institutional ownership data collected manually from 20 countries. This study also examines several variables of country governance, such as accountability to PCB decisions that have not been tested before. This study examines the relationship between institutional ownership and PCB ownership decisions that were not examined before and uses a cross-country sample. In addition, to the best of the authors’ knowledge, this study is the first one that examines the role of state governance, especially accountability for the relationship between institutional ownership and PCBs.

This paper aims to deal with intra and inter-cell layout problems in cellular manufacturing systems. The model is organized to minimize the total handling cost, i.e. intra and inter-cell handling costs in a continuous environment.

The research was conducted by developing a mixed integer mathematical model. Due to the complexity and NP-hard nature of the cellular manufacturing layout problem, which mostly originated from binary variables, a “graph-pair” representation is used for every machine set and cells each of which manipulates the relative locations of the machines and cells both in left-right and below-up direction. This approach results in a linear model as the binary variables are eliminated and the relative locations of the machines and cells are determined. Moreover, a genetic algorithm as an efficient meta-heuristic algorithm is embedded in the resulting linear programming model after graph-pair construction.

Various numerical examples in both small and large sizes are implemented to verify the efficiency of the linear programming embedded genetic algorithm.

Considering the machine and cell layout problem simultaneously within the shop floor under a static environment enabled managers to use this concept to develop the models with high efficiency.

The objective of this work is twofold: firstly, to develop a model for assessing healthcare service quality (HSQ), and secondly, to evaluate the effectiveness of machine learning algorithms in predicting the quality of healthcare services.

In this study, a comprehensive literature review has been performed to identify key quality dimensions in the healthcare services domain. Delphi’s method has been used to confirm the criticality of these dimensions based on experts’ opinions and proposed a novel CIRMQUAL model. Factor analysis techniques have been used to further validate the CIRMQUAL model. Using the data collected through a questionnaire survey, a number of machine learning models have been developed to predict the customer satisfaction level based on the service quality (SQ) performance of a healthcare unit on different dimensions of the CIRMQUAL model.

The study developed a CIRMQUAL model with 14 dimensions (quality of care, safety and security, skill and conduct, staff attitude, tangibles, quality of the atmosphere, patient rights, follow-up, communication, cost of treatment, availability of resources, accessibility, waiting time and services), and these dimensions have been clubbed into four major dimensions, i.e. clinical quality, infrastructural quality, relationship and managerial quality. Furthermore, the application of machine learning algorithms has demonstrated significant accuracy in predicting SQ, highlighting its ability to improve healthcare services and the satisfaction level of patients.

Managers of healthcare units work hard to identify and address the pain points of the patients and improve the working of the healthcare units being managed by them. The availability of many scales with numerous dimensions adds to their confusion in selecting a suitable scale. The current work addresses this confusion and provides four clear areas for assessing the quality of healthcare units. By using this scale, managers can assess the quality of services provided by them, identify the dimensions of low performance, plan and take suitable corrective actions to improve the performance of their healthcare units.

A comprehensive SQ model, i.e. CIRMQUAL has been proposed as a new scale to assess SQ in healthcare units. The model has been developed after analyzing the dimensions used by many researchers available in the literature. This model can be used by future researchers to assess the SQ in healthcare units. Moreover, an attempt has been made to use artificial intelligence-based techniques for predicting customer satisfaction. Such attempts are in the initial stage for healthcare sector. Future researchers can take this concept forward and test the applicability of different machine learning techniques in different functional areas of healthcare.

Good health is of utmost importance for all human beings. In spite of the expenditure of substantial time and efforts by various stakeholders, the service delivery doesn’t match the expectations of patients. Many times, the service providers are not aware of this dissatisfaction and specific aspects of service delivery that need to be improved to reduce dissatisfaction. The model proposed will help the service providers in this regard and the service providers will be able to take focused steps. Such initiatives will definitely improve patient’s satisfaction and their social well-being.

This work is unique because it uses a novel technique to redefine the quality of services in healthcare by using a dual methodology. The research presents a model that includes various factors and it is specially developed to evaluate the quality of services in healthcare settings. This study advances the area’s progress by implementing computational tools for accurate evaluation of HSQ. The healthcare decision-makers may use this novel perspective to evaluate and enhance the quality of service.