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This study aims to identify the strategic factors and their effects on the post-cross-border acquisitions (CBA) technological innovation performance of the acquiring firms. It develops a hierarchical model to examine the interrelationship between identified strategic factors such as strategic flexibility, strategic ambidexterity, environmental dynamism, etc.

This study uses modified total interpretive structural modeling qualitative methodology (m-TISM) to develop a hierarchical model and conducts a Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis to show the interrelationship between strategic factors affects the acquirer’s post-CBA technological innovation performance. It determines the autonomous, dependent, linkage and independent strategic factors. It further uses comparative case analysis to empirically examine the strategic factors in real-time CBA situations.

This study shows the m-TISM-based hierarchical model highlighting the interrelation, level of autonomy, dependence and linkage among strategic factors affecting the acquirer’s post-CBA technological innovation performance. It suggests that strategic factors such as environmental dynamism, R&D competence, innovation capability and technological capability are largely autonomous and have significant driving power, whereas strategic ambidexterity and strategic flexibility are the connecting factors. post-M&A integration is the governing factor for technological innovation performance in CBA.

The strategists and practitioners could evaluate the key strategic factors having significant driving power for strategy formulation and implementing efficient policies. By implementing the m-TISM model acquiring a firm’s post-CBA performance can be enhanced. Future researchers might utilize quantitative methods like regression and structural equation modeling in the CBA context.

This study uses a novel m-TISM and MICMAC approach to identify the driving and dependent factors affecting post-CBA technological innovation performance. It further provides a detailed theoretical and conceptual understanding relating to the philosophy and establishes an interrelation amongst these under-researched strategic factors in CBA.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.

The purpose of this study is to evaluate the impact of green human resource management (GHRM) practices on Indian companies of different sectors and to identify the most critical GHRM practices that can lead to a more sustainable and environmentally friendly workplace.

This study uses an integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach to determine the importance of 32 GHRM practices classified into eight categories, as identified through literature review and expert consultation. This study also identifies the best sector for GHRM practices in India.

This study reveals that employee engagement is the most critical practice among all the GHRM practices identified. India’s Information Technology-Enabled Services sector benefited the most from GHRM practices, followed by the Insurance sector.

This study contributes to the literature on GHRM practices and their impact on organisations and sectors. The integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach used in this study is innovative and can be helpful for Indian companies to prioritise and implement effective GHRM practices.