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Effective human resource management and organisational success depend heavily on measuring employee performance. This paper aims to investigate multiple factors that are crucial in assessing and measuring employee performance in Indian manufacturing sectors. Further prioritisation of the manufacturing industries based on their practices is conducted to measure employee performance.

The LOCOW approach has been used in this study to determine the relative weightage of the factors that assist in measuring employee performance, and the MARCOS method prioritises manufacturing industries.

Through weightage, criteria show that task performance is given the most weightage, followed by adaptability and contextual performance in the manufacturing sectors. The top three industries are oil and gas, steel and automobile.

This study gives manufacturing industries the tools they need to improve their HR practices, get better work from their employees and stay ahead of the competition in a constantly changing industry.

The current work examines the weightage among the factors that aid in assessing employee performance; further, the use of MARCOS technique prioritises the industries, which can be considered the original contribution.

Asset maintenance in manufacturing industries is a critical issue as organizations are highly sensitive towards maximizing asset uptime. In the advent of Industry 4.0, maintenance is increasingly becoming technology driven and is being termed as Maintenance 4.0. Several barriers impede the implementation of Maintenance 4.0. This article aims at - exploring the barriers to implementation of Maintenance 4.0 in manufacturing industries, categorizing them, analysing them to prioritize and suggesting the digital technologies to overcome them.

Twenty barriers to the implementation of Maintenance 4.0 were identified through literature survey and discussion with the industry experts. The identified barriers were divided into five categories based on their source of occurrence and prioritized using fuzzy-technique for order preference by similarity to ideal solution (TOPSIS), sensitivity analysis was carried out to check the robustness of the solution.

“Data security issues” has been ranked as the most influencing barrier towards the implementation of Maintenance 4.0, whereas “lack of skilled engineers and data scientists” is the least influencing barrier that impacts the implementation of Maintenance 4.0 in the manufacwturing industries.

The outcomes of this research will help manufacturing industries, maintenance engineers/managers, policymakers, and industry professionals for detailed understanding of barriers and identify easy pickings while implementing Maintenance 4.0.

Manufacturing industries are witnessing a paradigm shift due to digitization and maintenance 4.0 forms the cornerstone. Little research has been carried in Maintenance 4.0 and its implementation; this article will help in bridging the gap. The prioritization of the barriers and digital course of actions to overcome those is a unique contribution of this article.

This study aims to analyze the mediating role that green practices (GPs) and green technologies (GTs) play in the relationship between lean manufacturing (LM) and industrial performance (IP). It is suggested that GPs and GTs are crucial for transforming lean routines into enhanced performance that simultaneously meet current environmental requirements.

The hypotheses are tested using a mixed methodology, which includes a partial least squares structural equation modeling approach and a fuzzy-set qualitative comparative analysis (fsQCA) applied to a multisectoral sample from three European countries (Spain, Sweden and Croatia).

The results confirm that GPs mediate the relationship between LM and IP; however, in the case of GTs, this mediation does not appear to occur, although GTs emerge as a peripheral condition in the subsequent fsQCA. These findings highlight the need to avoid an exclusively technocentric approach and underscore the importance of implementing green organizational practices alongside technology investments to achieve successful lean initiatives.

It seems clear that managers should apply GPs, combined with LM, to improve sustainability and efficiency and should apply GTs once a more mature lean-green culture has been established.

In recent years, the scientific community has increasingly focused on the impact of implementing GPs and GTs on IP within LM plants. However, to the authors’ knowledge, no study has yet analyzed the combined effect of both initiatives. This paper seeks to address this gap by examining, in aggregate, the moderating effect of GPs and GTs on IP in LM plants.

The relationships between transformational leadership (TL), organizational climate (OC) and project performance have been investigated by previous studies, but no review of existing studies has systematically analyzed the effects of TL and OC on project performance in the industrial revolution (IR) 5.0 era. Therefore, this study aims to conduct a systematic literature review on the effects of TL and OC on project performance in IR 5.0, and to identify mainstream research topics, research gaps and future research directions.

To do this, a total of 53 included journal articles were obtained after initially retrieving 648 documents from the Scopus database by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. It consists of four main steps, namely, identification of documents, screening, eligibility and included articles. In addition, science mapping analyses were conducted for keyword co-occurrence and document analyses, which aided in identifying the mainstream research topics, research gaps and future research directions.

The results report the annual publication trends, keywords and document analyses. Furthermore, a detailed qualitative discussion highlighted four mainstream research topics including TL in project management; the relationship between TL, OC and innovation; safety climate; and OC in project management. Moreover, this review study identified four research gaps and future research directions aligned with the mainstream research topics. They include: longitudinal investigations and multinational corporation surveys in TL; scope and longitudinal data in innovation; mono-method bias and universality of safety climate; and more comprehensive analyses of OC.

This review study would contribute to not only advancing the effects of TL and OC on project performance in IR 5.0, but also enabling project managers to understand TL or OC issues to improve project performance.

Although several papers have been published over the past decade on various aspects of digital entrepreneurship, nothing has hitherto been written on the theme of digital entrepreneurship in the metaverse. This paper, therefore, aims to explore the key challenges of digital entrepreneurship in the metaverse, with a view to developing a model to address these challenges.

The Decision Making Trial and Evaluation Laboratory approach was adopted in this study to rank the selected challenges in order of importance and establish a cause-and-effect relationship between them. The data were gathered from 10 experts from Saudi Arabia who deploy augmented reality, virtual reality and other immersive technologies in the course of their business.

Three challenges, namely, “Market fragmentation (C3)”, “Technical complexity (C1)” and “Monetisation and revenue models (C5)” were highlighted in the findings as the main factors of influence in the Cause group, whereas the remaining five challenges, “Infrastructure and connectivity (C2)”, “Social and ethical considerations (C8)”, “User adoption and engagement (C6)”, “Privacy and security (C7)” and “Intellectual property protection (C4)”, were categorised in the Effect group, being significantly influenced by the challenges in the Cause group.

To the best of the authors’ knowledge, this is the first study to explore the challenges of metaverse-enabled digital entrepreneurship and classify the identified challenges into groups of Cause and Effect.

This paper examines the impact of green human resource management (GHRM) practices on employees’ pro-environmental behavior in Pakistan’s hospitality industry. It attempts to identify the critical success factors involved in promoting GHRM and pro-environmental behaviors at the workplace using Interpretive Structural Modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) approaches. Later, based on the ability-motivation-opportunity (AMO) model, the study also categorizes the identified critical factors into three categories: ability, motivation and opportunity.

The ISM approach was applied to determine the contextual relationship among the identified critical success factors responsible for promoting GHRM. MICMAC, a structural technique to analyze and validate the ISM-based model, was used to determine the autonomous, dependent, linkage and independent factors based on expert opinions and judgments. The goal was to determine the role of GHRM in transforming the pro-environmental behavior of employees.

The study’s findings show that the proper integration of effective GHRM practices significantly impacts pro-environmental employee behavior. The hierarchical model introduces innovation in the field of GHRM because ISM-based hierarchical models are flexible enough to include or exclude practices according to the green organizational objectives in the hospitality industry within the context of Pakistan. The results offer a comprehensive illustration of the importance of GHRM practices in facilitating, encouraging and promoting employees to take green initiatives and achieve business sustainability.

The study utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique to identify key success criteria for GHRM, while the innovative approaches of ISM and MICMAC techniques were used to investigate employee pro-environmental behaviors. This novel method gives GHRM research an analytical direction by providing an organized framework for evaluating the impact of GHRM initiatives on environmental outcomes. Additionally, by focusing on developed economies rather than emerging ones, our study within Pakistan’s hospitality sector fills a knowledge vacuum on the dynamics of GHRM in a developing nation.

This study highlights the significance of managers in the hospitality sector serving as role models for implementing GHRM practices to encourage pro environmental behavior among employees. Prioritizing green structural capital, establishing standard environmentally friendly criteria for hiring and evaluating prospective employees and initiating green projects to promote a psychologically green environment are some of the key recommendations. Improving environmental performance, employee satisfaction and loyalty in the hotel industry requires constant communication, training and employee participation in sustainability decision-making.

The GHRM practices have been extensively discussed by academics and researchers. However, there is a notable absence of discussion on the key factors that play a role in transforming employees’ attitudes and behaviors.

This study aims to construct a framework to evaluate technology innovation performance (TIP) of manufacturing organizations by adopting a fuzzy-based approach. In very short time, the world’s economic order has been reformed by economic globalization, thereby bringing new challenges as well as opportunities for the manufacturing industries. Policy makers encounter different decisions that require the use of various types of data in their decision-making process. These challenges raised the necessity of measuring innovation capability, which is critical issue for decision makers in today’s competitive world. The research results reveal the complexity of the path to technology innovation evaluation, constituting a novel contribution to the literature.

It is difficult for decision makers to make appropriate and effective decisions without knowing the innovation capability of companies in a particular sector or a region. As a result, not only on a macro but also on a micro level, an integrated and complete technique of measuring, estimating and even projecting innovation performance is necessary. In light of above mentioned facts, the present study proposes a technology innovation performance evaluation system of manufacturing organizations using fuzzy logic.

A model has been developed to be beneficial for any kind of organization where TIP evaluation is important consideration for enhancing manufacturing performance. Fuzzy control is used to determine the overall performance index by combining results of the TIP in selected criteria, which will certainly ensure suitability of the concerned organizations during performance rating calculations.

This study elaborates a fuzzy model to predict TIP with fuzzy rules. This is the first time a TIP evaluation model is developed using fuzzy approach that will be applicable for any kind of manufacturing industry where TIP evaluation is considered to be significantly important for cooperation’s to sustain competitiveness in the dynamic landscape.

The purpose of this study to identify and optimize the machining of polyvinyl butyral (PVB) material for industrial uses. The research is based on input machining parameters of rotary ultrasonic machining for better understand the output response surface roughness (SR) property of polyvinyl butyral (PVB) by using the Taguchi approach. The grey relational grade analysis (GRG) is also implemented to resolve the complex interrelationship of SR data for optimization and predicting and validate the results.

In experimental work, the input parameters, namely, concentration, abrasives, power rate, grit size, tool material and hydrofluoric (HF) acid has been selected. The experiment’s design was created using MINITAB Software; the L27 orthogonal array was selected for the experimentation. SR was examined with the GRG technique for process optimization. On the other hand, for single parameter optimization analysis of variance (ANOVA) has been used.

ANOVA optimization technique gives the best result on concentration (40%) of abrasive (Al2O3+SiC+B4C), power rate (40%), grit size (600), HF acid (1.5%) and tool material (D2 alloy) are the optimal parameters to provide the slightest degree of SR. GRG optimization of multi-response parameter setting: 40% concentration, SiC+B4C mixed abrasive slurry, 40% of power rating, 280 grit size, 0.5% HF acid and high-speed tool steel tool material gives better results. The SR of PVB glass material improved by 20% after grey relational analysis.

There are several practical applications in a variety of material processing sectors, including metallurgy, machinery, electronics and transportation. These real-world applications have produced substantial and discernible economic benefits.

The analytical and optimization results will be used in the various material processing sectors, including metallurgy, machinery, electronics and transportation.

The ANOVA and grey theory approaches offer the reader a primary picture of the machining research and process parameter optimization. Combined abrasive slurry of Al₂O3+SiC+B₄C with a high power-rating exhibits lower SR. Similarly, grit size is vital; larger grits produce better SR. Ra – 0. 611 m is the lowest SR value at the hole found in trial 25 after the experimentation.

The purpose of this study is to identify a critical pathway of the effect of big data analytics capabilities (BDACs) on strategic vigilance based on hierarchical process and a capability approach.

The researcher adopted a qualitative approach using interviews and a quantitative approach based on the interpretative structural modeling (ISM) fuzzy cross-impact matrix multiplication applied to classification (MICMAC) approach. A primary theoretical approach was also conducted to identify BDACs previously cited in the literature.

Four main subdivisions of BDACs were identified: management capabilities, infrastructure flexibility, talent capability and technology. Management capabilities followed by big data technical knowledge and associated with talent capabilities generate a flexible infrastructure to enhance SV. A dynamic capability perspective of knowledge and information is also required for SV.

Despite the opportunity of this research and the originality of results, some limitations have to be mentioned and can constitute further directives for future researchers, such as the problem of result generalization. First, this research was based in Saudi Arabia, and a comparative approach to defining BDAC on an international level can be more beneficial in providing an exhaustive list of these capabilities. Second, reliability issues, in this research can be addressed due to the use of qualitative data collection which is considered by many researchers as unspecified and can lack scientific rigor. Future studies can improve the number of interviews during the data collection process and data process using an advanced methodological approach. Third, the effect of BDAC in SV according to the hierarchical final modal is not quantified, future work can use this research model to appreciate each effect using a quantitative approach such as correlation and structural equation modeling while considering respondents with different profiles to take into account different point of view in this concern.

This research enriches the BDAC and MICMAC literature and contributes to this aspect in three main levels. First, by providing an additional empirical asset in this field, this study offers by the way a new case to the big data literature on the banking sector. Based on the limited knowledge as well as results collected from different databases and rigorously analyzed, this subject was not treated previously and the author could not find similar studies with the same approach dealing with the key BDACs in Saudi Arabia.

This research presents three main implications for policymakers and researchers interested in big data analytics (BDA) through a capability and strategic perspective. First, to attain SV, they should prioritize the development of interactive interfaces and open platforms as the primary step before collecting information and deconstructing it to guarantee the generation of knowledge and make decisions effectively. Second, policymakers must introduce organizational technologies in terms of technology management, technical knowledge and technology for decision-making. This requires simultaneous sharing and communication according to relational management. Third, the research conclusions have many critical managerial ramifications for banks in Saudi Arabia while considering the adoption of BDAC. The importance of BDACs (especially technical aspects) in shaping the decision-making to be strategically vigilant emphasizes policymakers’ orientation by paying close attention to these aspects and specific training programs to facilitate the use of such technologies and guarantee strong security measures. Moreover, findings support a balance between technical and functional BDAC.

The adoption of a knowledge-based dynamic capabilities (KBDCs) view to analyze the interaction between different BDACs in banks in Saudi Arabia to be strategically vigilant using a mixed approach.

The study develops a model that associates digital marketing capabilities with customer relations orientation, technology orientation and social customer relationship management (CRM) competence with business performance, considering market environment factors. It also aims to contribute to the literature with dynamic capability theory by testing this model.

The model suggested is tested by data obtained online from a sample of 178 Turkish companies that use digital marketing tools. The data obtained were analyzed using the structural equation model (SEM).

In this study, it has been determined that digital marketing capabilities affect business performance. Also, a positive moderating effect of dynamism is seen in the relationship of digital marketing capabilities with business performance. Also, technology orientation, social CRM competence and customer relations orientation affect and explain digital marketing capabilities as antecedents.

The study only focuses on Turkish companies, and no distinction has been made in terms of business type/size. Also, since this research was carried out during the COVID-19 pandemic, the data may have been affected by this period.

Contextual and methodological research gaps still exist in digital marketing capabilities literature. This study is evaluating Turkish companies’ digital marketing capabilities with business performance, and in the context of Turkey, it is important for other developing countries with a similar market environment. Also, it contributes to the dynamic capabilities theory literature by constructing a novel conceptual model examining the relationship among dynamic digital marketing capabilities, their antecedents and business performance.