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Quality 4.0 is essential to the Industry 4.0 framework, notably in the electronics sector. It evaluates product quality in real-time using automatic process controls, quality tools and procedures. The implementation of Quality 4.0 criteria in the electronics industry is the subject of this study’s investigation and analysis. In this study, nine Customer Requirements (CRs) and 18 Design Requirements (DRs) have been defined to adopt Quality 4.0, aiming to increase yield while reducing defects. This study has developed a Quality 4.0 framework for effective implementation, incorporating the People, Process and Technology categories.

Many CRs and DRs of Quality 4.0 exhibit interdependencies. The Analytic Network Process (ANP) considers interdependencies among the criteria at various levels. Quality Function Deployment (QFD) can capture the customer’s voice, which is particularly important in Quality 4.0. Therefore, in this research, we use an integrated ANP-QFD methodology for prioritizing DRs based on the customers' needs and preferences, ultimately leading to better product and service development.

According to the research findings, the most critical consumer criteria for Quality 4.0 in the electronics sector are automatic systems, connectivity, compliance and leadership. The Intelligent Internet of Things (IIOTs) has emerged as the most significant design requirement that enables effective control in production. It is observed that robotics process automation and a workforce aligned with Quality 4.0 also play crucial roles.

Existing literature does not include studies on identifying CRs and DRs for implementing Quality 4.0 in the electronics industry. To address this gap, we propose a framework to integrate real-time quality measures into the Industry 4.0 context, thereby facilitating the implementation of Quality 4.0 in the electronics industry. This study can provide valuable insights for industry practitioners to implement Quality 4.0 effectively in their organizations.

New product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology.

The data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature.

The statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation.

The data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world.

NPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy.

The current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy.

The study aims to build upon the Resource-based view of the firm (RBV) and Dynamic Capability Theory (DCT) to perform a meta-analysis on the eco-innovation/SMEs’ sustainable performance relationship.

Employing a psychometric meta-analytic approach with a random-effects model, the study examines a sample of 134,841 SMEs covering 99 studies and 233 study effects. Subgroup and meta-regression analysis were used to test the study`s hypotheses in Comprehensive Meta-Analysis (CMA) statistical software.

Results unveil that the average impact of eco-innovation on SMEs` sustainable performance is positively significant but moderate. Moreover, it was found that eco-process, eco-product, eco-organizational, and eco-marketing innovations positively influence SMEs’ sustainable performance, but the impact of eco-organizational innovation is the strongest. Findings further reveal that eco-innovation positively influences economic, social, and environmental performance, but its effect on social performance is the largest. Moreover, our findings reveal that contextual factors, including industry type, culture, industry intensity, global sustainable competitive index, and human development index, moderate the eco-innovation/SMEs’ sustainable performance relationship. Lastly, methodological factors, namely sampling technique, study type, and publication status, account for study-study variance.

Our findings imply that investing in eco-innovation is worthwhile for SMEs. Therefore, CEOs/managers of SMEs must adopt eco-innovation initiatives by establishing a sustainability vision, developing employee environmental development and training, building a stakeholder management system, and promoting employee engagement in sustainability activities.

The study develops a holistic conceptual framework to consolidate the distinct types of eco-innovation and their association with the sustainable performance of SMEs for the first time in this research stream, thereby resolving the anecdotal results and synthesizing the fragmented literature across culture, discipline, and contexts.

This study aims to investigate the emerging and attractive phenomenon of sustainable performance influenced by the role of effective knowledge management with the mediation effect of green innovation. This study incorporated the formative construct of knowledge management, which consists of knowledge creation, acquisition, knowledge sharing and knowledge application that collectively predict sustainable performance and enable the firms to become innovative under a greening perspective. This study contributed to the body of knowledge by explaining the effect and influence of knowledge management practices and greening perspective on sustaining performance.

The empirical evidence of this relationship is examined in the current research to establish effective knowledge management practices for ensuring sustainability in Malaysia’s production and service industry. The data was collected from individuals from Malaysia’s small and medium enterprise (SME) sector by using a convenience sampling technique, and analysis was performed by using the SmartPLS4.

This study’s results revealed that knowledge management influences performance by inclusion of green practices. Similarly, green innovation predicts sustainable performance; finally, green innovation mediates the relationship between knowledge management and sustainable performance. Organizations are required to ensure knowledge management practices, including the creation of knowledge, acquisition, sharing and application of knowledge to sustain performance, with a mediation effect and an essential element of green innovation that empirically explains the association between knowledge management practices and performance.

The scope of research is limited and restricts the capacity of findings for a broader context inside Malaysia. There is a need to consider the organizational culture, contextual elements and leadership styles in investigating and explaining the critical phenomenon of sustainable performance.

Organizations are required to ensure knowledge management practices, including the creation of knowledge, acquisition, sharing and application of knowledge to sustain performance, with a mediation effect and an essential element of green innovation that empirically explains the association between knowledge management practices and performance.

Sustainable performance has the potential to increase the economic, social and environmental concerns of the firm, as strategies must be performance-centric to achieve the objectives. This research effort can assist Malaysia’s SME sector from various perspectives. This study provides the basis for taking the initiatives for knowledge management by focusing on creation, acquisition, sharing and application practices for achieving sustainable performance. This study incorporated the emerging concept of green innovation for its actions and strategies to achieve sustainable performance, and the actions of firms to adopt and implement the green practices that influence sustainable performance.

The framework for this study is novel. This study has been conducted to determine the sustainable business performance influenced by knowledge management, technological advancements and green supply chain management, moderated by leadership support. This study incorporated the formative construct of knowledge management, which consists of knowledge creation, acquisition, knowledge sharing and knowledge application that collectively predict sustainable performance and enable the firms to become innovative under a greening perspective. This study contributed to the body of knowledge by explaining the effect and influence of knowledge management practices and greening perspective on sustaining performance.

While operational performance is important for a company's competitiveness and profitability, this study claims that procurement may be required to drive operational performance. This study aims to focus on how procurement practices is related to operational performance among selected manufacturing firms.

The model was tested on a sample of 329 manufacturing firms in clothing and textiles, chemicals and plastics, food and beverages processing, wood processing, metal processing and pharmaceutical manufacturing industries in a developing economy.

The results indicate that firms with higher and optimal level of procurement practices achieve higher levels of operational performance. Regardless of the H1 rationale, this study suggests that increasing procurement techniques has a limit in terms of improving operational performance. This study establishes that extreme level of procurement practices is associated with decreasing operational performance.

This study calls attention to how managers can guide organizations in refining their procurement strategies and practices; there is the need to strive to strike a balance between diversification and efficiency in procurement.

Manufacturing firms are often integral parts of local communities. The decisions they make regarding procurement practices can impact the economic health of these communities. Striking a balance between diversification and efficiency can contribute to sustainable business practices that benefit both the company and the local community.

As the main contribution, this study brings the additional perspective that extreme level of procurement practices is associated with decreasing operational performance.

Co-creation of services (CCOS) is a collaborative strategy that emphasises customer involvement and their expertise to increase the value of the service experience. In the service ecosystem, artificial intelligence (AI) plays a key role in value co-creation. Therefore, this study is undertaken to empirically uncover how AI can empower CCOS.

The source data were collected from 305 service provider respondents and quantitative methodology was applied for data analysis.

New service development augmented with AI provides tangible value to service providers while also providing intangible value to supportive customers. With AI, service providers adapt to new innovations and enrich additional information, which eventually outperforms human-created services.

AI adoption for CCOS empowerment in service businesses brings “service-market fit”, which represents the significant benefits wherein customers contribute to creativity, intuition, and contextual awareness of services, and AI contributes to large-scale service-related analysis by handling volumes of data, service personalisation, and more time to focus on challenging problems of the market.

This study presents theoretical concepts on AI-empowered CCOS, AI technological innovativeness, customer participation in human-AI interaction, AI-powered customer expertise, and perceived benefits in CCOS, and subsequently discusses the CCOS empowerment framework. Then, it proposes a novel conceptual model based on the theoretical concepts and empirically measures and validates the intention to adopt AI for CCOS empowerment. Overall, the study contributes to novel insight on empowering service co-creation with AI.

The purpose of this paper is to investigate and review the impact of the use of statistical quality control (SQC) development and analytical and numerical methods on average run length for econometric applications.

This study used several academic databases to survey and analyze the literature on SQC tools, their characteristics and applications. The surveys covered both parametric and nonparametric SQC.

This survey paper reviews the literature both control charts and methodology to evaluate an average run length (ARL) which the SQC charts can be applied to any data. Because of the nonparametric control chart is an alternative effective to standard control charts. The mixed nonparametric control chart can overcome the assumption of normality and independence. In addition, there are several analytical and numerical methods for determining the ARL, those of methods; Markov Chain, Martingales, Numerical Integral Equation and Explicit formulas which use less time consuming but accuracy. New ideas of mixed parametric and nonparametric control charts are effective alternatives for econometric applications.

In terms of mixed nonparametric control charts, this can be applied to all data which no limitation in using of the proposed control chart. In particular, the data consist of volatility and fluctuation usually occurred in econometric solutions. Furthermore, to find the ARL as a performance measure, an explicit formula for the ARL of time series data can be derived using the integral equation and its accuracy can be verified using the numerical integral equation.

As the hospitality business adapts to the digital age, the importance of using Virtual Design (VD) to create memorable visitor experiences has grown. This study aims to investigate the potential of VD in the hospitality sector, particularly regarding the improvement of guests' overall experiences.

The research study examines how Virtual Reality (VR), Augmented Reality (AR) and other digital technologies are currently used in VD firms. It explores how these simulated architectural features are implemented in other facets of the hospitality experience, like the decor of guest rooms and restaurants and staff responsiveness to guests' needs.

The study also examines VD's potential outcomes and advantages for the hotel industry and its clients. It investigates the potential of VD to help hospitality businesses offer more customised services, boost customer loyalty and gain an edge in the market. VD implementation in the hospitality business may face several obstacles, some of which are discussed in this study.

Methods include both qualitative and quantitative techniques, such as interviews with experts, guest surveys and an examination of the use of VD in specific hotels. This study intends to help the hotel industry benefit from a VD by analysing real-world case studies and gathering empirical data that can be used to draw conclusions and formulate recommendations.

This paper primarily aims to identify the multilevel learning processes emerging from abrupt telework implementation in a public knowledge-intensive organization (KIO) amid the COVID-19 crisis.

This single-case process research was guided by interpretivist epistemology. Empirical data from documentary research and 41 interviewed managers were processed by inductive qualitative analysis using the multilevel learning theoretical model.

Eight types and three modes of learning processes during the COVID-19 pandemic were identified in a public KIO, iteratively emerging in multilevel learning dynamics during the compulsory adoption of telework and replacing the face-to-face work mode conducted since its foundation.

As insider researchers, while daily and privileged access to the field was obtained, it also demanded their continuous effort to maintain transparency and scientific distancing; conceptual results are restricted to process theorisation studies, specifically the 4Is theoretical model in the scope of crisis learning process studies concerning KIOs.

This study provides evidence for managers to adopt interactive dynamics among eight multilevel types and three learning modes of emergent learning, developed during the COVID-19 pandemic, and support learning practices’ implementation and routinisation across three organizational levels in crisis situations. In addition, evidencing emergent types of learning enables organizational learning (OL) researchers to examine how organizational structures and work practices either promote or inhibit different learning types and impact multilevel learning when adopting teleworking during a crisis.

This research has theoretical value in two ways: (i) Providing empirically supported knowledge: This involves understanding multilevel learning processes resulting from emergent learning in a public KIO that abruptly adopted teleworking during a crisis context; (ii) deepening process theorization studies on OL: To achieve this, we enhance the 4I model by incorporating eight types and two modes of learning processes. These processes iteratively emerge from the individual and group levels towards the institutional level in a public KIO.

This study investigates the impact of personal, organisational and innovation capability on technological innovation and further impacts on small and medium enterprises’ (SMEs') performance during uncertainty. Moreover, the moderator role of social sustainability orientation on the relationships of technological innovation, innovation performance and organisational resilience is also examined.

A quantitative approach using 524 SME responses and Smart PLS 4.0 are adopted in this research.

The results indicate a correlation between three types of capabilities with technological innovation and further conversion to organisational resilience. Additionally, social sustainability shows a negative moderating effect between innovation performance and organisational resilience. The research findings advanced the resources-based-view (RBV) by proposing three capability dimensions as platforms for SMEs' innovation success, which later generate resilience possibilities. Specifically highlighted in this study are the personal capabilities of managers, organisational capabilities and innovation capabilities in setting business objectives and resource allocation towards economic and sustainable goals during turbulence and uncertainty.

This study investigates the role of technological innovation and innovation on SME resilience. Notably, we deploy the social sustainability orientation as moderators towards the relationship between technological innovation, innovation performance and SMEs’ resilience. SMEs employing social sustainability orientation might negatively inhibit the translation from innovation performance to SME resilience, providing novel insights into navigating uncertainty in modern business. It has no effect on the relationship between technological innovation and its consequences.