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This paper aims to derive a model that explores how the interplay between knowledge integration capability and innovation impacts strategic orientation, leading to the attainment of sustainable competitive advantage. The study considers the constituents of strategic orientation, namely, customer orientation, competitor orientation and technology orientation, as the basis for achieving sustainable competitive advantage. The study suggests that the firm’s capacity for integrating external and internal knowledge shapes how strategic orientation influences sustainable competitive advantage through service innovation.

This empirical research relies on qualitative and quantitative data gathered from telecom professionals to assess how knowledge integration and service innovation influence sustained competitive advantage. Structured equation modeling is used to examine the model and its interrelationships.

The research establishes significant relationships between strategic orientations, knowledge integration capability, service innovation and sustainable competitive advantage. Knowledge integration capability and service innovation are found to mediate the relationship between strategic orientations and the achievement of sustainable competitive advantage.

The study highlights the significant contribution of a firm’s knowledge integration capability in driving service innovation, especially in technology-intensive service industries facing hypercompetition. It also advocates prioritizing technology orientation and integrating knowledge from internal and external sources for competitive advantage.

To the best of the authors’ knowledge, this study is the first to model the effect of knowledge integration capability and service innovation on strategic orientation-led sustainable competitive advantage.

This study aims to explore the integration of Industry 4.0 technologies with lean six sigma practices in the manufacturing sector for enhanced process improvement.

This study used a fuzzy decision-making trial and evaluation laboratory approach to identify critical Industry 4.0 technologies that can be harmonized with Lean Six Sigma methodologies for achieving improved processes in manufacturing.

The research reveals that key technologies such as modeling and simulation, artificial intelligence (AI) and machine learning, big data analytics, automation and industrial robots and smart sensors are paramount for achieving operational excellence when integrated with Lean Six Sigma.

The study is limited to the identification of pivotal Industry 4.0 technologies for Lean Six Sigma integration in manufacturing. Further studies can explore the implementation challenges and the quantifiable benefits of such integrations.

Integrating Industry 4.0 technologies with Lean Six Sigma enhances manufacturing efficiency. This approach leverages AI for predictive analysis, uses smart sensors for energy efficiency and adaptable robots for flexible production. It is vital for competitive advantage, significantly improving decision-making, reducing costs and streamlining operations in the manufacturing sector.

The integration of Industry 4.0 technologies with Lean Six Sigma in manufacturing has significant social implications. It promotes job creation in high-tech sectors, necessitating advanced skill development and continuous learning among the workforce. This shift fosters an innovative, knowledge-based economy, potentially reducing the skills gap. Additionally, it enhances workplace safety through automation, reduces hazardous tasks for workers and contributes to environmental sustainability by optimizing resource use and reducing waste in manufacturing processes.

This study offers a novel perspective on synergizing advanced Industry 4.0 technologies with established Lean Six Sigma practices for enhanced process improvement in manufacturing. The findings can guide industries in prioritizing their technological adoptions for continuous improvement.

After completion of this case study, students will be able to understand about characteristics of urban poor in the city of Mumbai which will aid in understanding about other emerging markets as well, to apply Health Belief Model to help students analyse behaviour change model, to apply social marketing strategies to popularize a social marketing cause, to learn about non-traditional intermediaries and apply to promote a social marketing cause, to apply Ansoff matrix and evaluate various strategies for growth and to analyse various challenges faced by social entrepreneurs and enable learners to arrive at solutions (applicable for social entrepreneurs and marketing executives).

This case study looked at a Mumbai-based organization, Medow Brite Enterprises, which sold sanitary napkins under the brand FeelOn to women particularly from urban poor background. The protagonist Mrs Ameeta Neel Ramesh was at the helm of the organization and was stuck with a dilemma – whether to enter rural markets or focus on selling incinerators and aid in disposing used sanitary napkins which was adding to the volume of non-biodegradable waste in the city. In 2019, Neel Ramesh made her first investment in Medow Brite. The organization had seen turbulent times during COVID-19 outbreak. However, Neel Ramesh with her astute strategy, helped the company get back on its feet. Medow Brite instead of manufacturing started procuring quality sanitary napkins from specific vendors. In contrast to many other sanitary napkins available in the market, FeelOn had cotton sanitary napkin variant without presence of plastic in the pads. Neel Ramesh had taken a different route for sale of her sanitary napkin, she conducted awareness sessions with the help of non-governmental organizations in various locations of Mumbai as well as Maharashtra. Post these sessions she sold her sanitary napkins among the attendees of the awareness sessions.

The case study can be included in marketing management course, consumer behaviour as well as social marketing courses in both undergraduate level and postgraduate level. In addition, the case study is also suitable for social entrepreneurs and marketing executives to discuss about non-traditional sales and marketing approaches, identifying unique segments and understanding behaviour change theories.

Teaching notes are available for educators only.

CSS 8: Marketing.

The purpose of this study is to review the different facets associated with Industry 5.0 (I5.0) and propose a conceptual framework to boost the applicability of this novel technological cum social aspects within industrial organizations for improved organizational sustainability.

This research work adopted a bibliometric analysis that encapsulates a quantitative set of tools for bibliometric and bibliographic information. This study uses the database of Scopus to acquire data related to different facets of I5.0. The study implies a different spectrum of terms to reach the final corpus of 91 articles related to I5.0. Furthermore, a conceptual define, measure, analyze, improve and control (DMAIC)-based framework based on different literature findings is proposed and validated based on the input of experts from different parts of the world.

The results indicate that I5.0 is still in its infancy. The wider applicability of I5.0 demands comprehensive theoretical knowledge of different facets of this new paradigm and the development of a framework to adopt it on a larger scale. Organizations that are in the race to adopt I5.0 face major challenges related to the digitization of processes along with well-defined cyber-physical systems and the lack of a dedicated framework to execute I5.0. Furthermore, the result also suggests that manufacturing industries are more ready to adopt I5.0 practices as compared to service industries, which can be attributed to well-defined technological measures available in manufacturing settings.

To the best of the authors’ knowledge, this is one of the first studies that explore different know-how and challenges and provides a holistic view of I5.0 by providing a systematic adoption framework.

Agriculture is a vital sector for every country, especially for a country like India, where the majority of the population is dependent on agriculture as their earning source. Technological improvements in agriculture will increase output with proper forecasting of input resources. In this study, the author tries to investigate the attitude of end users (farmers) about the use of Industry 4.0 (I4.0) technologies.

The unified theory of acceptance and use of technology (UTAUT) model is used to assess the behavioral aspects. The significance of socioeconomic and technological factors is highlighted, providing the study with a thorough understanding of farmers' decision-making processes. A research questionnaire was developed for data collection, and descriptive and inferential statistics were used to analyse the results using AMOS and SPSS software.

A total of 371 survey responses were collected. The results demonstrate that the hypothesis regarding UTAUT model components is validated, while several mediating hypotheses are not supported, indicating that they are not significant in farmers' decision-making.

In this study, socioeconomic and technological factors are considered to be mediating and moderating elements between the constructs of the UTAUT model. Increasing the accuracy and reliability of our study by integrating mediating and moderating variables. This study assists industry specialists in understanding the elements that farmers consider while switching toward new technologies.

This study aims to explore the potential application of blockchain technology in Lean Six Sigma (LSS) project through a proposed blockchain-LSS (BLSS) model. The proposed model can tackle real-time problems in information sharing, transparency and traceability in every stage of the LSS project.

The scoping review approach is used to develop the integrated model of the BLSS approach for operational excellence. The proposed model is validated through expert’s input, which is collected by a questionnaire survey method.

The prime function of the proposed BLSS model is the information sharing among the project team and real-time monitoring, transparency, traceability and immutability in the Define-Measure-Analyze-Improve-Control phase. The proposed model also consists the information about the role of blockchain features at each phase of the LSS project. The project team and industry employees can trace the success of the project at every moment, resulting in trust buildup and the elimination of fake data. Moreover, there would be no disputes among various sections/shops of the plant and employees to share the real information.

This paper provides guidelines to practitioners and managers for integrating the LSS approach and blockchain. The blockchain helps managers and practitioners in better data traceability and transparency, monitoring of data as well as more sustainable LSS project management.

To the best of the authors’ knowledge, this is the first research attempt that developed an integrated model of blockchain and LSS approach to maintaining the immutable records of assets in projects and targeted Industry 4.0.

Blockchain technology can potentially address the challenges of information storage, sharing and management and improve them further in an organization and sector as a whole. This study aims to investigate the effects of technology, organization and environment on the behavioral intention of employees to adopt blockchain in the Indian insurance sector and the mediating role of knowledge management practices.

A structured questionnaire was used to collect a sample size of 390 responses based on convenience sampling. Partial least square structural equation modeling was used to analyze the data.

The findings highlighted that organizational factors, followed by technological factors, significantly impact employees' behavioral intentions. The results established that the impact of environmental factors is insignificant on blockchain adoption intention. Knowledge management practices significantly mediate the relationship between organizational factors, technological factors and behavioral intention.

The results indicate that organizations must prioritize organizational factors (technological competence, top management support and financial readiness) and knowledge management practices (knowledge creation, sharing and retention) to positively impact employees' behavioral intentions and ensure successful and effective technology adoption.

Using the Technology-Organization-Environment framework, the study tests the conceptual model, showing the relationship between technological, organizational and environmental factors, behavioral intention and knowledge management practices. The role of knowledge management practices in technology adoption within organizations has been scarcely explored. This study adds significant and novel contributions in this area.

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.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.