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The purpose of this paper is to identify the key elements of digitalization for lean and green operations and develop a conceptual framework for their implementation. The paper focuses on small and medium-sized enterprises (SMEs) and aims to explore the role of digitalisation in enhancing their operational efficiency and sustainability. By identifying key factors and metrics related to digitalisation, the paper seeks to provide insights for strategic management to improve lean and green practices in SMEs.

Interpretive structural modeling (ISM) and Matrix of Cross-Impact Multiplication Applied to a Classification (MICMAC) approaches were deployed to classify the major dimensions of digitalisation. These methods were used to analyse the direct and indirect relationships among the identified elements of digitalisation. A comprehensive literature review and expert consultations were conducted to identify 13 key elements relevant to lean and green operations. The experts also assisted in determining the contextual relationships between the variables for the ISM model.

The analysis classified the 13 identified elements of digitalisation into different levels according to their driving power and dependence. The results from the ISM model indicated three levels of classifications. At level-1, Internet of things (IoT) and smart sensors (IoT & SS), automation and robotics directly influence lean and green operations. At level-2, real-time monitoring and control system and at level-3 fundamental elements of digitalisation such as big data analytics, predictive maintenance, cloud computing, energy management systems (EMSs), additive manufacturing, blockchain, digital workflow automation and digital collaboration platform.

All elements are interrelated and essential for making strategic decisions. This study emphasis the significance of prioritising these attributes to attain long-term excellence through digitalisation. For the industries that seek the reward of lean and green operations for their growth, this paper has great practical utility. Identifying the key factors of digitalisation would help strategic managers in handling lean and green environment of SMEs through these aspects.

This study aims to explore the constituents of artificial intelligence (AI)-augmented knowledge management (AIKM) capability and its impact on clinical performance (CP) in the health-care sector. It further examines the mediating role of absorptive capacity (Abs Cap) and discusses the implications of these findings for marketing strategies, highlighting how enhanced CP through AIKM can lead to more effective and patient-centered marketing approaches.

This research uses a mixed-method design. A qualitative study through semi-structured interviews was conducted to explore the facets of AIKM. The synthesis of qualitative findings infused with the relevant literature to develop a hypothesized model of AKM, Abs cap and CP metrics (e.g. diagnostic accuracy, patient satisfaction and treatment effectiveness). A survey of health-care professional in India was conducted to assess the proposed model by using structural equation modeling (PLS-SEM).

The results demonstrate a significant positive relationship between AIKM and CP. Moreover, Abs Cap mediates this relationship partially, highlighting its crucial role in translating improved knowledge access and analysis enabled by AI into enhanced clinical outcomes.

The findings suggest that health-care organizations should invest in developing AIKM alongside strengthening Abs cap to maximize the positive impact of AI on CP and ultimately improve patient care. Future research can explore specific AIKM components and Abs cap facets influencing different aspects of CP.

This study represents a pioneering effort to conceptualize AIKM within the health-care context and empirically establish it as a higher-order factor. The inclusion of marketing strategies underscores the potential of AIKM not only in improving clinical outcomes but also in transforming health-care marketing. The mediating role of Abs Cap emphasizes the importance of organizational structures and processes that facilitate the absorption and utilization of knowledge, thereby contributing to both clinical and marketing excellence.

The purpose of this research is to identify and analyse the perceived risk factors affecting the safety of electric two-wheeler (E2W) riders in urban areas. Given the exponential growth of the global E2W market and the notable challenges offered by E2W vehicles as compared to electric cars, the study aims to propose a managerial framework, to increase the penetration of E2W in the emerging market, as a reliable, and sustainable mobility alternative.

The perceived risk factors of riding E2Ws are relatively scanty, especially in the context of emerging economies. A mixed-method research design is adopted to achieve the research objectives. Four expert groups are interviewed to identify crucial safety risk E2W factors. The grey-Delphi technique is used to confirm the applicability of the extracted risk factors in the Indian context. Next, the Grey-Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is employed to reveal the causal-prominence relationship among the perceived risk factors. The dominance and prominence scores are used to perform Cause and Effect analysis and estimate the triggering risk factors.

The finding of the study suggests that reckless adventurism, adverse road conditions, individual characteristics and distraction caused by using mobile phones, as the topmost triggering risk factors that impact the safety of E2Ws drivers. Similarly, reliability on battery performance low velocity and heavy traffic conditions are found to be some of the critical safety factors.

E2Ws are anticipated to represent the future of sustainable mobility in emerging nations. While they provide convenient and quick transportation for daily urban commutes, certain risk factors are contributing to increased accident rates. This research analyses these risk factors to offer a comprehensive view of driver and rider safety. Unlike conventional measures, it considers subjective quality and reliability parameters, such as battery performance and reckless adventurism. Identifying the most significant causal risk factors helps policymakers focus on the most prominent issues, thereby enhancing the adoption of E2Ws in emerging markets.

We have proposed an integrated framework that uses grey theory with Delphi and DEMATEL to analyse the safety risk factors of driving E2W vehicles considering the uncertainty. In addition, the amalgamation of Delphi and DEMATEL helps not only to identify the pertinent safety risk factors, but also bifurcate them into cause-and-effect groups considering the mutual relationship between them. The framework will enable practitioners and policymakers to design preventive strategies to minimize risk and boost the penetration of E2Ws in an emerging country, like India.

This study aims towards developing a conceptual framework by systematically reviewing the available literature with reference to job crafting under the lens of an emerging economy from South Asia, i.e. India, which is the largest country and the largest economy in the South Asian region.

The study employs a hybrid methodology of a systematic literature review (SLR) and bibliometric analysis using VOSviewer and Biblioshiny. Bibliometric analysis provides glimpses into the current state of knowledge like-trend of publication, influential authors, collaboration with foreign authors, the major themes and studied topics on job crafting in India etc. Further, a detailed SLR of the selected articles led to the development of the conceptual framework consisting of the enablers and outcomes of job crafting.

It discusses implications for academia, business and society at large, and also provides valuable insights to policymakers and practitioners paving the way for better adoption, customization and implementation of job crafting initiatives.

Owing to its own unique social, cultural, and economic characteristics, the dynamics of job crafting in India may vary from other countries and regions which can also be reflective of how job crafting operates in South Asia in general. As job crafting was conceptualized and later evolved mostly in the western context, our study assumes greater significance as it is the first study which attempts to systematically review the job crafting literature to understand how job crafting manifests in the Indian context and presents a conceptual framework for the same.

This paper investigates how stockist brand equity is created in the unorganized pharma retail sector in emerging markets despite the absence of any proactive brand-building efforts by distribution firms (stockists). In addition, this study also aims to unravel the sources of stockist brand equity.

Drawing from grounded theory, a qualitative research design using semi-structured interviews was conducted, and a theoretical saturation approach was used for this purpose. The retailer–stockist (business-to-business [B2B]) relationship in the Indian pharmaceutical market context was used as the study context.

The findings show that in the absence of any brand-building activities, stockists’ sales representatives play an important role in creating stockist brand awareness. In addition, word of mouth from other retailers also plays a minor role. Rational and emotional brand associations which are strong, favorable and unique are created 1) directly by the services provided by stockists, which includes product availability, deliverability, accuracy in billing and batch numbers, credit facilities and discounts, handling issues such as product expiry, and 2) indirectly by the services provided by stockists’ sales representatives which includes their frequency of visits, proactive customer orientation rather than sales orientation, fulfilling commitments and relationship with retailers. The strength of the retailer–stockist (B2B) relationship also depends on retailer-driven factors and other external factors discussed in this study.

Strong stockist brand equity helps build trust and loyalty with pharmacies, ensuring a consistent and timely supply of medicinal products, which will benefit their end consumers or society.

This study identifies the antecedents determining the strength of the retailer–stockist (B2B) relationship, a precursor for establishing retailer-based stockist brand equity in the unorganized sector. This study also highlights the consequences of establishing strong retailer-based stockist brand equity.

This study aims to attempt to put forward a nuanced conceptual framework to develop a better understanding of the relationship between job crafting and employees’ turnover intention, by bringing in “social exchange theory” perspective and proposing the mediating role of perceived organizational support (POS).

An extant review of literature was undertaken to identify and summarize the relevant findings on the topics of job crafting, POS and turnover intention, which further led to the development of hypotheses and the conceptual framework.

Results indicate that job crafting has a significant relationship with turnover intention with POS as an underpinning variable.

This study generates actionable insights and suggestions for the business leaders and practitioners, which can help them in handling the problem of rising employee turnover in today’s era marked by evolving attitudes and mindsets of the employees, along with the catalytic effects of pandemic and “Great Resignation”. Insights generated in this study for reducing dysfunctional employee turnover would have a stabilizing effect and lead society and business towards sustainable growth.

Insights generated in this study for reducing dysfunctional employee turnover would have a stabilizing effect and lead society and business towards sustainable growth.

This study is original in three ways. Firstly, the study introduces a unique perspective by putting forward a conceptual framework nestled in the social exchange theory. Secondly, to the best of the authors’ knowledge, it is the first study to examine the role of POS as a mediator in the association between job crafting and turnover intention. Thirdly, by proposing POS as an outcome of job crafting, this study suggests a reciprocal relationship between job crafting and POS.

The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

Despite IoT’s huge potential, enterprises’ ability to leverage it is their competitive advantage. Thus, competitive differentiation is primarily predicated on leveraging IoT toward customer needs. To examine the research gap, this study aims to explore the drivers of customer satisfaction and how they are affected by the interaction between IoT capabilities.

A mixed-method research framework is applied to assess the impact of IoT capabilities on customer satisfaction. Based on the theoretical underpinning of the resource-based view and dynamic capability, the study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. First, DEMATEL is used capture the interrelationship between IoT capabilities. Further, the impact of each IoT capabilities on customer satisfaction is studied using CoCoSo method.

The study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. The findings are enriched through the complementarity of resources in a dynamic business-to-business-to-customer (B2B2C) scenario. The authors expand the IoT capabilities from conventional business-to-business (B2B) or business-to-customer (B2C) scenario to tri-nodal B2B2C relationship triangle.

Based on the findings, the authors offer a business transformation strategy for firms in key areas of customer satisfaction by leveraging IoT. The study can help management prioritize and develop key IoT capabilities to meaningfully increase customer satisfaction metrics.

Building on the dynamic capabilities and resource-based view of the firm, an integrated decision-making research model is proposed. In addition, this study investigates the product and service capabilities unlocked using IoT capabilities. This work can be considered one of the leading attempts to improve customer satisfaction using IoT capabilities from traditional dyadic (B2B or B2C) structure to triadic (B2B2C) framework.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This study aims to evaluate the low energy impact characteristics of 3D printed carbon fiber thermoplastic and thermoset polymer composite using the Izod impact test. The effects of infill density are examined on the Izod impact properties of 3D printed thermoset polymer and thermoplastic composite specimens. Furthermore, a thorough investigation is conducted into the effect of heat treatment using a hot-air oven on both types of 3D printed composite specimens. To characterize the impact characteristics of each specimen, the fracture surfaces caused by impact load are inspected, and the fracture mechanism is studied using scanning electron micrographs.

Izod Impact specimens of thermoset (epoxy resin) and thermoplastic carbon fiber of different infill density (70, 75, 80, 85, 90 and 100%) are fabricated using the different fiber impregnation 3D printing process. To carry out the heat treatment process, printing of composites is done for each infill design from both thermoset and thermoplastic composites and the impact characteristics of specimens are evaluated on a pendulum test-rig using the ASTM D-256 standard. Using a scanning electron microscope, each fracture zone underwent four separate scanning processes, ranging in size from 2 µm to 100 µm.

The impact resistance of the 3D printed thermoset and thermoplastic composite material is significantly influenced by the type of fiber placement and infill density in the matrix substrate. Because of the weak interfacial strength between the layers of fiber and polyamide 6, the specimen printed with continuous fiber implanted at the part exhibited reduced impact resistance. At 75% infill density, the impact specimen printed with coextruded fiber showed the highest impact resistance with a 367.02% greater magnitude than the continuous fiber specimen with the same infill density.

This work presents a novel approach to analyze the low energy impact characteristics and three-dimensional printing of carbon fiber reinforced thermoplastic and carbon fiber reinforced thermoset and thermoplastic composite material.