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The digital learning and learning & development (L&D) at workplaces in corporates is having a significant challenge, where only about 1% of the week is spent on L&D by the employees. There are an array of recent L&D reports–by Deloitte, 2019; Skillsoft's, 2019; LinkedIn Workplace Learning Report-2019; UK L&D Report-2019; FICCI-NASSCOM and EY “Future of Jobs” Report-2017–which have clearly been indicating that the digital learning is fast-emerging as one of the realistic option. The employees invest their time and energy for skilling/up-skilling/re-skilling for remaining relevant to the emerging business context under volatility, uncertainty, complexity and ambiguity (VUCA) world and also coronavirus disease 2019 (COVID-19) is being researched.

The L&D interventions have primary objective of enhancing skills, competencies and career growth among employees, and the learning engagement styles/ systems are undergoing dramatic paradigm shifts. There is dire need to understand the impact of sweeping changes with Industry 4.0 and HR 4.0; however, there are only a few industry-centric studies that are available to assess the impact of technology on L&D with digital learning. Hence, there is a need to study the factors influencing various segments of workforce in large corporates, where the learning engagement with digital learning is fast-emerging among corporates.

Given the digital learning / L&D context in corporates, this research paper has attempted to review and analyse the opportunities, challenges and emerging trends with respect to leveraging technology and innovation to enhance L&D to deliver the business goals, under the 70:20:10 framework, with case analysis of ten different corporates (across different industry sectors) viz., Genpact, Nexval, Airbus, Siemens, AstraZeneca Pharma, HPCL, HGS (BPM), HP, Flipkart and IBM. The A-to-Z of Talent Management and Leadership Development (adopted version from India Leadership Academy, Publicis Sapient, 2019) best practices are analysed, summarized and presented to indicate emerging trends in Industry 4.0 era.

This study has been carried out for just ten major corporates/ multinational companies (MNCs) operating in various sectors. The sample size used is relatively less; therefore, the study can be carried out with a larger sample size and deeper data analysis and insights across countries/continents. At present, this can be considered as a base-research for undertaking deep-dive analysis. The sectoral analysis and cross-industry perspectives require consideration in next studies. To address the sector-specific issues, the research can be undertaken for either a particular sector such as manufacturing, automotive, IT/ITeS, telecom, aviation, agri-tech and pharmaceutical, knowledge-based industries, etc. or comparative analysis across few related sectors required.

This research has provided/shall provide a basis to understand the various factors that influence the L&D and digital learning ecosystem in large corporates. It is expected to provide a practical and also strategic perspective towards effective usage of digital learning systems (both in-house and open systems) for enhancing the effectiveness of L&D in the context of VUCA World and HR 4.0 around us. The proposed hypothesis of “The Digital Learning is the “Future of HR”, especially for the L&D in large Corporate Academies (in the context of Industry 4.0)” stands justified.

The clear shift from training culture to “Learning Culture” is possible and feasible with strategically planned digital learning/ L&D interventions, which benefits the corporates, employees, customers and the society at large.

To the best of our knowledge, probably this is one of the first paper in the analysing the industry best L&D/Digital learning practices from an practitioners and academic perspective, as we live in the era of bit-sized and byte sized micro-learning. This study contributes to the academics by providing insights on possible digital learning policies that can be practiced by large corporates, where the “ownership of learning and career growth” is transferred onto the employees. The result of this study complements the evolving digital learning trends, in line with science of self-driven and lifelong learning principle.

Massive open online courses (MOOCs) are among the most recent e-learning initiatives to gain widespread acceptance among universities. However, despite MOOCs' “much-documented” benefits, many questions are being raised late regarding the long-term sustainability of the open online teaching e-learning model. With high dropout rates in MOOCs courses, recent research has focused on the challenges limiting MOOCs’ growth. But most of the research is directed toward students’ perspectives, leaving the instructors’ perspective. One of the most important aspects of instructors’ perspective is the motivation for MOOCs' development and delivery.

The present study collected the data from 25 MOOC developers of Indian origin. To prioritize or rank the motivational factor behind developing a MOOC, a fuzzy-analytical hierarchical process (F-AHP) technique was applied to the data set. The primary motivational factors considered for the study were professional development, altruism, personal development, institutional development, intrigue, monetary benefits and peer influence.

The results showed that professional development and personal development are two prime motives that drive MOOCs development. Monetary benefits and peer influence were the least important factors among all the factors considered for the study.

Previous studies have identified and modeled the motivational factors that contribute toward developing MOOCs. However, there was little knowledge about the hierarchy among the motivating factors. The present study fills this gap by establishing the ranking of motivational factors responsible for MOOCs development.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0205.

The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment.

A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature.

ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs.

The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability.

Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context.

This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large.

A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.

Agriculture diversification acts as income enhancing as well as distress mitigating strategy. India has witnessed rise in per-capita income which in turn has increased the demand for food particularly high-valued food items but agricultural production has failed to keep pace with the growing demand. The purpose of this paper is to examine spatio-temporal variations in agricultural diversification (AD) in India. Second, the authors try to identify the determinants of AD. Third, the authors examine the convergence hypothesis with reference to agriculture diversification across Indian states.

The study is based on the panel data constituting 20 major states of India during 1990–1991 to 2013–2014. It uses Simpson Diversification Index to measure AD. The heteroskedasticity-corrected panel regression model is applied to find out the determinants of AD. The fixed-effects model is used to examine β-convergence in AD across the sample states. Alternative time series models are applied to examine σ-convergence in AD.

The rising per-capita income and urbanization are driving dietary diversity towards high-valued crops and providing ample opportunity for AD. But poor and inadequate cold storage facility and rising cost of cultivation are posing major hindrance to it. Small land holding and road length have negatively influenced AD which is contrary to the traditional wisdom. The study found divergence in diversification and rising inequality in diversification.

The study is based on secondary data. A primary study to complement this could have been better. It is only based on one country.

Food inflation has serious adverse effect on the society at large. It is necessary to promote AD for controlling food price inflation. Minimum support price provided by the government should be extended to all crops; otherwise, it will fuel inflation. Given the fact fragmentation of land holding is adversely affecting AD, community based farming and consolidation of farm land should be the way forward to improve farmers’ income as well as reduce risk.

To best of the authors’ study, this is the first study that examines determinants of AD and convergence in AD during the high growth period of India.

The purpose of this paper is to introduce and provide an overview of the various issues related to Green Supply Chain Management (GSCM) and suggest further scope and direction of research in this emerging field.

The work relies on experiences, case studies and other literature related to GSCM. Literature has been segregated to understand various GSCM issues. A detailed review is used to sort out the literature and develop the research direction of the study. The review is focussed on development of GSCM including all those researchers which is relevant to environmental and social sustainability toward operation management and the supply chain. A literature review seems to be a valid approach, as a necessary step in structuring a research field.

The objectives of this paper are to identify major research work conducted on GSCM and to classify them to identify gaps in literature and opportunities for future research. The paper has provided an integrative framework for study, design, implementation and GSCM performance. The findings also identify a number of issues that need to be addressed.

Implication of the work is that the knowledge of the research gap can be used to focus efforts on key areas so as to ensure speedy and comprehensive implementation of GSCM practices.

The paper may prove to be a very useful source of information to practitioners and regulators in their green practices implementation programs.

This paper provides some of the very first insights into development of GSCM theories. The methodological review will provide better understanding of the current state of research in the discipline.

The purpose of this paper is to measure technical efficiency of Indian thermal power sector employing the recent by-production approach.

The by-production approach is used in conjunction with data from the Central Electricity Authority (CEA) of India to compute the output-based Färe, Grosskopf, Lovell (FGL) efficiency index and its decomposition into productive and environmental efficiency indexes for the ITPPs

The authors show that given the aggregated nature of data on coal reported by CEA, CEA’s computation of CO₂ emissions through a deterministic linear formula that does not distinguish between different coal types and the tiny share of oil in coal-based power plants, the computed output-based environmental efficiency indexes are no longer informative. Meaningful measurement of environmental efficiency using CEA data is possible only along the dimension of the coal input. Productive efficiency is positively associated with the engineering concept of thermodynamic/energy efficiency and is also high for power plants with high operating availabilities reflecting better management and O&M practices. Both these factors are high for private and centrally owned as opposed to state-owned power-generating companies. The example of Sipat demonstrates the importance of (ultra)supercritical technologies in increasing productive and thermodynamic efficiencies of the ITPPs, while also reducing CO₂ emitted per unit of the net electricity generated.

This paper uses the by-production approach for the first time to measure technical efficiency of ITPPs and highlights how the nature of the Indian data impacts on efficiency measurement.