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Energy-efficiency leads to productivity gains as it can lower operating and maintenance costs, increase production yields per unit of manufacturing input and improve staff accountability. Implementation of energy-efficient technologies amongst industries, the factors influencing them and the barriers to their adoption have been the subject of several studies during the past three to four decades. Though energy-use behaviours of individuals or households are sufficiently explored, industrial energy conservation behaviour is scarcely studied. This study identifies the relationship between the different behavioural elements to open up a door for behaviourally informed intervention research.

Total interpretive structural modelling technique was used to determine the relationship between different elements of the behaviour of energy managers. Expert responses were collected to understand the relationship between the behavioural elements, through telephone interviews.

The study identified the relationship between the behavioural elements and found imperfect evaluation as the key element with the highest driving power to influence other elements.

The authors postulate that a behaviourally informed intervention strategy that looks into the elements with high driving power such as imperfect evaluation, lack of focus on energy-saving measures and the lack of sharing energy-saving objectives can lead to: an increase in the adoption of energy efficiency measures and thereby a reduction in the energy efficiency gap; greater productivity gains and reduced greenhouse gas (GHG) emissions; Preparation of M&V protocol that incorporates behavioural, organisational and informational barriers.

Various policy level interventions and regulatory measures in the energy field which did not address the behavioural barriers are found unsuccessful in narrowing the energy-efficiency gap, reducing the GHG gas emissions and global warming. Understanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement.

Understanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement. This study argues that through the systematic analysis of the imperfect evaluation of energy audit recommendations, it is possible to increase the adoption of energy efficiency measures that can lead to greater productivity gains and reduced GHG emissions.

In the realm of energy behaviour studies, very little research has been done to understand industrial energy behaviour (IEB) that influences the willingness to adopt (WTA) energy-efficient measures. Most of the studies on energy behaviour were focused on the residential and commercial sectors where the behaviour under investigation was under volitional control, that is, where people believe that they can execute the behaviour whenever they are willing to do so. The purpose of this paper is to examine the factors influencing the industry’s intentions and behaviour that leads to enhanced adoption of energy efficiency measures recommended through energy audits. In particular, this paper aims to extend the existing behaviour intention models using the total interpretive structural modelling (TISM) method and expert feedback to develop an IEB model

TISM technique was used to determine the relationship between different elements of the behaviour. Responses were collected from experts in the field of energy efficiency to understand the relationship between identified factors, their driving power and dependency.

The results show that values, socialisation and leadership of individuals are the key driving factors in deciding the individual energy behaviour. WTA energy-saving measures recommended by an energy auditor are found to be highly dependent on the organisational policies such as energy policy, delegation of power to energy manager and life cycle cost evaluation in purchase policy.

This study has a few limitations that warrant consideration in future research. First, the data came from a small sample of energy experts based on a convenience sample of Indian experts. This limits the generalizability of the results. Individual and organizational behaviour analysed in this study looked into a few select characteristics, derived from the literature review and expert feedback, which may pose questions about the standard for behaviours in different industries.

Reasons for non-adoption of energy audit recommendations are rarely shared by the industries and the analysis of individual and organisational behaviour through structured questionnaire and surveys have serious limitations. Under this circumstance, collecting expert feedback and using the TISM method to build an IEB model can help to build strategies to enhance the adoption of energy-efficient measures.

Various policy level interventions and regulatory measures in the energy field, adopted across the globe, are found unsuccessful in narrowing the energy-efficiency gap, reducing the greenhouse gas (GHG) emissions and global warming. Understanding the key driving factors can help develop effective intervention strategies to improve energy efficiency and reduce GHG emissions.

The industry energy behaviour model with driving, linking and dependent factors and factor hierarchy is a novel contribution to the theory of organisational behaviour. The model takes into consideration both the individual and organisational factors where the decision-making is not strictly under volitional control. Understanding the key driving factor of behaviour can help design an effective intervention strategy that addresses the barriers to energy efficiency improvement. The results imply that it is important to carry out post energy audit studies to understand the implementation rate of recommendations and also the individual and organisational factors that influence the WTA energy-saving measures.

This study aims at understanding how higher education institutions (HEIs) can contribute to sustainable development, by designing their programmes for bringing about a transformative impact on communities and students, and also to examine what alternative pedagogical approaches could be used for that. In the past decades, HEIs have increasingly created social innovation (SI) programmes, as a way to achieve United Nations Sustainable Development Goals. These community-oriented and field-based programmes are difficult to ally with conventional classroom education. This study explores how these programmes could integrate the participatory approach and what would be the benefits. It also investigates the effectiveness of the experiential learning approach for teaching sustainability.

A case study method is used to document SI projects initiated by an HEI programme in rural India.

It was found that the participatory approach contributes to empowering communities and also benefits the students in terms of academic, professional and personal growth. Empirical findings show that experiential learning is an efficient method to teach sustainability. Ultimately, both pedagogical approaches are found to be mutually beneficial.

This study fills a gap in the literature, by providing empirical evidence on how HEI can implement innovative educational strategies such as participatory approach and experiential learning in their programmes towards teaching sustainability. A conceptual model for HEI interested in developing similar programmes is also proposed. To the best of the authors’ knowledge, this study is one of the first studies focusing on the context of Indian HEI.

The study aims to study the extent of research done in luxury marketing in an emerging economy like India by conducting a bibliometric analysis. A period of 21 years has been considered to present a comprehensive picture for results and analysis. Key findings indicate the gaps and scope of further research for academics in India and abroad. The findings indicate a dearth of research by scholars and academicians in luxury, counterfeit and masstige, especially when there is a surge of the upper middle class in India. More specifically, Indian-grown luxury brands also present a massive scope for future research.

To describe the Pragathi Bandhu Groups (PBG) Model and portray the performance of PBG farmers encouraged by their financial and decision-making participation in micro financing and labor sharing, as well as to analyze the factors that influenced participation of small farmers in PBG.

The empirical study is confined to the small farmers and laborers of PBG functioning in the coastal districts of Karnataka State in India. The study is conducted in Belthangady and Bantwal Taluks of Dakshina Kannada (DK) Districts-Udupi taluk of Udupi District in the State of Karnataka. Primary data from 100 farmer members, selected at random in each of the Taluks, is collected through personal interview by administering semi-structured interview schedules and open discussion. In addition, the data on the functions and the performance of PBG in the State of Karnataka in India are also collected from the official records of Shri Kshetra Dharmasthala Rural Development Project (SKDRDP) and their field-level functionaries through informal discussions. Factor analysis is performed with principal component analysis followed by Varimax rotation to analyze the factors that influenced participation of small farmers in PBG.

Results show that the implementation of PBG Model, through the collective participation of small farmers in micro financing, free labor sharing, financial and decision-making activities underlying the functions of PBG Model, has helped them to achieve robust performance in terms of increased savings mobilization, loan utilization, and value of free labor sharing and acres of land brought under cultivation with the help of irrigation facilities created by them. The factor analysis has derived four factors that influenced the participation of farmers in PBG (agriculture development, financial participation, capacity building, and other benefits) which explain 63.701 of total factor variance.

The findings of this paper can benefit the small farmers and laborers in replicating the PBG Model and its initiatives that address shortages of labor and credit, as well as the high cost of labor, particularly in the unorganized sector in the agrarian economy.

The insights offered are likely to be beneficial to the distressed small farmers, development agencies, and agriculture policy makers to solve the agrarian crisis caused due to shortages of labor and farm credit.

Reducing income gap and enhancing welfare of people can be achieved through improved performance regarding socio-economic indicators, which can support sustainable development. People living in villages tend to migrate to the cities with the hope of enhancing their income. People living in cities and income gap among the people in the cities are expected to increase further. Enhanced living conditions in villages can reduce the need for this migration and increase welfare of the people in the villages. This chapter aims to examine drivers for sustainable smart villages and ways for enhancing and supporting their effectiveness in socio-economic development, in enhancing welfare and living conditions of people living in villages, and in reducing income gap between urban and rural people. This chapter investigates potential and roles of sustainable and smart villages in enhancing welfare and living conditions of people living in villages. Furthermore, this chapter emphasises the villages’ roles in sustainable development as well as importance of transformation of villages into the sustainable and smart ones and establishment of sustainable and smart villages so that convergence, social inclusion and socio-economic indicators can be supported. This chapter explains drivers for transformation of the villages into the sustainable and smart ones as well as establishment of new sustainable and smart villages. This chapter provides recommendations for sustainable and smart villages effective in contributing to sustainable and socio-economic development. This chapter can be useful to urban planners, construction industry stakeholders, policy makers and researchers.

Asset liability management is a multi-dimensional set of activities. Against this backdrop, the purpose of this paper is to build a goal programming model for optimally determining the asset allocation and liability composition for Indian Banks.

The conceptual model framework has been developed and then tested for four banks that typically represent the Indian banking sector. Published balance sheet data were used for the model that span over 1995-2009. The veracity of the model has been tested in terms of its ability to project the optimum asset allocation and liability composition for the year 2010.

The model has been able to generate the optimum asset and liability mix that meets the goals set on the key drivers. The solution provided is realistic and compatible with the actual figures. Sensitivity analysis including current and savings account and interest rate changes has been successfully performed to study impact they cause on profitability.

The model provides an overall approach to asset allocation and liability composition based on past data reflecting the preferences and priorities of the banks with regard to their outlook on setting targets. This may change. The variables like return and risk are stochastic in nature.

The model demonstrated in this paper would be useful to the policy makers in any bank for decision support and planning in view of its ability to incorporate a large number of constraints. Changes in profit could be instantaneously captured through sensitivity analysis.

The goal programming model used here is invariant to the type of bank and year of consideration.

Introduction of organic farming and reduced dependence on inorganic family has positive implications for the economic viability of the small farming units in India’s agricultural sector. The privatization of access to seeds, fertilizers and other inputs, as an outcome of economic liberalization, has deeply dented the affordability of agriculture, especially at the level of small and micro farms, thereby undermining the export potential of the agricultural sector as well. Zero budget farming can be potentially effective in weathering out the affordability crisis and restoring economic viability of farming at large. This chapter is a moderate attempt to explore how a shift towards zero-budget farming from inorganic farming can improve the access of agricultural goods, as predominant component of their export basket, to the markets in developed countries (DCs), given the rising stringency of sanitary and phytosanitary and environmental standards, as well, coming disproportionately against the exports from developing and less-developed countries (LDCs), including India. With reference to Balassa’s the measure of revealed comparative advantage, the chapter points to the dwindling competitive advantage of India’s agriculture in global market during the last decade. Given the stringent environment standard facing, disproportionately, the exports from LDCs, zero-budget farming can improve the access of agricultural goods, as predominant component of their export basket, to the markets in DCs. The benefit from the creation of public goods by means of organic farming practices can be utilized to deepen the fiscal support to small and marginal farms.

Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.

This study aims to ascertain the impact education for sustainable development (ESD) on knowledge, attitude and behavior among senior (last year undergraduate) Indian students.

This study is a cross-sectional causal investigation with descriptive design using survey method. It uses the knowledge, attitude and behaviors toward the sustainable development scales of the International Institute of Sustainable Development (IISD) for data collection and partial least-square structural equation modelling for empirically testing the proposed research model.

The study results establish the importance of sustainability education on knowledge, attitude and behavior. The impact of knowledge on behavior is direct and partially mediated through the attitudinal pathway – indicating central and peripheral routes sustainability related information processing and attitude formation. The results also indicate poor knowledge of the impact of socio-cultural issues on sustainability.

The study results will help assess the impact current ESD initiatives in India and redesign the ESD curriculum in line with ESD for 2030 initiatives to facilitate the achievement of UN’s sustainable development goals by 2030. This study is limited to senior students and offers scope for extension to other groups.

The study results can help redesign the sustainability curriculum at the school and undergraduate levels in the country in line with ESD for 2023 objectives. The results indicate an urgent need to include the impact of sociocultural issues on sustainability to sensitize Indian students towards the same.

This study contributes to the sustainability education literature by assessing the impact of sustainability curricula on sustainability knowledge, attitude and behavior among Indian senior students – hitherto uninvestigated. It also will help in the informed redesign/ improvement of ESD curriculum under the ESD for 2030 initiatives.