PurposeEnergy-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.Design/methodology/approachTotal 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.FindingsThe 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.Research limitations/implicationsThe 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.Social implicationsVarious 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.Originality/valueUnderstanding 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.

Purpose 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 Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications 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. Originality/value 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.