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PurposeHigher education management institutions play a vibrant role in imparting managerial skills to the students to face the corporate world. Performance evaluation of such institutions is mandate to ensure the outcome quality. To establish this, the present research explored the critical performance indicators of management institutions using the balanced scorecard (BSC) approach.Design/methodology/approachThis research explored the critical performance indicators of public, private and standalone management institutions in India. Data were collected from the representative sample of all the stakeholders in those management institutions. A specific vision was created and a systematic procedure was employed to arrive at the objectives, measures and metrics of the scorecard specific to the vision. Confirmatory factor analysis was used to perform analysis on the collected data. For the objectives and measures that evolved from confirmatory factor analysis, metrics were formulated based on the expert opinion.FindingsThe study resulted in 16 objectives, 46 measures and 54 metrics encompassing all the four perspectives of BSC. This paper has contributed a concrete, concise, comprehensive and context specific framework.Research limitations/implicationsThe nature of the BSC framework paves the way for continuous assessment and eventually helps the institutions to attain sustainable growth. This research contributes to the literature of balanced scorecard and also to the performance assessment of the management institutions.Originality/valueBSC-based benchmarking is a unique contribution to the academia of management education to precisely measure the performance of institutions. The model comprehensively includes the indicators from all the perspectives of stakeholders in terms of objectives, measures and metrics, thus proposing a holistic assessment.

Purpose Second-hand/used goods channels compete with existing traditional channels to satisfy consumers’ needs that are unmet by traditional retail networks. However, most studies on online shopping have largely ignored online second-hand/used good purchases. This study aims to use Thaler’s mental accounting model, principal–agent perspective and contamination theory to highlight the differences in the value sought by online new goods and second-hand shoppers. Design/methodology/approach A conceptual framework linking perceived uncertainty, perceived acquisition value and e-loyalty was developed and tested using structural equation modelling. The moderating effects of product type (new vs second-hand) and frugality were also included. Findings The paper found strong support for the model. Results showed that online second-hand shoppers were more uncertain and perceived lesser levels of acquisition value when compared to new goods shoppers. They were also less frugal. Online shoppers are also more likely to buy products with sensory attributes (experience goods) in new goods websites and products with non-sensory attributes (search goods) from second-hand websites. The authors recommend various ways in which managers can increase perceived value for the online shopper. Research limitations/implications Future studies can extend this investigation by including transaction value or other hedonic values to verify their impact on acquisition value and e-loyalty. While the authors found support for the notion that consumers who buy used goods online are less frugal, there is some research that could point to the opposite. Hence, research can investigate this topic in depth in more countries to throw more light on this. Practical implications To sustain themselves in a competitive online market, retailers need to understand the value sought by consumers. This study provides empirical evidence of the importance of acquisition value for new goods and second-hand shoppers. Originality/value No recent research has compared the value sought by online second-hand and new goods shoppers. This study contributes to the understanding of the acquisition value perceived by consumers in online new goods and second-hand shopping channels.

Purpose Previous green research shows conflicting results regarding the relationship between environmental concern and persuasion. It has also largely overlooked the role of situational involvement. The purpose of this paper is to aim to show that message involvement influences attitude towards green advertisements. Design/methodology/approach To test the proposed model, an experimental study based on a sample of young adults in an emerging economy was used to investigate if situational involvement can produce favourable attitudes towards green advertisements. Findings Using PLS, it was found that fear and response efficacy increased message involvement which in turn was a significant predictor of attitude towards the advertisement. The findings show that advertisers can use these variables to increase message involvement. Originality/value This research extends previous studies on message involvement and expands current knowledge by showing that situational involvement predicts attitude towards green advertising.

PurposeRenewable energy sources are likely to play a major role in meeting the future energy requirement of a developing country like India. Among the various renewable energy sources, the bio‐energy plays a key role for the power generation. In this paper, an attempt is made to develop a fuzzy based linear programming optimal electricity allocation model (OEAM) that minimizes the cost and determines the optimum allocation of different energy sources for the centralized and decentralized power generation in India with special emphasis to bio‐energy.Design/methodology/approachThe OEAM model optimizes and selects the appropriate energy options for the power generation on the factors such as cost, potential, demand, efficiency, emission and carbon tax. The objective function of the model is minimizing the cost of power generation. The other factors are used as constraints in the model. The fuzzy linear programming optimization approach is used in the model.FindingsThe extents of energy sources distribution for the power generation in the year 2020 would be 15,800 GWh (4 per cent) from the coal based plants, 85,400 GWh (20 per cent) from the nuclear plants, 191,100 GWh (44 per cent) from the hydro plants, 22,400 GWh (5 per cent) from the wind mills, 45,520 GWh (11 per cent) from the biomass gasifier plants, 14,112 GWh (3 per cent) from the biogas plants, 8,400 GWh (2 per cent) from the solid waste, 33,600 GWh (8 per cent) from the cogeneration plants and 11,970 GWh (3 per cent) from the mini hydel plants, respectively.Originality/valueThe OEAM has been developed for the electricity demand allocation for the year 2020. An extensive literature survey revealed that carbon tax and emission constraints were never used in the previous models and they are considered in the present model.