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Higher 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.

This 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.

The 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.

The 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.

BSC-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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to understand the impact of transformational leadership (TL) on follower emotional intelligence (EI) and examine the potential mediation role played by follower EI in the relationship between TL and follower outcomes (i.e. growth satisfaction in the job and job stress (JS)).

Data were obtained through survey using questionnaire collected from 908 employees who worked across six different sectors, i.e. manufacturing, IT, healthcare, hospitality, educational and public services in Southern India. The mediation model proposed in this study was tested using structural equation modelling and bootstrapping method.

The relationship between TL and Follower EI was significant. Follower EI was found to partially mediate the relationship between TL and followers’ growth satisfaction in job. Contrary to expectations, the follower EI did not significantly predict JS in this study and hence the follower EI did not mediate in the proposed model. However, follower EI and growth satisfaction in the job jointly mediated the relationship between TL and follower JS fully.

Self-report bias about supervisors’ TL behaviours and followers’ own EI assessment and collection of data from the mono-source (subordinate self-report) might have impacted the results of this study. Moreover, some items were negatively worded and reverse coded as cognitive speed bumps to restrain the respondent’s tendency to rush through answering the survey questionnaire.

This study established a partial and joint mediation of follower EI on the relationship between TL and follower outcomes. Basing on these findings, this study highlights the need for the practitioners to better understand the importance of EI training for the leaders in the organisations for obtaining better outcomes in the followers.

The study establishes the fact that the attunement of transformational leaders’ EI and follower EI help leaders as well as followers to guide their behaviour towards positive outcomes.

This study is among the first to examine the impact of TL on follower EI and the potential mediation of follower EI between TL and follower outcomes. From a theoretical perspective, this study is one step closer to fully understand the intervening process between TL and follower outcomes.

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.

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.

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.

This research extends previous studies on message involvement and expands current knowledge by showing that situational involvement predicts attitude towards green advertising.

Renewable 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.

The 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.

The 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.

The 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.

Fuel demand forecast is a fundamental tool to guide private planning actions and public policies aim to guarantee energy supply. This paper aims to evaluate different forecasting methods to project the consumption of light fuels in Brazil (fuel used by vehicles with internal combustion engine).

Eight different methods were implemented, besides of ensemble learning technics that combine the different models. The evaluation was carried out based on the forecast error for a forecast horizon of 3, 6 and 12 months.

The statistical tests performed indicated the superiority of the evaluated models compared to a naive forecasting method. As the forecast horizon increase, the heterogeneity between the accuracy of the models becomes evident and the classification by performance becomes easier. Furthermore, for 12 months forecast, it was found methods that outperform, with statistical significance, the SARIMA method, that is widely used. Even with an unprecedented event, such as the COVID-19 crisis, the results proved to be robust.

Some regulation instruments in Brazilian fuel market requires the forecast of light fuel consumption to better deal with supply and environment issues. In that context, the level of accuracy reached allows the use of these models as tools to assist public and private agents that operate in this market.

The study seeks to fill a gap in the literature on the Brazilian light fuel market. In addition, the methodological strategy adopted assesses projection models from different areas of knowledge using a robust evaluation procedure.

This study aims to explore how transformational leaders enhance public service employees’ growth satisfaction in the job and mitigate job stress and burnout, incorporating follower interpersonal communication satisfaction with the leader (IPCSL) as a mediator.

On the basis of the survey data collected from the Indian public service employees, regression analysis, bootstrapping and SOBEL test are used to test the proposed research model.

The findings highlighted a partial mediation of follower interpersonal communication satisfaction with leader between transformational leadership (TL) and public service employees’ growth satisfaction in the job. Although there was no significant direct effect of TL on job stress and burnout, the results underlined a significant indirect effect of follower IPCSL.

By examining the important role of follower IPCSL, this study unravels the precise intervening mechanism between TL and follower affective outcomes like growth satisfaction in job, job stress and burnout among public service employees.

Embedded in higher educational settings, work-integrated learning (WIL) is a key reflection to students' perceived employability. The purpose of this study is to explore the antecedents of internal and external perceived employability. The research attempts to test a theoretical model examining the relationships among human capital, work values, career self-management, internal perceived employability and external perceived employability.

Data were collected from 588 students who have internship experience from two self-financing higher education institutions in Hong Kong. We adopted structural equation modelling (SEM) to test the proposed research hypotheses.

Results support the idea that human capital and intrinsic work values are significant antecedents of perceived employability. Furthermore, this relationship is fully mediated by career self-management. The implications of the findings for understanding the process through which psychological variables affect an individual's perceived employability are discussed.

Previous studies have extensively examined the effectiveness of WIL in increasing graduates' employability. However, unclear focus has been given to examine psychological attributes, such as human capital, work values and career self-management in WIL. In addition, few researchers have empirically examined the linkages among human capital, work values, career self-management and employability through internships or WIL experiences. Therefore, to bridge these gaps, the present study examines the effect of human capital, work values and career self-management on students' perceived employability when gaining internships or WIL experiences in a higher education setting.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

The purpose of this paper is to analyze and explore the effectiveness of using balanced scorecard (BSC) as a management tool in the evaluation of the performance and thereby the quality services of a University.

The survey was conducted among the various faculties and departments Sohar University through a well-defined questionnaire. The collected primary data were compiled, analyzed and a critical analysis was carried out using a null hypothesis, χ², and regression analysis.

Our empirical result clearly reveals that there is a strong association exists between the strategic road map and the four perspectives of BSC.

The study was carried out in Sohar University, Oman and the same can be extended to all the higher educational institutions globally.

The study reveals that the key performance measures identified under the classification the four perspectives of BSC, play a crucial role in establishing the framework based on the strategic map determined by the vision and the mission of the University.

The study demonstrates that the BSC concept can be introduced to an educational institution to fill up the gap created between the creation of strategy and implementation of the same.

No study has investigated the performance evaluation of a higher education institution in Oman, using the four perspectives of BSC, and this is a first hand study.