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This paper aims to propose a new benchmarking framework that uses a series of existing intuitive and analytical methods to systematically capture both objective data and subjective beliefs and preferences from a group of decision makers (DMs).

The proposed framework combines the excellence model developed by the European Foundation for Quality Management with the Rembrandt method, the entropy concept, the weighted‐sum approach, and the theory of the displaced ideal. Hard data and personal judgments are synthesized to evaluate a set of business units (BUs) with two overall performance scores plotted in a four quadrant model.

The two performance scores are used to benchmark the performance of the BUs in accordance with their Euclidean distance from the “ideal” BU. Quadrants are used to classify the BUs as efficacious, productive ineffectual, proficient unproductive, and inefficacious. The efficacious BUs, referred to as “excellent”, fall in the competency zone and have the shortest Euclidean distance from the ideal BU relative to their peers.

The benchmarking framework presented in this study has some obvious attractive features. First, the generic nature of the framework allows for the subjective and objective evaluation of a finite number of BUs by a group of DMs. Second, the information requirements of the framework are stratified hierarchically allowing DMs to focus on a small area of the large problem. Third, the framework does not dispel subjectivity; it calibrates the subjective weights with the objective weights determined through the entropy concept.

The purpose of this paper is to find excellent banks on the basis of identified variables. First of all, banks are evaluated based on operation costs, deposits, staff, investments, net profit, and loans variables. Subsequently, these variables are categorized into inputs and outputs. The performances of the banks based on these variables are analyzed by data envelopment analysis (DEA) method to find efficiency and inefficiency of decision making units (DMUs).

This research is aimed to determine the best banks based on predetermined indicators. The indicators are categorized into inputs and outputs. DEA method is used to find efficiency and inefficiency of DMU. However, the aim is to find the efficient banks and to implement the model by using AP Super Efficiency method in order to find the most efficient unit for benchmarking. However, some inputs and outputs have more priority for banks than the others, as a result it will require some changes.

The results indicate that among 13 banks, including ten public and three private, solely five public banks are efficient. Moreover, DEA is used as a benchmarking tool for inefficient banks to be efficient. Among these banks ten of them are public banks and three are private. Among efficient ones, all are public banks. Moreover, five of public banks and three of private are inefficient.

In some cases, inputs and outputs have more priority for DMs than the others, as a result it will require some changes. Also, if one of the inputs or outputs is larger in number than the others, the DMU becomes efficient, despite its low priority. Thus, for solving this problem, the indicators of this research are ranked by Rembrandt method considering the existing ones to find the best banks (best DMU) based on their performance and the relevant indicators.

THE monumental History of Criticism by Professor Saintsbury, and Mr. Hall Caine's lighter series of studies would be sufficient to put anyone on their guard against accepting as final many of the critical decisions of the important literary reviews. Mr. Caine's book particularly is a revelation of error and spite such as makes one wonder that anonymous literary criticism should be received with toleration by bookmen.

The purpose of this paper is to develop a systematic approach to extract users’ feelings and emotions about their experiences in hospitals from online reviews and rank the places using multi-criteria decision making (MCDM) techniques based on the aggregated sentiment score.

The proposed model used a linguistic approach to extract the sentiment words from the free text. It used term frequency-inverse document frequency values to represent features of various places in bag-of-words format. Sentiment dictionary is used to calculate senti-scores. It used different MCDM techniques like simple additive weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods for ranking hospitals based on their aggregated senti-score.

Statistical correlation analysis between the rankings of places reveals that the TOPSIS method is the most suitable ranking technique among other MCDM techniques. By improving the senti-score, one can bring their enterprise to the top position.

Data set is collected from different websites like Twitter, Facebook, etc., for various services/features. Moderate amount of reviews are collected for each place. But not all users give their views on the social media websites. It would be essential to collect responses from all the customers who avail different services at different places.

The sentiment analysis model proposed in this paper enables B2C and C2C commerce. Business may take suitable measures to overcome their issues/problems raised by the consumer. Consumers can share and educate other consumers about their experiences.

The development of internet has strong influence in all types of industries like healthcare. The availability of internet has changed the way of accessing the information and sharing their experience with others. This paper recognizes the use and impact of social media on the healthcare industry by analyzing the users’ feelings expressed in the form of free text. A suitable decision-making technique is applied to rank the places, which enables the users to plan their treatment place in a better way.

The paper develops a novel approach by applying the TOPSIS method to rank the different alternative places of the healthcare industry by using the senti-score derived from the users’ feelings, emotions and experiences expressed in the form of free text.

With the growing importance of service quality in Indian retail, it becomes critical for the retailers to identify the appropriate dimensions for their retail stores. In the process of evaluating service quality the decision maker is often faced with ambiguities due to the imprecise information gained from the respondents. The purpose of this paper is to present an integrated fuzzy (fuzzy analytic hierarchy process (FAHP) approach to help the decision makers/retailers in practicing and judging the priorities of service quality strategies and accordingly benchmarking retail stores in Indian retail environment.

The study incorporated the five basic dimensions of Retail Service Quality Scale proposed by Dabholkar et al. (1996) and the FAHP approach to three leading apparel retail stores of a major city (Rourkela) of Orissa (an Indian state located in eastern part of the country) to determine the weights of criteria and sub-criteria of retail service quality.

The study identified that the dimensions, namely, personal interaction, physical aspects, reliability and policy are perceived as important by the Indian consumers. Merchandise and the store’s willingness to handle returns and exchanges emerge as the most influencing variable affecting the overall service quality of the store.

The study was restricted to a major city of Orissa and to three apparel stores. The results obtained may not be extrapolated to the country as a whole. The authors believe that the integrated approach of FAHP could be used by a variety of service industries to evaluate the service quality. The study did not investigate switching behavior among the respondents as they had been visiting all the three apparel stores during the preceding months.

The integrated approach of FAHP makes an empirical contribution to the service quality and retail marketing literature by overcoming the uncertainty of concepts those are associated with human beings’ subjective judgments.

The retailer can improve the quality of service provided by them based on the parameters important in Indian context, which will lead to higher customer satisfaction.

This paper can help the retail service providers to identify which of the retail service quality dimensions requires much attention to create sustainable competitive advantage.

The Indian power sector is dominated by coal. Environmental awareness and advances in techno-economic front have led to a slow but steady shift towards greener alternatives. The distributions of both fossil fuel resources and renewable energy potential are not uniform across the states. Paper attempts to answer how the states are performing in the sector and how the renewable energy and conventional resources are affecting the dynamics.

The authors employ a two-stage data envelopment analysis (DEA) to rank the performance of Indian states in the power sector. Multi-stage analysis opens up the DEA black-box through disaggregating power sector in two logical sub-sectors. The performance is evaluated from the point-of-view of policy formulating and implementing agencies. Further, an econometric analysis using seemingly unrelated regression equations (SURE) is conducted to estimate the determinants of total and industrial per-capita electricity consumption.

Efficiency scores obtained from the first phase of analysis happens to be a significant explanatory variable for power consumption. The growth in electricity consumption, which is necessary for economic wellbeing, is positively affected by both renewable and non-renewable sources; but conventional sources have a larger impact on per-capita consumption. Yet, the share of renewables in the energy mix has positive elasticity. Hence, the findings are encouraging, because development in storage technologies, falling costs and policy interventions are poised to give further impetus to renewable sources.

The study is one of the very few where entire spectrum of the Indian power sector is evaluated from efficiency perspective. Further, the second phase analysis gives additional relevant insights on the sector.

The purpose of this paper is (1) to review, analyze and assess the existing literature on lean tools selection studies published from 2005 to 2021; (2) to identify the limitations faced by previous studies; and (3) to suggest future works that are necessary to facilitate the selection of lean tools.

A systematic approach was used in order to identify, collect and select the articles. Several keywords related to the selection of lean tools were used to collect articles from different Scopus indexed journals. Next, the study systematically reviewed and analyzed the selected papers to identify the lean tools' selection method and discussed its features and limitations.

An analysis of the results showed that previous studies have adopted two types of methods for selecting lean tools. First, there are various traditional methods being used. Second, multi-criteria decision-making (MCDM) methods were commonly used in previous studies, such as the multi-objective decision-making method (MODM), single multi-attribute decision-making (MADM) methods and hybrid (MCDM). Moreover, the study revealed that the lean tools' selection methods in previous studies were based on evaluating the relationship between either lean tools and performance metrics or lean tools and waste, or both.

In terms of its theoretical value, the study is considered as an extension of the previous researches performed on this topic by determining and analyzing the features of the most selection methods of lean tools. Unlike previous review papers, this review had considered discussing and analyzing the characteristics and limitations of these methods. Section 2.2 of this paper reviewed some of the categories of MCDM methods as well as some of the traditional methods used in the selected previous studies. Section 2.1 of this paper explained the concept of lean management and its application benefits. Further, only three sectors were covered by the previous studies in this review paper. This study also provided recommendations for future research. Therefore, it provided researchers with a good conception of how to conduct the studies on lean tools selection. Besides, knowing the methods used in previous studies can help researchers develop new methods to select the best set of lean tools. That is, this study provided and advanced the existing knowledge base for researchers concerning lean tools selection, especially there is limited availability of review papers on this topic. Moreover, the study showed researchers the importance of the relationship between lean tools and indicators or/and performance indicators to determine the appropriate set of lean tools so that the results of future studies will be more realistic and acceptable.

Practically, manufacturers face a significant challenge when selecting proper lean tools. This study may enhance managers, manufacturers and company's knowledge to identify most of the methods used to choose the best set of lean tools and what are the advantages, disadvantages and limitations of these methods as well as the latest studies that have been adopted in this topic. That means this study can direct companies to prioritize the application of lean tools depending on either the manufacturing performance metrics or/and manufacturing wastes so that they avoid incorrect application of lean tools, which will add more non-value added activities to operations. Therefore companies can decrease the time and cost losses and enhancing the quality and efficiency of the performance. Correctly implementing the best set of lean tools in companies will lead in general to correctly applying lean management in corporations. Therefore, these lean tools can boost the economic aspect of companies and society through reducing waste, improving performance indicators, preserving time and cost, achieving quality, efficiency, competitiveness, boosting employee income and improving the gross domestic product. The correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. Besides, the correct lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right selection of lean tools will have positive effects on both the environment and society. The study may also encourage manufacturers and researchers to adopt studies on lean tools selection in small- and medium-sized companies because the study referred to the importance and participation of these kinds of companies in a large proportion of the economy of developing countries. Further, the study may encourage some countries that have not previously adopted this type of study, academically and industrially to conduct lean tools selection studies.

As mentioned previously, the correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. The proper lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right choice of lean tools will positively affect both the environment and society.

The study expanded the efforts of previous studies concerning lean management features. It provided an accurate review of most lean tools selection studies published from 2005 to 2021 and was not limited to the manufacturing sector. It further identified and briefly described the selection methods concerning lean tools adopted in each paper.

Service quality is an evaluation of how well a delivered service meets customers’ expectations. The purpose of this paper is to provide a reliable scale of measurement for service quality in banks.

The SERVQUAL model was adopted based on a Banking Service Quality (BSQ) model and a two-phase multiobjective optimization model was designed. A structured questionnaire with five-point Likert scale was administered with a 93 percent response rate of 270 sample size. A total of 22 variables were considered based on the BSQ model and the significance of these variables to customers’ satisfaction were investigated. Factor analysis was used to extract the most influential factors on the measure of service quality and four factors were selected namely: they deliver when promised, precision on account statements, queues that move rapidly and sufficient number of ATMs per branch. In order to determine the reliability of the multiple Likert questions in the survey, Cronbach’s α was used indicating a scale reliability of 0.743. Moreover, multiple regression analysis was carried out on the selected factors to design an objective function for the design and evaluation of service quality model. The model design used for benchmarking was done using multiobjective genetic algorithm in MATLAB. Similarly, the model evaluation was done in a java interface using multiobjective particle swamp optimization.

The evaluation results validated the designed model and showed that the factors they deliver when promised and queues that move rapidly are a more reliable scale of measurement for customer’s satisfaction than the factors precision on account statements and sufficient number of ATMs per branch.

The implication of the results is that effectiveness and assurance combined with access is a more significant factor for measuring customers’ satisfaction than tangibles based on the BSQ model.

The introduction of a two-phase optimization model for model benchmarking and evaluation as compared to ordinary factor analysis of the dimension constructs.

Deriving a measure of efficiency of public-funded organizations (primarily not-for-profit organizations) and ranking these efficiency measures have been major subjects of debate and discussion. The purpose of this paper is to evaluate the relative performances of public-funded research and development (R&D) organizations functioning across multiple countries working on similar research streams. The authors use multiple measures of inputs and outputs for this purpose.

The authors use the data envelopment analysis (DEA) as the primary methodology of analysis The keywords highlighting the major research areas in the field of non-metrology, conducted by National Physical Laboratory (NPL), India, were utilized to select the global comparators working on similar research streams. These global comparators were three R&D organizations located in the USA and one each located in Germany and Japan. The relative efficiencies of the organizations were assessed with the following output variables – external cash flow, and the numbers of technologies transferred, publications and patents; and the following input variables – amount of grants received from the parent body, and the number of scientific personnel working in these public R&D organizations. The authors follow the output-oriented measure of efficiency at constant return to scale and variable return to scale, along with scale efficiencies.

The performance of NPL, India under multiple dimensions has been evaluated relative to its global comparators – the National Institute for Materials Science, Japan; the National Renewable Energy Laboratory, USA; Fritz Haber Institute of the Max Planck Society, Germany; the National Centre for Atmospheric Research, USA; and the Oak Ridge National Laboratory, USA. The study indicates suggested measures and a set of targets to achieve the best possible performance for NPL and other R&D organizations. In most cases of efficient local but not so efficient global efficiency scores indicate that, on an average, the actual scale of production has diverged from the most productive scale size.

The approach highlights the utilization of the DEA methodology for relative R&D performance assessment of global comparators. The discriminatory analysis has brought into sharp focus the dichotomy between local efficiency and global efficiency scores of these units and issues of scale size and regional disparities. The outcome of this approach is dependent upon correct selection of input and output variables and data availability.

The study results have profound implications for the management of public R&D institutions across nations working on similar-focused research streams, but functioning within different societal, economic, and political contexts.

The present work, being perhaps one of the few multinational studies of relative performance assessment of pubic-funded R&D organizations working on similar research streams, signifies the relevance of such an approach in the field of R&D/innovation management. This has opened up new avenues for further research in this area.

Banking industry forms a part of financial services that has emerged itself as the most important source for India’s economic growth. Job satisfaction of employees is one of the important pre-requisites to ensure smooth functioning of banks. The purpose of this paper is to explore the association of job satisfaction with the quality of work life factors of bank employees (n=300), followed by the essential influential relationship of these concepts with socio-demographic characteristics, thereby, proving its own distinct contribution to the subsist body of literature.

This study has considered five private sector banks in India and has used the technique of multi-stage sampling to collect primary data. The respondents from different cadres, namely, executive, associate and manager involved in customer-oriented interactions participated in this survey. The analysis has been conducted by applying descriptive statistics, regression analysis (impact of the quality of work life factors on job satisfaction) and χ² statistics (association of the quality of work life and job satisfaction with socio-demographic variables). The results have been compared with the Herzberg Theory of Job Satisfaction.

The results of the study show the presence of variance (R² 61.40 percent) in job satisfaction as explained by the quality of work life constructs. The unconducive work environment has confirmed negative association with job satisfaction. The study foresees to contribute useful information to the top management level in the organizations to enhance employees’ overall job satisfaction.

The opaqueness with which the Indian banking industry has its roots and existence in India, the present study clearly has limits: the small size of the sample and the study considered only private sector banks.

A planned approach at organizational and individual level is highly recommended. The bank management must realize the importance of their devoted staff by giving them quality work environment. The initiatives like regular exercise routines can be adopted to reduce stress. Some respondents expressed the need for intercity branch associations, which can help them to solve common problems, better learning opportunities with an informal atmosphere along with other training sessions organized formally.

The paper gives a theoretical explanation of the quality of work life and job satisfaction factors in the Indian private banks falling under the umbrella of Indian banking industry with respect to the employees of private sector banks.