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The purpose of this paper is to provide an approach that a manufacturer can periodically use to measure the performances of its supplier development programs (SDPs). It also quantifies and indicates the specific supplier development outcomes (SDOs) that a manufacturer has to concentrate in improving its SDPs.

The proposed methodology integrates fuzzy analytic hierarchy process (to determine the importance of SDOs), Pareto analysis (to choose the significant SDOs) and fuzzy logic (to periodically determine the performance indices and grades of the SDPs) methods to measure the performances of SDPs. In order to demonstrate the utility of the methodology, a case situation is presented where the performance measures of SDPs running at five key suppliers of an Indian turbine manufacturing company were determined.

By periodically applying the above mentioned methodology, the performance indices of a manufacturer’s SDPs are measured, monitored and managed effectively. It is shown that the methodology is advantageous in clearly indicating the scope of improvement for the SDPs along the specific SDOs.

Since the obtained results in this study are specific to a manufacturing environment, they may not be generalized. However, the generic lists of SDOs mentioned in the paper and the proposed approach can be used in any manufacturing environment for analyzing the performances of SDPs.

The performance analysis of SDPs would help a manufacturer to know more about the functioning of its SDPs along the timeline and provide the scope for improvement.

The proposed approach successfully measures and also grades the performances of SDPs. The results enable the supplier development managers to effectively distribute their investments and efforts along the SDPs.

Selection of best product recovery alternative in reverse logistics (RL) has gained great attention in supply chain community. The purpose of this paper is to provide a robust group decision-making tool to select the best product recovery alternative.

In this paper, fuzzy values, assigned to various criteria and alternatives by a number of decision makers, are converted into crisp values and then aggregated scores are evaluated. After obtaining experts’ scores, objective and subjective weights of the criteria have been calculated using variance method and analytic hierarchy process, respectively. Then integrated weights of criteria are evaluated using different proportions of the two weights. The superiority and inferiority ranking (SIR) method is then employed to achieve the final ranking of alternatives. An example is presented to demonstrate the methodology.

The proposed methodology provides decision makers a systematic, flexible and realistic approach to effectively rank the product recovery alternatives in RL. The alternatives can easily be benchmarked and best recovery strategy can be obtained. The sensitivity analysis carried out by changing different proportion of objective and subjective weights reveals that best ranking alternative never changes and proves the robustness of the methodology. The present benchmarking framework can also be used by decision makers to simplify any problem which encounters multi-attribute decision making and multiple decision makers.

The proposed methodology should be tested in different situations having varied operational and environmental conditions dealing with different products. A real case study from an industrial set up can help to assess the behavior of the proposed methodology. The presented methodology however can deal with such multi-disciplinary and multi-criteria issues in a simple and structured manner and ease the managers to select the best alternative.

A novel approach for decision making taking into account both objective and subjective weights for criteria has been proposed to rank the best recovery alternatives in RL. The proposed methodology uses SIR method to prioritize the alternatives. As RL alternative selection is an important issue and involves both technical and managerial criteria as well as multiple decision makers, the proposed robust methodology can provide guidelines for the practicing managers.

The purpose of the study is to analyze the risk of violent conflict with the global conflict risk factors in the Middle East economies by using an integrated fuzzy decision approach. For this purpose, five different dimensions and 24 different criteria are defined by analyzing similar studies in the literature. The dataset is borrowed from the European Commission, and experts appointed for the linguistic evaluation of each dimension and criterion. Additionally, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to weigh dimensions and criteria and Multi-objective Optimization on the basis of Ratio Analysis (MOORA) approach is considered to rank the countries with respect to the conflict risk. Social dimension was concluded to have the highest importance of the Global Conflict Risk Index. Moreover, Syria, Libya, and Saudi Arabia were identified as the countries that have high conflict risk. Because these countries have high risk of facing conflict in the future, it is strongly recommended that they should primarily focus on social factors in order to minimize this risk.

The purpose of the paper is to determine the factors that lead to satisfaction of the customers as regards to e‐banking services provided by selected banks in India.

Survey method was used to conduct the study. Data were collected through a well structured questionnaire from a sample of 350 respondents.

As a major finding of the study, six factors namely ease of use, reliability, convenient accessibility, security, low transaction cost and the time consumption emerged as factors that lead to customer satisfaction as regards e‐banking services. Further, the results of multiple regression showed that out of the above mentioned six factors, three factors, namely, ease of use (i.e. user friendly web sites leading to easy technology‐customer interaction), low transaction cost (i.e. saving of time, speed of service delivery, convenience and reduced paper work in monetary terms) and security (i.e. dependable safety mechanism in terms of illegal access of accounts, hacking and password protection) are found to be statistically significant at 5 percent significance level.

The study has a regional bias since the respondents belong to a single state of northern India. To have better generalisation of the results, a sample size could be made appropriately large and a wider geographical area be covered.

Taking findings of the study into consideration, strategies could be drawn by the bankers to spread their businesses as a large chunk of the population in India is still not using banking services. Through internet, however, access could be provided to customers residing in remote areas of the country.

The study is quite helpful for the policy makers in comprehending the attitude of banking customers towards e‐banking services provided by the banks and for developing appropriate strategies for placing themselves at competitively advantageous positions.

This paper aims to examine alliance governance at different hierarchical levels.

The data is collected from both top-level and operating-level managers in 286 strategic alliances in China (a total of 572 managers). Hierarchical moderated regression models are adopted to test the hypotheses and two-stage regression analyzes are used to correct for endogeneity.

This paper finds that relational governance has a greater impact on alliance performance than contract utilization at the top level. Furthermore, the simultaneous use of relational governance at the top and operating levels have a detrimental impact on alliance performance. Finally, top-level contract utilization has a negative interaction with operating-level relational governance but a positive interaction with operating-level contract utilization.

First, the cross-sectional nature of the data collection approach provides only a snapshot of how each type of governance mechanism and its interactions affect alliance performance. Second, the sample is limited to firms located in emerging markets.

Managers should realize that the effectiveness of contract and relational governance mechanisms varies across different management levels and they should be cautious about the cross-level governance mechanism alignment.

This study advances the interfirm governance literature in that this paper examined alliance governance at different hierarchical levels and provides new insights into the ongoing debate on whether the contract and relational governance mechanisms function as complements or substitutes by exploring the governance alignment across different alliance hierarchies.

In the presented research, the authors have conducted the literature review and organised real interviews of fruit retailers (FRs) to construct the advanced hierarchical structural (AHS) chain of macro-micro parameters for measuring the performances of defined fruit supply bazaars (FSBs). Apart from this, the purpose of this paper is to develop the grey set-based scorecard model for solving the proposed AHS chain of macro-micro parameters.

The performance of FSBs is linked with the supply of fruits towards clients under a feasible rate, which circuitously depends upon the evaluation of the economic locality of FSBs. The authors developed an advanced hierarchical structure of macro-micro parameters via a literature survey and considered these parameters based on the sampling score of FRs corresponding to select feasible FSBs/alternatives. Furthermore, the authors developed a grey set-based scorecard model for undertaking the incomplete information of FRs against the hierarchical structure.

It is found that the work is well suited for FRs as they can measure the performances of defined FSBs in accordance with their own opinions under the proposed AHS of macro-micro parameters. Apart from this, the work is useful for benchmarking the vegetable supply bazaars (VSBs) on the replacement of AHS. The proposed hierarchical structure with a grey-based scorecard model is flexible in its nature and can undertake more than 1,000 macro-micro parameters and FRs to access potential decision.

The conducted research work has a precise value for evaluating the economic FSB locality. The overall performance scores of considered FSB localities are computed as (∂₁)=1.991, (∂₂)=2.567 and (∂₃)=2.855, where (∂₃) is found to be more significant than available FSBs. This work can be used for opting the economic locality of VSB too.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

Product success is contingent on forecasting when a product is needed and how it should be offered. Forecasting accuracy is contingent on the correct forecasting technique. Using supermarket data across two product categories, this chapter shows that using a bevy of forecasting methods improves forecasting accuracy. Accuracy is measured by the mean absolute percentage error. The optimal methods for one consumer goods product may be different than for another. The best model varied from sophisticated, most such as autoregressive integrated moving average (ARIMA) and Holt–Winters to a random walk model. Forecasters must be proficient in multiple statistical techniques since the best technique varies within a categories, variety, and product size.

The purpose of this paper is to adapt integrated hierarchical evaluation platform (associated with “green” performance indices) toward evaluation and selection of alternative suppliers under green supply chain (GSC) philosophy.

In this context, incompleteness, vagueness, imprecision, as well as inconsistency associated with subjective evaluation information aligned with ill-defined suppliers’ assessment indices has been tackled through logical exploration of fuzzy set theory. A fuzzy-based multi-level multi-criteria decision-making (FMLMCDM) approach as proposed by Chu and Varma (2012), has been case empirically studied in the context of green suppliers selection.

Result obtained thereof, has been compared to that of fuzzy-TOPSIS to validate application potential of the aforementioned FMLMCDM approach.

The proposed method has been found fruitful from managerial implication viewpoint.

This paper aims to examine how the quality of work-life (QWL) factors influence job satisfaction and tests if organization type moderates this relationship.

The study adopts a quantitative cross-sectional research design using survey administration among resident doctors used in the Indian health-care sector. Drawing on the person-environment fit theory, leader-member exchange theory and action learning theory, the authors discuss the results.

Results depicted that in addition to the direct relationship between QWL and job satisfaction, this association is moderated by organization type.

The study provides critical information for health-care managers to prioritize three QWL attributes, namely, meaningful development, supervision, working conditions to enhance job satisfaction among resident doctors.

The study contributes to the limited empirical scholarly research adding a deeper understanding of pertinent factors influencing resident doctor’s QWL-job satisfaction relationship.