R.P. Mohanty

Green supply chain management practices in India: an empirical study

This paper presents an empirical study of green supply chain management (GSCM) practices in the Micro, small and medium enterprises (MSMEs) in India. Although the research in the area of GSCM has grown in recent times, the literature has yet to furnish an accepted explanation for why green practices are to be manifested in supply chain management given external and internal pressures. These MSMEs have been involved in such green supply chain practices only to the extent of their participation as suppliers, distributors and in other capacities as business partners. This study confirms and validates that Indian MSMEs face significant pressures from external stakeholders to adopt GSCM practices. Among internal pressures, on-the-job training forces MSMEs in India to adopt GSCM practices. It has been also established that external pressures and adoption of GSCM are fully mediated by internal pressures.

Understanding Construction Supply Chain Management

The purpose of this study was to understand some of the many perplexing questions eluding managers of overwhelmingly complex construction supply chain management (CSCM) based on review of some important research issues pertaining to construction supply chains. This study has illustrated understanding on CSCM using qualitative approach of triangulation applying foundational research methodology and soft system methodology in a coal-based thermal power plant project to validate its complex CSCM systems. The study highlights that a typical CSCM eco-system for the coal-based thermal power plant construction as unit can be considered to operate as a system with sub-systems as concept phase, procurement phase, production phase, installation phase and winding up phase. This study suffers from methodological limitations associated with qualitative research. Finally, this study provides practical insights for research opportunities in the area of interdisciplinary construction projects.

Validation of multiple-item scale for measuring service quality in life insurance business: structural equation modelling approach

The purpose of this paper is to validate the multiple-item scale for measuring service quality in a way that it predicts customer satisfaction and patronage intentions in life insurance business in India. It has used structural equation modelling approach going after the model development strategy, which takes account of proposing a basic structural model framework and subsequently it improves the framework through modifications of competing structural models for arriving at the best-fit structural model. The multiple-item scale for measuring service quality in life insurance demonstrates good psychometric properties based on findings from a variety of reliability and validity tests and builds on the research already conducted on the topic. This study has established that a satisfied life insurance customer is not necessarily a loyal one as the best-fit structural model does not include patronage intention. Based on the significant relationships observed in the best-fit structural model, content, welcome and alignment are identified as the main factors of service quality in life insurance business; and service quality has not been found to be leading to customer satisfaction significantly. This study suffers from methodological limitations associated with convenience sampling and anonymous survey-based research.

Service quality modelling for life insurance business using neural networks

Running a successful life insurance company is about right structuring the life insurance products, which calls for minimising the performance-expectation gap. The life insurance business is not just about paying claims, but involves multiple expectations from various stakeholders. A lot of research has been carried out in the area of service quality; however, there is a paucity of critical examination of service quality neural networks applicable to life insurance business. The study attempts to critically examine five possible combinations of neural networks as applicable to measure service quality. A survey instrument has been designed to collect data from various stakeholders in Indian life insurance business, and have been analysed by using neural networks. A comparative analysis of all neural networks for key stakeholders is presented.

Green supply chain management practices in India: a confirmatory empirical study

This paper illustrates the application of Structural Equation Modelling (SEM) to understand the green supply chain management (GSCM) practices relating to Micro Small Medium Enterprises (MSME) in India. Path Analysis as a special case of SEM has been used here in seeing that the sample moment matrix of our multivariate data fails to be positive definite. Although the research in the area of GSCM has grown in recent years, the literature has yet to furnish an accepted explanation for why green practices are to be manifested in supply chain management given external and internal pressures. This research intends to study whether the greening scores measure a common construct called GSCM and whether external and internal pressures due to stakeholders have any influence on GSCM practices when differences are controlled for different types of MSME and different nature of industries of MSME in India. This study confirms and validates that the lower level of GSCM involvement of Indian MSME can be attributed to lack of the necessary external and internal pressures.

Scenario planning for service quality: a Monte Carlo simulation study

Purpose – The purpose of this paper is to propose the idea of linking the use of the Monte Carlo simulation with scenario planning to assist strategy makers in formulating strategy in the face of uncertainty relating to service quality gaps for life insurance business, where discontinuities always remain for need‐based selling.Design/methodology/approach – The paper reviews briefly some applications of scenario planning. Scenario planning emphasizes the development of a strategic plan that is robust across different scenarios. The paper provides considerable evidence to suggest a new strategic approach using Monte Carlo simulation for making scenario planning.Findings – The paper highlights which particular service quality gap attribute as risk impacts most and least for the possibility of occurrences as best case, worst case, and most likely case.Research limitations/implications – This study suffers from methodological limitations associated with convenience sampling and anonymous survey‐based research....

Business strategy of automotive and farm equipment sector of the Mahindra & Mahindra Group of India

Purpose – This paper traces the historical evolution and growth trajectory of the automotive and farm equipment sector, which is a very significant entity of the Mahindra & Mahindra (M&M) group. The purpose of this paper is to understand and provide a pragmatic framework through which the authors can see what were the internal and external factors and the spirit of the contemporary times that led to the changes in the nature of the group. Design/methodology/approach – The “Greiner curve” model has been applied to interpret the evolutionary growth of the group and strategic trajectory explaining characteristics in its different phases. Findings – M&M initially went through its share of learning and grew through pragmatic and, orchestrated entrepreneurial risk. The group made a very successful transition from a proprietorship model to a professionally managed group. It is found that rapid growth has been possible through innovation led collaboration. The group is increasingly organizing its innovation activ...

DEA and Monte Carlo simulation approach towards green car selection

Purpose Automakers are engaged in manufacturing both efficient and inefficient green cars. The purpose of this paper is to categorize efficient green cars and inefficient green cars followed by improving efficiencies of identified inefficient green cars for distribution fitting. Design/methodology/approach The authors have used 2014 edition of secondary data published by the Automotive Research Centre of the Automobile Club of Southern California. The paper provides the methodology of applying data envelopment analysis (DEA) consisting of 50 decision-making units (DMUs) of green cars with six input indices (emission, braking, ride quality, acceleration, turning circle, and luggage capacity) and two output indices (miles per gallon and torque) integrated with Monte Carlo simulation for drawing significant statistical inferences graphically. Findings The findings of this study showed that there are 27 efficient and 23 inefficient DMUs along with improvement matrix. Additionally, the study highlighted the best distribution fitting of improved efficient green cars for respective indices. Research limitations/implications This study suffers from limitations associated with 2014 edition of secondary data used in this research. Practical implications This study may be useful for motorists with efficient listing of green cars, whereas automakers can be benefitted with distribution fitting of improved efficient green cars using Monte Carlo simulation for calibration. Originality/value The paper uses DEA to empirically examine classification of green cars and applies Monte Carlo simulation for distribution fitting to improved efficient green cars to decide appropriate range of their attributes for calibration.

Expectations of professional graduates from organisations: a diagnostic study

Many studies have been performed previously to investigate different theories of employee attrition/turnover, motivation and, more specifically, how to attract, motivate, develop, and retain professionals. But, there are no such studies available to provide critical insights to Indian organisations. This paper is a diagnostic study in Indian context through a questionnaire survey administered on engineering and management new graduates. The objective is to get critical insights about their expectations from their employers in the short and long term. Human resource managers need to understand the various ways in which recruitment, career planning, development and retention policy decisions affect career of professionals. The more this knowledge is acquired, analysed and applied in real life, the more the company will derive from its investment in human capital.

A diagnostic study of employee attrition in an Indian automotive company

Employee commitment, productivity and retention issues are emerging as the most critical human resource management challenges of the immediate future driven by employee loyalty concerns, corporate restructuring efforts and tight competition for key talent. This paper is centred on the critical examinations of the demographic profiles of past employees, who have exited from a major automotive company during the last five years. A construct has been developed and checked for face validity, content validity and reliability and has been administered in a sample of 629 past employees. This study shows that in the future such organisations will be successful, if it can adapt their organisational behaviour and human resource department systems to the realities of the contemporary work environment. This includes playing an active role in assisting and advising the employee on career development decision, ensuring education and training; providing for adequate time to pursue career development activities; encouraging cross-functional training/assignments; engaging in innovative work practices and providing meaningful feedbacks for performance improvements.