Faculty & Research

Research Productive

Show result

Search Query :
Area :
Search Query :
821 items in total found

Journal Articles | 2018

Economic geography and emerging market clusters: A co-evolutionary study of local and non-local networks in Bangalore

Florian A.Täubea, Amit Karna, and Petra Sonderegger

International Business Review

The presence of network ties within location plays a significant role in organization and evolution of clusters. This has proven to be particularly true for clusters specializing in knowledge intensive industries, where the organization of resources – people and technology – has been a primary driver for firm and regional performance. With the help of a longitudinal case study of the Bangalore IT cluster in India, we investigate the effect of local and non-local network ties on its evolution. We argue that networks – both local and non-local – play an important role in the development of cluster. We propose a non-linear relationship between cluster evolution phases and the type of network ties most prominent. Our study also outlines the role that embeddingexpansion, and extension of ties plays in transitioning cluster from one phase to the other. The consideration of non-local ties is rather nascent in the cluster literature and promises to enhance the understanding of how clusters develop at both levels – policy as well as firm.

Read More

Journal Articles | 2018

An empirical study of Latitude of Quantity Acceptance (LQA) in an emerging economy: India

Gordhan Saini, Arvind Sahay, and Gurumurthy Kalyanaram

Journal of Global Marketing

This study examines three important research questions. First, is there a latitude of acceptance with respect to small quantity changes? Second, is there an asymmetric effect of quantity changes? Third, is there a differential effect between high-equity and low-equity brands in response to quantity changes which is acceptable to customers? The effect of quantity change on purchase intention was examined through the decrease (increase) in original quantity of high-equity brands (low-equity brands), keeping price constant. ANOVA and ANCOVA were used to estimate the main and interaction effects. Empirical results show that: (a) there is evidence for LQA; (b) the effect of quantity change is asymmetric; and (c) the LQA range is larger for low-equity brands. A lower range of LQA for high-equity brands limits quantity reduction choice as a firm strategy and lowering price by a small percentage is unlikely to be successful for a low-equity brand.

Read More

Journal Articles | 2018

Asset liability management model with decision support systems for life insurance companies: Computational results

Goutam Dutta, Harish V.Rao, Sankarshan Basu, and Manoj Kr.Tiwari

Computers & Industrial Engineering

Big Data Analytics is an important and flexible tool available for data analysis and informed decision making. In this paper, we look at the use of Big Data Analytics in asset liability management and asset allocation in uncertain economic situations using stochastic linear programming (SLP). In particular, this paper is an extension of our earlier work and we contribute to the existing literature by conducting experiments on the stochastic model through DSS. In particular, for this SLP based DSS, we address issues like the optimal number of scenarios required for good results, and the impact of the change in the number of scenarios on the stability of the model. The paper also addresses the impact of the change in the number of scenarios on the policy holders’ as well as shareholders’ reserves. In particular, we show the relevance of employing a larger number of scenarios and also present the experimental design developed to test the relevance of this model. We also show that a stochastic model employing fewer scenarios produced marked improvements in both return side measures as well as risk side measures compared to a mean value model or a partial mean value model.

Read More

Journal Articles | 2018

New decision support system for strategic planning in process industries: Computational results

Goutam Dutta, Narain Gupta, Jasashwi Mandal, and Manoj Kr.Tiwari

Computers & Industrial Engineering

The impact of a Stochastic Linear Programming (SLP) based Decision Support System in a manufacturing company, such as an integrated aluminum plant, is measured by two important parameters, the VSS and EVPI. With the real data of an integrated steel plant in India, we demonstrate that SLP based DSS can be very effective in managing demand uncertainty and performing futuristic integrated planning, and their financial impact can be in millions of dollars. A two stage stochastic programming model with recourse is implemented in the DSS here. A set of experiments is conducted. Real data from an aluminum company is used to validate the system. The importance of SLP based DSS can be realized from the fact that the value of the stochastic solution (VSS) is USD 3.58 million with 30% demand variability and equally likely demand distribution. The VSS as a percentage of Expectation of Expected Value (EEV) ranges from 0.90% to 18.93% across experiments.

Read More

Journal Articles | 2018

New asset liability management model with decision support system for life insurance companies: interface design issues for database and mathematical models

Goutam Dutta, Harish V. Rao, and Sankarshan Basu

International Journal of Revenue Management

We introduce a new asset liability management (ALM) model based on decision support system (DSS) for a life insurance firm. The DSS is based on multi-stage stochastic linear programming (SLP) with recourse for strategic planning. The model can be used with minimal knowledge of management sciences. The model maximises the expected value of total reserve (policy holders' reserve and shareholders' reserve) at the end of the time period of planning with constraints both on the asset and the liability side of the firm's balance sheet. We discuss issues related to DSS interface design, one to one correspondences between the SLP model and the database and the difficulty in multi stage DSS compared to two stage DSS. We also compare and contrast the similarities and differences with our earlier work on SLP based DSS for process industries.

Read More

Journal Articles | 2018

Promoting health in rural India: Enhancing job performance of health activists

Jatin Pandey, Manjari Singh, Biju Varkkey, and Dileep Mavalankar

Academy of Management Proceedings

The health of people in a nation is a potential indicator of its development. Over and above that, the job performance of people involved in the delivery and facilitation of health care services within a nation reflects the actual health conditions in it. In developing countries, where a large chunk of the population lives in rural areas, the job performance of grass-roots health care workers gains significant importance in order to ensure effective and efficient delivery of health care services to the masses and marginalized communities. The present study takes the case of Accredited Social Health Activists (ASHAs) in difficult rural areas of India to identify factors that affect their job performance and suggests interventions through which it could be enhanced. Fifty-five ASHAs were interviewed and five focused group discussions (FGDs) were conducted. Additionally, triangulation was done by interviewing other stakeholders, while studying relevant documents. Through content analysis of these interviews and documents, this study identifies the demands, resources and stressors that affect the job performance of these important intermediaries in the health care supply chain (in the Indian context). The study also suggests policy-level decisions that could help in enhancing job performance of ASHAs by managing demands, increasing resources and reducing stressors.

Read More

Journal Articles | 2018

Emotional labour of rural women in difficult geographies of an emerging economy: Narratives of community healthcare workers of India

Jatin Pandey, Manjari Singh, and Shrihari Suresh Sohani

International Journal of Work Organisation and Emotion

This study attempts to find the existence, execution and outcome of emotional labour in the work of community healthcare workers. Through a ten-month field study comprising in-depth interviews with 26 accredited social health workers (ASHAs), we found that their work requires emotional labour. Our study shows that they use 'attached approach', which is similar to deep acting, and 'detached approach', which is similar to surface acting, to perform emotional labour. We also found that surface acting resulted in minimal negative effect in case of negative situation and in well-being due to attenuation of work benefits in case of positive situation at the workplace. Deep acting in a positive situation led to emotional permeability between work and personal life, whereas in the event of a negative situation, it led to stress. Furthermore, deep acting decreases their effectiveness and efficiency due to associated stress that could have detrimental effects on the beneficiaries of healthcare.

Read More

Journal Articles | 2018

Worth the wait? How restaurant waiting time influences customer Behaviour and revenue

Jelle De Vries, Debjit Roy, and Rene De Koster

Journal of Operations management

In many service industries, customers have to wait for service. When customers have a choice, this waiting may influence their service experience, sojourn time, and ultimately spending, reneging, and return behavior. Not much is known however, about the system-wide impact of waiting on customer behavior and resulting revenue. In this paper, we empirically investigate this by analyzing data obtained from 94,404 customers visiting a popular Indian restaurant during a 12 month period. The results show that a longer waiting time relates to reneging behavior, a longer time until a customer returns, and a shorter dining duration. To find out the impact of the consequences of waiting time, we use the empirical findings and data collected in a simulation experiment. This experiment shows that, without waiting, the total revenue generated by the restaurant would increase by nearly 15% compared to the current situation. Stimulating customers to reserve could enable restaurants to reap part of this benefit. Furthermore, the results of simulation experiments suggest that, within the boundaries of the current capacity, revenue could be increased by a maximum of 7.5% if more flexible rules were used to allocate customers to tables. Alternatively, by increasing the existing seating capacity by 20%, revenue could be boosted by 7.7% without the need to attract additional customers. Our findings extend the knowledge on the consequences of customer waiting, and enable service providers to better understand the financial and operational impact of waiting-related decisions in service settings.

Read More

Journal Articles | 2018

Farmer producer organizations as farmer collectives: A case study from India

Nalini Bikkina, Rama Mohana R. Turaga, and Vaibhav Bhamoriya

Development Policy Review

Small and marginal farmers in India have been vulnerable to risks in agricultural production. Several organizational prototypes are emerging to integrate them into the value chain with the objectives of enhancing incomes and reduction in transaction costs. One such alternative is Farmer Producer Organizations (FPOs). We explore the potential of FPOs as collective institutions through a case study of Avirat, one of the first FPOs in Gujarat. Our analysis suggests that FPOs have the potential to provide benefits through effective collective action. The main challenge, however, is to raise sufficient capital that can maximize these benefits. We discuss the implications of our findings to policy

Read More

Journal Articles | 2018

Real time location prediction with taxi-GPS data streams

Arnab Kumar Laha and Sayan Putatunda

Transporation Research Part C: Emerging Technologies

The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a Damped window model namely, Multivariate multiple regression, spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. The Multivariate multiple regression method and the Ensemble of the three methods are found to be the two best performers. The next pickup location problem is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method and the Ensemble of the three methods gives better performance than the rest.

Read More
IIMA