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3899 items in total found

Journal Articles | 2018

The effect of relationship and transactional characteristics on customer retention in emerging online markets

Anand K.Jaiswal, Rakesh Niraj, Chang Hee Park, and Manoj K.Agarwal

Journal of Business Research

Trust is important for maintaining customer relationships in online retailing, as customers have only a virtual connection with sellers. This is especially true in online markets of emerging economies, given their lack of trust-enhancing infrastructure and well-functioning regulatory institutions. We investigate the effect of trust and a set of other relationship and transactional characteristics—mode of customer acquisition, length of relationship, service communication, product return activity, and type of products purchased—on retention in the context of emerging online markets. We obtain data from an online retailer in India that include both survey and transaction information. Using a latent attrition model, we find that trust positively affects customer retention behavior. We also find that relationship length, service communication, product return experience, and the type of products purchased affect retention. Furthermore, we conduct split-sample analysis and suggest some managerial actions on spending efforts to enhance retention.

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Journal Articles | 2018

Project Nirman: The way ahead

Anamika Sinha, Biju Varkkey, and Priyanka Dave

South Asian Journal of Business and Management Cases

Project Nirman by SAATH, a Gujarat-based NGO, aimed at empowering and training migrated workmen as masons, carpenters and electricians as per industry requirements. The project was funded by Bosch India Foundation. Although all aspects of the pilot project were successfully tested for sustainability, continuous funding remained a challenge. The project’s protagonist wanted to upscale operations but was facing a dilemma. While exploring options for sustainability on a continuum of dependency to complete self-sufficiency, the protagonist became increasingly aware of roles and identities of each partner in such alliances.

Some peripheral dilemmas like challenges in identifying a socially relevant project, upscaling the pilot project, identifying team capabilities for growth and need for value integration by different stakeholders for desired growth were noted. This case closes by questioning on how strategic alliances should be made so that the four partners — government, community, Non-government Organization and corporate — learn to coexist with mutual respect.

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Journal Articles | 2018

Energy balance of Indian villages: A case study of seven villages

Amit Garg, Jaypalsinh Chauhan, and Abha Chhabra

Journal of Operation and Strategic Planning

This paper estimates the rural energy balance of 7 Indian villages of different agro-climate zones. This was done through primary survey of households in each village covering energy consumption, production, export, import and stock change across Crop, Livestock, Industry/Trade, Tree outside forest/plantations and Residential Sector. An energy flow model was created to capture all the various energy flows at household levels. Two villages are showing Negative annual energy balance—one is the desert village of Gujarat state and another is a tribal village of Mizoram state. All other villages were found to be energy positive mainly due to high forest density and high crop yields.

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Journal Articles | 2018

Investigating the impact of workforce racial diversity on the organizational corporate social responsibility performance: An institutional logics perspective

Amalesh Sharma, Aditya Christopher Moses, Sourav Bikash Borah, and Anirban Adhikary

Journal of Business Research

Racial diversity is considered an integral part of the business world. The extant literature has focused on the effect of racial diversity on a firm's financial performance and presented mixed findings. Building on the institutional logics lens and using a sample of 204 firms belonging to 9 industries and spread across 21 countries for a period of six years, we explore the impact of workforce racial diversity on the Corporate Social Responsibility Performance (CSRP) of a firm. In addition, we also investigate the contingency effects of a firm's absorptive capacity and slack resources on the proposed relationship. Using a seemingly unrelated regression model and accounting for endogeneity, we find that racial diversity has an inverted U-shaped relationship with a firm's financial and social performance and has a U-shaped relationship with its environmental performance. We also find significant moderating effects. Thus, we contribute to the theory and practice in the field.

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Journal Articles | 2018

Managing children's internet advertising experiences: Parental preferences for regulation

Akshaya Vijayalakshmi Meng‐Hsien (Jenny) Lin and Russell N. Laczniak

Journal of Consumer Affairs

Recent research suggests that children are spending a significant amount of time on the Internet which increases their exposure to subtle, engaging, and interactive ads. As a result, policy makers have developed regulations intended to empower parents to manage their children's exposure to Internet advertising. However, prior research has not examined parental perceptions of these regulations. This article aims to identify (1) parents' regulatory preferences regarding children's exposure to Internet advertising and (2) whether (and how) parents' locus of control (LOC) drives their regulation preferences. Findings reveal that internal-LOC parents prefer parental responsibility while external-LOC parents prefer government regulations, parental responsibility, and involvement of independent organizations and firms. External-LOC parents' preference is mediated by their concerns about Internet advertising and their tendency to have faith in regulation. Policy makers can use the findings to develop guidelines that better assist parents in influencing their children's Internet use.

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Journal Articles | 2018

Theoretical comparisons of estimators of finite population proportion under simple random sampling. Stastistics and Applications

Sumanta Adhya, and Tathagata Banerjee

Statistics and Applications

We consider the classical survey problem of estimation of finite population proportions based on a polychotomous response variable when data on an auxiliary variable is known for all units in the finite population. Under simple random sampling different model and design-based estimators are compared theoretically and it is shown that model-based estimator performs more efficiently under mild conditions.

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Journal Articles | 2018

Qasab: Kutch Craftswomen's Producer Co. Ltd.

Shweta Mittal, Vishal Gupta, and Manoj Motiani

Asian Case Research Journal

This case was prepared by Assistant Professor Shweta Mittal of Institute of Management & Research, Ghaziabad, India, Associate Professor Vishal Gupta of Indian Institute of Management Ahmedabad, India and Assistant Professor Manoj Motiani of Indian Institute of Management Indore, India, as a basis for classroom discussion rather than to illustrate either effective or ineffective handling of an administrative or business situation.

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Journal Articles | 2018

Efficient mining of high utility itemsets with multiple minimum utility thresholds

Srikumar Krishnamoorthy

Engineering Applications of Artificial Intelligence

Mining high utility itemsets is considered to be one of the important and challenging problems in the data mining literature. The problem offers greater flexibility to a decision maker in using item utilities such as profits and margins to mine interesting and actionable patterns from databases. Most of the current works in the literature, however, apply a single minimum utility threshold value and fail to consider disparities in item characteristics. This paper proposes an efficient method (MHUI) to mine high utility itemsets with multiple minimum utility threshold values. The presented method generates high utility itemsets in a single phase without an expensive intermediate candidate generation process. It introduces the concept of suffix minimum utility and presents generalized pruning strategies for efficiently mining high utility itemsets. The performance of the algorithm is evaluated against the state-of-the-art methods (HUI-MMU-TE and HIMU-EUCP) on eight benchmark datasets. The experimental results show that the proposed method delivers two to three orders of magnitude execution time improvement over the HUI-MMU-TE method. In addition, MHUI delivers one to two orders of magnitude execution time improvement over the HIMU-EUCP method, especially on moderately long and dense benchmark datasets. The memory requirements of the proposed algorithm was also found to be significantly lower.

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Journal Articles | 2018

Efficiently mining high utility itemsets with negative unit profits

Srikumar Krishnamoorthy

Knowledge-Based Systems

A High Utility Itemset (HUI) mining is an important problem in the data mining literature that considers utilities of items (such as profits and margins) to discover interesting patterns from transactional databases. Several data structures, pruning strategies and algorithms have been proposed in the literature to efficiently mine high utility itemsets. Most of these works, however, do not consider itemsets with negative unit profits that provide greater flexibility to a decision maker to determine profitable itemsets. This paper aims to advance the state-of-the-art and presents a generalized high utility mining (GHUM) method that considers both positive and negative unit profits. The proposed method uses a simplified utility-list data structure for storing itemset information during the mining process. The paper also introduces a novel utility based anti-monotonic property to improve the performance of HUI mining. Furthermore, GHUM adapts key pruning strategies from the basic HUI mining literature and presents new pruning strategies to significantly improve the performance of mining. The proposed method is evaluated on a set of benchmark sparse and dense datasets and compared against a state-of-the-art method. Rigorous experimental evaluation is performed and implications of the key findings are also presented. In general, GHUM was found to deliver more than an order of magnitude improvement at a fraction of the memory over the state-of-the-art FHN method.

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Journal Articles | 2018

Mining top-k high utility itemsets with effective threshold raising strategies

Srikumar Krishnamoorthy

Expert Systems With Applications

Top-K High Utility Itemset (HUI) mining problem offers greater flexibility to a decision maker in specifying her/his notion of item utility and the desired number of patterns. It obviates the need for a decision maker to determine an appropriate minimum utility threshold value using a trial-and-error process. The top-k HUI mining problem, however, is more challenging and requires use of effective threshold raising strategies. Several threshold raising strategies have been proposed in the literature to improve the overall efficiency of mining top-k HUIs. This paper advances the state-of-the-art and presents a new Top-K HUI method (THUI). A novel Leaf Itemset Utility (LIU) structure and a threshold raising strategy is proposed to significantly improve the efficiency of mining top-k HUIs. A new utility lower bound estimation method is also introduced to quickly raise the minimum utility threshold value. The proposed THUI method is experimentally evaluated on several benchmark datasets and compared against two state-of-the-art methods. Our experimental results reveal that the proposed THUI method offers one to three orders of magnitude runtime performance improvement over other related methods in the literature, especially on large, dense and long average transaction length datasets. In addition, the memory requirements of the proposed method are found to be lower.

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