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Article

Rajat Kumar Behera, Pradip Kumar Bala and Rashmi Jain

Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and…

Abstract

Purpose

Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.

Design/methodology/approach

An experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.

Findings

The rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.

Research limitations/implications

A small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.

Practical implications

The best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.

Originality/value

The proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

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Article

Rahul Kumar and Pradip Kumar Bala

Collaborative filtering (CF), one of the most popular recommendation techniques, is based on the principle of word-of-mouth communication between other like-minded users…

Abstract

Purpose

Collaborative filtering (CF), one of the most popular recommendation techniques, is based on the principle of word-of-mouth communication between other like-minded users. The process of identifying these like-minded or similar users remains crucial for a CF framework. Conventionally, a neighbor is the one among the similar users who has rated the item under consideration. To select neighbors by the existing practices, their similarity deteriorates as many similar users might not have rated the item under consideration. This paper aims to address the drawback in the existing CF method where “not-so-similar” or “weak” neighbors are selected.

Design/methodology/approach

The new approach proposed here selects neighbors only on the basis of highest similarity coefficient, irrespective of rating the item under consideration. Further, to predict missing ratings by some neighbors for the item under consideration, ordinal logistic regression based on item–item similarity is used here.

Findings

Experiments using the MovieLens (ml-100) data set prove the efficacy of the proposed approach on different performance evaluation metrics such as accuracy and classification metrics. Apart from higher prediction quality, coverage values are also at par with the literature.

Originality/value

This new approach gets its motivation from the principle of the CF method to rely on the opinion of the closest neighbors, which seems more meaningful than trusting “not-so-similar” or “weak” neighbors. The static nature of the neighborhood addresses the scalability issue of CF. Use of ordinal logistic regression as a prediction technique addresses the statistical inappropriateness of other linear models to make predictions for ordinal scale ratings data.

Details

Journal of Modelling in Management, vol. 12 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

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Article

Rahul Kumar, Shubhadeep Mukherjee, Bipul Kumar and Pradip Kumar Bala

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving…

Abstract

Purpose

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving various business processes from product development to stock market development. This paper aims to transform this wealth of information in the online medium to economic wealth. Earlier approaches to investment decision-making are dominated by the analyst's recommendations. However, their credibility has been questioned for herding behavior, conflict of interest and favoring underwriter's firms. This study assumes that members of the online crowd who have been reliable, profitable and knowledgeable in the recent past will continue to be so soon.

Design/methodology/approach

The authors identify credible members as experts using multi-criteria decision-making tools. In this work, an alternative actionable investment strategy is proposed and demonstrated through a mock-up. The experimental prototype is divided into two phases: expert selection and investment.

Findings

The created portfolio is comparable and even profitable than several major global stock indices.

Practical implications

This work aims to benefit individual investors, investment managers and market onlookers.

Originality/value

This paper takes into account factors: the accuracy and trustworthiness of the sources of stock market recommendations. Earlier work in the area has focused solely intelligence of the analyst for the stock recommendation. To the best of the authors’ knowledge, this is the first time that the combined intelligence of the virtual investment communities has been considered to make stock market recommendations.

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Article

Arghya Ray, Pradip Kumar Bala and Rashmi Jain

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both…

Abstract

Purpose

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data.

Design/methodology/approach

This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes.

Findings

Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.

Practical implications

Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts.

Originality/value

The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.

Details

Benchmarking: An International Journal, vol. 28 no. 2
Type: Research Article
ISSN: 1463-5771

Keywords

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Article

Shubhadeep Mukherjee and Pradip Kumar Bala

The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services…

Abstract

Purpose

The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services, etc. This can be immensely helpful in reducing incorrect classification of consumer sentiment toward issues, products and services.

Design/methodology/approach

In this study, 5,000 tweets were downloaded and analyzed. Relevant features were extracted and supervised learning algorithms were applied to identify the best differentiating features between a sarcastic and non-sarcastic sentence.

Findings

The results using two different classification algorithms, namely, Naïve Bayes and maximum entropy show that function words and content words together are most effective in identifying sarcasm in tweets. The most differentiating features between a sarcastic and a non-sarcastic tweet were identified.

Practical implications

Understanding the use of sarcasm in tweets let companies do better sentiment analysis and product recommendations for users. This could help businesses attract new customers and retain the old ones resulting in better customer management.

Originality/value

This paper uses novel features to identify sarcasm in online text which is one of the most challenging problems in natural language processing. To the authors’ knowledge, this is the first study on sarcasm detection from a customer management perspective.

Details

Industrial Management & Data Systems, vol. 117 no. 6
Type: Research Article
ISSN: 0263-5577

Keywords

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Article

Arghya Ray, Pradip Kumar Bala, Shilpee A. Dasgupta and Narayanasamy Sivasankaran

This paper aims to explore the consumers’ and service-providers’ perspectives on the factors influencing adoption of e-services in rural India. The purpose is to enable…

Abstract

Purpose

This paper aims to explore the consumers’ and service-providers’ perspectives on the factors influencing adoption of e-services in rural India. The purpose is to enable better diffusion of technology for societal development in this digital era.

Design/methodology/approach

Using qualitative-based multiple-participant interviews, this study explores the factors affecting e-service adoption from two different perspectives. While interviews were conducted in five villages with 14 respondents to find out the perspectives of the consumers, this study also explores the service-providers’ perspectives through interviews conducted among 11 managerial respondents.

Findings

Catering to personal needs, improving perceived usefulness, value-added options, data analytics for better understanding customers and improving service delivery of the e-service are the major factors identified by the service-providers. The study also concludes that convenience, compatibility, societal influence and availability of value-added addition of the e-service are decisive in e-service adoption from the perspectives of the consumers.

Research limitations/implications

The first limitation of this research is that there can be common method bias. Second, there were overlapping themes.

Practical implications

This study can help researchers working on the adoption of e-services in under-developed/developing countries. The findings of this study may help industries to focus on the determinants while designing the e-services for improving their rate of adoption.

Social implications

This study will help in better diffusion of e-services in rural areas, which in turn will help in societal development in this digital era.

Originality/value

The focus is on societal development through the adoption of e-services in rural areas. To the best of the knowledge of the researchers, no qualitative study has been performed to capture the perspectives of both the service-providers and the consumers on the adoption of e-services in India.

Details

Journal of Indian Business Research, vol. 12 no. 2
Type: Research Article
ISSN: 1755-4195

Keywords

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Article

Puneet Kaur, Amandeep Dhir, Arghya Ray, Pradip Kumar Bala and Ashraf Khalil

The current study tries to better understand the resistance toward food delivery applications (FDAs). This study has adapted the existing criteria to measure different…

Abstract

Purpose

The current study tries to better understand the resistance toward food delivery applications (FDAs). This study has adapted the existing criteria to measure different consumer barriers toward FDAs. It also examined the relationships between various consumer barriers, intention to use FDAs and word-of-mouth (WOM).

Design/methodology/approach

This study utilized the innovation resistance theory (IRT) and a mixed-method approach comprised of qualitative essays submitted by 125 respondents and primary surveys (N = 366) of FDA users.

Findings

Tradition barrier (trust) shared a negative association with use intention, while image barrier (poor customer service) shared a negative association with WOM. The intention to use was positively associated with WOM. Additionally, the study results reveal that image barrier (poor customer experience) and value barrier (poor quality control) were, in fact, positively related to WOM. This study also discusses the managerial and theoretical implications of these findings and the scope for further research on FDAs.

Originality/value

FDAs have revolutionized the food delivery industry and made it more comfortable and convenient for the consumers. However, FDA service providers are facing challenges from both customers and restaurants. Although scholars investigated customer behavior toward FDAs, no prior study has focused on consumer barriers toward FDA usage.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

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Article

Pradip Kumar Bala

The purpose of this paper is to develop a forecasting model for retailers based on customer segmentation, to improve performance of inventory.

Abstract

Purpose

The purpose of this paper is to develop a forecasting model for retailers based on customer segmentation, to improve performance of inventory.

Design/methodology/approach

The research makes an attempt to capture the knowledge of segmenting the customers based on various attributes as an input to the demand forecasting in a retail store. The paper suggests a data mining model which has been used for forecasting of demand. The proposed model has been applied for forecasting demands of eight SKUs for grocery items in a supermarket. Based on the proposed forecasting model, the inventory performance has been studied with simulation.

Findings

The proposed forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. Hence, the proposed model in the paper results in improved performance of inventory.

Practical implications

Retailers can make use of the proposed model for demand forecasting of various items to improve the inventory performance and profitability of operations.

Originality/value

With the advent of data mining systems which have given rise to the use of business intelligence in various domains, the current paper addresses one of the most pressing issues in retail management, as demand forecasting with minimum error is the key to success in inventory and supply chain management. The proposed forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. The proposed model outperforms other widely used existing models.

Details

Journal of Modelling in Management, vol. 7 no. 1
Type: Research Article
ISSN: 1746-5664

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Article

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Abstract

Purpose

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings

Growing doubts about the reliability of professional analysts is making many investors hesitant to use the conventional approach to stock market investment. They are instead becoming increasingly attracted to an alternative strategy based on recommendations offered from members of virtual communities. Objective criteria are used to identify experts within such domains who have the potential to generate results comparable with major global indices.

Originality/value

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Details

Strategic Direction, vol. 37 no. 2
Type: Research Article
ISSN: 0258-0543

Keywords

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