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In order to enhance the performance of R&D in manufacturing organizations, the R&D managers need to identify the internal as well as the external factors that affect the R&D performance of manufacturing organizations in India. They need to understand the inter‐dependencies of these factors. This paper seeks to identify the critical success factors for R&D in Indian manufacturing firms.

There may be a number of factors that are critical for achieving acceptable R&D performance and these factors have been identified by a number of instruments or means, such as questionnaire surveys, brainstorming, and consolidation by Principal Component Analysis (PCA). A total of 14 factors have been identified by using principal component analysis and finally we have developed a structure of interrelationship among the identified critical success factors using an interpretive structural model.

The results show that R&D vision and direction and R&D oriented culture are the most important critical success factors (CSFs) and they have a great influence on the other CSFs. Though R&D vision and direction and R&D oriented culture are the short‐term objectives, Indian manufacturing firms should be equipped with proper R&D management strategy to achieve the long‐term objectives, such as achievement of revenue and profitability within a quick time frame.

Although R&D managers of Indian manufacturing firms are aware of various critical success factors, a systematic approach is required for identifying them, and as these factors may have complex interrelations between them for analyzing R&D performance in a manufacturing firm, it is essential that such an approach is in place. The hierarchy based ISM further defines those factors which are really critical and need more focus on the root causes of the success. In addition to that, the proposed ISM model acts as a good guideline in order to improve the performance of the manufacturing R&D organizations in India.

The paper provides an interpretive structural model to develop a map of the complex relationships and magnitude among identified critical success factors.

In order to ensure the uninterrupted supply of items, the purchasing manager needs to evaluate suppliers' performance periodically. The evaluation process typically consists of identifying the attributes and factors relevant to the decision, and measuring the performance of a supplier by considering the relevant factors. Linguistic assessment of suppliers may be carried out based on several criteria. In this paper, an attempt has been made to evaluate the suppliers' performance by adopting an evolutionary fuzzy system. One of the key considerations in designing the proposed system is the generation of fuzzy rules. A genetic algorithm‐based methodology is developed to evolve the optimal set of fuzzy rule base, and a fuzzy inference system of the MATLAB fuzzy logic toolbox is used to assess the suppliers' performance. The proposed methodology, illustrated with the data collected in a process plant, provides acceptable results in determining the suppliers' performance score.

The purpose of this paper is to validate through a case study involving an organization in India, a five-phase Define-Identify-Build-Assess-Review methodology proposed for designing and implementing knowledge management capability (KMC) in an organization from a holistic perspective.

This paper adopts the case study approach, using semi-structured interviews and survey questionnaires to gauge KMC in the organization. Exploratory factor analysis and multiple regressions are applied to determine the impact of the chosen factors on KMC of the organization. Further, interpretive structural modelling is used to determine impact of selected variables on the business performance.

KMC of the organization is predominantly based on the “embedded routines”, “knowledge base” and its “shared utilization” in the organization. The KMC is primarily driven through improved learning and rich explicit knowledge.

The study is confined to a specific business process in the organization. As the focus of study is based on a single organization, the generalization of the results to other organizations needs to be proven.

The periodical monitoring of the identified KMCs leads to enterprises making corrections and adjustments on the knowledge assets accordingly.

Introspection of the KMCs of the organization by the management in a holistic manner and bridging the operational gap by developing performance metrics.

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

The purpose of this paper is to investigate the role of the “Big Five” personality traits (extraversion, openness, agreeableness, conscientiousness and neuroticism) on the adoption of augmented reality (AR), with a particular focus on the role AR may play in interactive marketing.

A quantitative-based approach was followed by a questionnaire survey, which was completed by 230 respondents comprising graduate and postgraduate students, using structural equation modelling.

While the trait of openness was positively associated with the perceived ease of use of AR, the usefulness of AR and subjective norms, the trait of neuroticism was negatively associated with the perceived ease of use of AR. Extraversion was positively associated with subjective norms. Perceived ease of use of AR, the usefulness of AR and subjective norms were positively associated with attitudes toward AR.

The data gathered will add a valuable contribution to the currently limited data available on empirical consumer behaviour research, particularly in relation to the adoption of AR for interactive marketing.

The findings of this study will benefit academics working on the adoption of technology in rapidly developing fields such as automation and artificial intelligence; the study also contributes to the emerging interdisciplinary domain of psychology, information systems, marketing and human behaviour.

After reading the case, the students shall be able to explain the concept of insider trading and differentiate between illegal insider trading and legal insider trading, business ethics, financial institutions, financial markets and accounting; to interpret the legal framework for prevention of insider trading; to identify the role and significance of the market regulator, Securities and Exchange Board of India (SEBI), in detecting financial crimes such as insider trading; to demonstrate the association between information, stock trading and stock prices within the framework of efficient markets; and to appraise the ethical dilemma in a family-owned firm, where the family members of the promoter group are alleged to have indulged in a financial crime.

The case revolves around allegations of insider trading against the promoter and the promoter group of the family owned and controlled firm, Lux Industries Limited. On January 24, 2022, the SEBI, the regulator of securities markets in India, accused Udit Todi, the Executive Director of Lux Industries Limited, of engaging in insider trading through a chain of 14 connected parties. Udit Todi was also the son of the Managing Director, Pradip Kumar Todi, and the nephew of the Executive Chairman, Ashok Kumar Todi. In its interim order, SEBI alleged a breach of insider trading regulations by a group of 14 connected entities that had built up long positions starting from May 21, 2021, before the quarterly financial results (Q4) and the annual results of the financial year (FY) 2021 in the equity shares of Lux Industries Limited, with its registered office in Kolkata, India, were announced. Subsequently, they squared off the long positions to make a profit of ₹29.43m. To restore the confidence of the investors, the Executive Chairman, Ashok Kumar Todi, needed to review the matter expeditiously and impartially. Taking into consideration the family ties of the accused, it was not going to be an easy task, yet, it had to be done. The case highlights the role of the regulator, SEBI, in unearthing financial frauds such as insider trading in an emerging market such as India.

Postgraduate programs in management, Executive education programs.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance