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Motivated by the evidence highlighting the role of sentiments and cognitive biases in investors' decision-making, this study examines a novel behavioral finance-based asset pricing model incorporating the prospect theory framework in the Indian equity market. Specifically, the study’s primary objective is to investigate the importance of Prospect Theory Value (PTV) in the cross-sectional pricing of stocks.

The empirical findings rely on data taken from NIFTY 500 and BSE S&P 500 stocks, encompassing daily, weekly and monthly observations. The analysis employs diverse statistical techniques, including Ordinary Least Squares (OLS), Fama–Macbeth Cross-section Regressions, Panel Fixed Effect and Quantile Regression.

The study demonstrates an asymmetric association between PTV and subsequent stock returns. The findings maintain their robustness even when factoring in stock-specific attributes such as market capitalization and book-to-market ratio, market beta and indicators related to lottery-like behavior such as skewness and MAX. This observed pattern persists when analyzing data at various frequencies, including daily, weekly and monthly intervals. Loss aversion behavior dominates among Indian equity investors, contrary to lottery preferences in the US equity market.

As far as the authors are aware, the study is the first to introduce a new behavioral finance-motivated stock return predictor (PTV) in the Indian stock market. The study also marks the pioneering use of a novel method that evaluates the predictability of PTV across various sections of the conditional return distribution using quantile regression.

This case study is related to start-up post-listing investment analysis. Through this case study, students will be able to perform the business analysis guided by the Venture Evaluation Metric tool, perform financial analysis using the discounted cash flow methods and perform investment analysis recommendation with justifications from the business and financial analysis performed above.

This case study sets out the study of a scalable start-up, Zomato, which is a successfully listed start-up firm in India. Despite the start-up development success in the pre-listing, the firm has exhibited a continuous unprofitable finance performance in the post-listing and has further experienced a volatile share price performance, both of which have puzzled existing and potential investors. In addition, some analysts are in the opinions that the firm share price valuation have been inflated with overvaluation since in the initial public offering stage and remain traded with overvaluation in the market. Notably, considering the negative indicators mentioned above, investors are concerned about long-term sustainability of the firm business and financial performance. In the context of post-listing investment, the following questions are material to investors: What is the realistic growth trajectory for Zomato in the medium term? What is Zomato’s share fair value in the medium term? Can one see opportunities or risks ahead of investing in Zomato’s shares? What will be the investment strategy for new investors?

This case study is suited to bachelor’s and master’s level in business schools studying entrepreneurial finance analysis.

Teaching notes are available for educators only.

CSS 1: Accounting and finance.

Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.

The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.

Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.

The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.

The purpose of this paper is to examine how exporters’ time-out periods and re-entry to various export areas impact their knowledge stock and capacity to learn from foreign markets.

This paper introduces the concept of innovation divergent export areas (IDEXAs), which refers to a group of countries with relatively similar average levels of innovation capabilities (intra-area homogeneity), and different from other areas (inter-area heterogeneity), as measured by their R&D expenditures over gross domestic product (GDP). This paper tests the hypotheses on a longitudinal sample of Spanish manufacturing companies that exported to different IDEXAs from 1990 until 2016.

The findings suggest a positive effect of IDEXA re-entry on new product and process introductions and a negative impact of a time-out period of four or more years for those export areas with higher innovation levels.

Re-internationalization offers exporters the opportunity to reuse the knowledge gained in prior exporting episodes to increase their chances of success. Hence, it is important that managers make sense of the potentially damaging exit experience, to avoid repeating the same mistakes and perform better the next time around.

This study investigates for the first time the effects of re-entry to specific export areas on exporters’ capacity to increase their innovation output. Hence, it contributes to the international business literature by examining the performance consequences of companies’ re-internationalization, a key and under-researched topic. Furthermore, most studies focus on full withdrawal from foreign markets and ignore the more common microscopic decisions concerning withdrawing from one or more export areas.

While extant literature explores the influence of institutions on the national innovation system (NIS), most research has either focused on specific institutional aspects or treated institutions as a unified entity. This study aims to examine the effect of various institutional factors on a country’s NIS.

The conceptual model was empirically validated using regression analysis. The study sample comprised a total of 84 countries.

This study identifies and empirically validates a comprehensive set of institutional factors. It also highlights the significant institutional factors (including political stability, government effectiveness, ease of resolving insolvency and the rule of law) that can help improve a country’s NIS.

The research provides practical implications for organizations and policymakers seeking to understand and foster an innovative culture within the NIS. Policymakers are encouraged to develop a nurturing environment within the NIS by focusing on significant institutional factors. Organizations are encouraged to closely monitor developments in the NIS of a country to make informed strategic decisions at the business, corporate and international levels.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.