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This paper offers a fresh perspective on this debate by exploring the direct relationship between a firm’s stock price performance and its CSR activities, placing particular emphasis on the underlying intent or motive behind the CSR initiatives.

This research examines the relationship between a firm’s stock price and its corporate social responsibility (CSR) activities, distinguishing between responsive and adaptive CSR. While responsive CSR, often a response to negative events, elicits immediate positive stock performance, adaptive CSR initially triggers negative stock performance. However, long-term analysis reveals adaptive CSR leads to positive stock performance, especially for family firms. The study challenges the notion of market myopia, suggesting the market values responsive CSR in the short term but recognizes the long-term benefits of adaptive CSR over time. Clear communication about adaptive CSR intentions and benefits may help in accurate market valuation.

This research examines the relationship between a firm’s stock price and its CSR activities, distinguishing between responsive and adaptive CSR. While responsive CSR, often a response to negative events, elicits immediate positive market reactions, adaptive CSR initially triggers negative reactions. However, long-term analysis reveals adaptive CSR leads to positive returns, especially for family firms.

The study challenges the notion of market myopia, suggesting the market values responsive CSR in the short term but recognizes the long-term benefits of adaptive CSR over time. Clear communication about adaptive CSR intentions and benefits may help in accurate market valuation.

First, it expands on previous studies by exploring how the different motivations behind CSR activities lead to varying effects on stock returns. Second, it sheds new light on the subject of market myopia. The findings demonstrate that adaptive CSR initiatives can initially trigger market reactions similar to those caused by perceived over-investment, in contrast to the more favorable response to responsive CSR activities.

Millennials are a vital generational cohort of the Indian population, and understanding their motivation to participate in the stock market is crucial. This study aims to understand the investment decision-making behavior among millennials in the Indian Stock Market.

Using a cross-sectional research design that entails in-depth personal interviews, this study aims to understand the equity investment behavior of millennials. Verbatim texts from interview transcripts were used to analyze the content and arrive at themes.

The study investigated the motivation to enter the stock market and gained insights into how individuals make equity investment decisions considering economic and behavioral dimensions. The basis for stock selection was predominantly on the self-analysis of investors. Multiple stock selection priorities are also discussed. In addition, informants ensured asset diversification and exercised various strategies to overcome emotions. Furthermore, they suffered from various behavioral biases.

Individual investors are the least informed and most impacted stakeholders in the stock markets; therefore, this study contributes fresh insights to enhance their financial security. The paper also examines some noticeable behavioral tendencies retail investors exhibit and gathers helpful strategies for mitigating behavioral biases.

The uniqueness of the research lies in its adoption of a qualitative methodology that uses the investment experience of millennial investors to reveal the components of decision-making behavior and investor psychology. The findings are thereby unique and have significant managerial implications.

This study aims to examine the links between carbon emissions, electric vehicles, economic growth, energy use, and urbanisation in 15 countries from 2010 to 2020.

This study adopts seminal panel methods of moments quantile regression with fixed effects to trace the distributional aspect of the relationship. The reliability of methods is confirmed via fully modified ordinary least squares coefficients.

This study reveals that fossil fuel use, economic activity, and urbanisation negatively impact environmental quality, whereas renewable energy sources have a significant positive long-term effect on environmental quality in the selected panel of countries.

The main limitation of this study is the generalisability of the findings, as the study is confined to a limited number of countries, and focuses on non-renewable and renewable energy sources.

Finally, this study proposes several policy recommendations for decision-makers and policymakers in the 15 nations to address climate change, boost sales of electric vehicles, and increase the use of renewable energy sources.

This study calls for a comprehensive transition towards green energy in the transportation sector, enhancing economic growth, fostering employment opportunities, and improving environmental quality.

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Purpose: In this chapter, we have theoretically investigated the role of artificial intelligence (AI) supported chatbots and virtual assistants in reshape the decision support systems in insurance industry.

Methodology: For this purpose, we adopted a theoretical approach to investigate the bounded rationality theory, technology acceptance model, and sociotechnical systems theory, and draw insights to comprehend the intersection between AI and insurance ecosystem. These theoretical insights were used to develop a “AI-nudge framework for insurance decision support” that explains the role of AI for nudging the users toward insurance-related informed decision-making.

Findings: It was found that through the user interaction, conversations, sociotechnical system dynamics technology acceptance drivers, the AI can nudge the user toward the use of insurance support systems such as chatbots for informed decision-making. Thus, AI must be integrated to the user interfaces for personalized decision support, ethical considerations, and continuous learning mechanisms. We outlined the future trends and presented the directions for future research in the context of AI-enabled chatbots and virtual assistants for insurance decision support.

In today’s competitive era, it has become significant for companies to understand their end consumer and target customers effectively. One of the ways to accomplish this goal is data-driven marketing. The current study seeks to explore the differences between traditional marketing and digital marketing, the pros and cons of data-driven marketing and usage of artificial intelligence (AI) in data-driven marketing. The research objective was met by exploration of published papers in the past 10 years covering the evolution of data-driven marketing, functions of data engineering, application of technology like AI in data-driven marketing and opportunities and challenges. This study is significant as it provides the insight into the relationship between marketing and data engineering and thus helps marketers to frame strategies by leveraging data-driven marketing to improve consumer experience and gain a competitive edge. Moreover, this study is an interdisciplinary study including marketing, engineering and data science. This study focusses on use of innovative methods to improve profitability of business and consumer experience.

This paper tries to retrospect the mounting application of machine learning (ML) and artificial intelligence (AI) in the human resource management area. The document applies bibliometric analysis and uses relational techniques to explore dimensions of documents in the field. The results highlight publication trends, most impactful authors, countries and institutes in the research area. The science mapping along with co-citation and bibliometric coupling analysis revealed major developments in the field. The thematic mapping and trend analysis highlighted the past and emerging trends towards significant and impactful research in the areas of robotics, big data, AI and data analytics. This paper sets the base for future researchers by coordinating and combining various past researches to help in understanding the evolution of ML and AI in human resource management and expansion of knowledgebase.

The ongoing adverse effects of climate change have led scientific think tanks to aim towards achieving net-zero greenhouse gas (GHG) emissions targets with affordable and clean energy (Sustainable Development Goal 7). One of the significant contributors to the escalating emissions rate is the use of conventional vehicles. The uptake of electric vehicles (EVs) is a promising solution for a cleaner economy. However, increased penetration poses various challenges to the power system. There is a need to explore alternatives, such as hydrogen fuel cell vehicles (HFCVs), to use the advantages of both electric and conventional vehicles and bridge the gap between them. However, the transition to hydrogen-based transport requires intensive study of its key benefits and issues, the actions that need to be taken to achieve a changeover concerning light and heavy vehicles and whether such kind of transformation is likely or even possible. This chapter highlights the brief history and mechanics of HFCVs. It further analyses the various benefits and challenges which the technology poses. Additionally, it addresses critical questions regarding the feasibility of the shift towards hydrogen fuel to satisfy the world's rapidly growing energy needs and meet net-zero targets based on real-life applications. This chapter will be a valuable resource for further research, development and education efforts in HCFVs to assist in the rapidly growing transportation needs for automobiles and other vehicles.