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This paper aims to examine the relationship between environmental, social and governance (ESG) performance and text-based corporate innovation based on a sample of India’s ESG-disclosed companies from financial year 2011–2012 to 2021–2022. Further, it endeavors to investigate the moderating role of heightened climate policy uncertainty (CPU) in this relationship.

To verify these hypotheses, the authors first construct a corporate innovation index for India using a sophisticated natural language processing model on each firm-year’s management discussion and analysis reports. Next, the authors use a panel fixed effects model to examine how ESG performance impacts corporate innovation and its moderating and mediating components.

Empirical evidence suggests higher ESG performance bolsters text-based corporate innovation. After addressing endogeneity issues with the system GMM estimator and two-stage least square IV, incorporating additional control variables and using alternative innovation measurement, the baseline results remain unchanged. Next, the authors find this link is mediated by reducing information asymmetry, financial constraints and managerial myopia. The authors also observe that increased CPU favorably moderates the ESG-innovation nexus. Additionally, the heterogeneity research shows that ESG only positively impacts innovation in specific industries and firms in their growth and mature life cycle phases.

The results demonstrate that sustainable and ethical business practices can foster corporate innovation. Thus, this study may provide valuable insight for investors, managers and policymakers.

To the best of the authors’ knowledge, this is the first study to examine the relationship between ESG performance and text-based corporate innovation using a machine learning model.

This paper investigates the relationship between managerial sentiment and corporate investment in emerging capital markets. Further, we begin with the assertion that the positive impact of managerial sentiment on corporate investment varies according to the corporate life cycle. Lastly, we investigate whether the relationship between managerial sentiment and corporate investment can be moderated by factors like (1) economic policy uncertainty/geo-political risk, (2) size of the firm, (3) financial constraint, (4) industrial competition, and (5) Environmental Social and Governance (ESG) rating.

This study has considered Indian listed companies (465 firms) for the period spanning from 2003–2004 to 2022–2023. This study constructs the managerial sentiment using a novel large language model-financial bidirectional encoder representation from the Transformers (FinBERT), as well as on management discussion and analysis reports. Then, we employ fixed effect regression to investigate the relationship between managerial sentiment and corporate investment. Additionally, we use propensity score matching, two-stage least squares instrumental variables, and a two-step system generalized method of moments approach for robustness tests.

The findings show a positive and significant relationship between managerial sentiment and corporate investment. Additionally, our results demonstrate that this relationship is evident only during the growth and maturity phase of the corporate life cycle. Moreover, uncertainty pertaining to the economy and geopolitical issues, firm size, financial health, industry dynamics, and ESG disclosure also play a crucial role in shaping the investment-sentiment relationship.

The study is unique because it determines the relationship between managerial sentiment and corporate investment by using the novel FinBERT model. In addition, we have introduced a corporate life cycle, which is an essential aspect of our study. Additionally, this research was conducted in an emerging market with more information asymmetry and weaker disclosure rules. Thus, other emerging markets can benchmark the outcomes.

This study aims to examine the impact of manager sentiment on the firm performance (FP) of Indian-listed nonfinancial firms. Further, it endeavors to investigate the moderating role of economic policy uncertainty (EPU) and environment, social and governance (ESG) transparency in this relationship.

A noble manager sentiment is introduced using FinBERT, a bidirectional encoder representation from a transformers (BERT)-type large language model. Using this deep learning-based natural language processing approach implemented through a Python-generated algorithm, this study constructs a manager sentiment for each firm and year based on the management discussions and analysis (MD&A) report. This research uses the system GMM to examine how manager sentiment affects FP.

The empirical results suggest that managers’ optimistic outlook in MD&A corporate disclosure sections tends to present higher performance. This positive association remains consistent after several robustness checks – using propensity score matching and instrumental variable approach to address further endogeneity, using alternative proxies of manager sentiment and FP and conducting subsample analysis based on financial constraints. Furthermore, the authors observe that the relationship is more pronounced for ESG-disclosed firms and during the low EPU.

The results demonstrate that the manager sentiment strongly predicts FP. Thus, this study may provide valuable insight for academics, practitioners, investors, corporates and policymakers.

To the best of the authors’ knowledge, this is the first study to predict FP by using FinBERT-based managerial sentiment, particularly in an emerging market context.

The unorganised manufacturing sector contributes one third share of overall manufacturing employment and one fifth share of gross value added of the manufacturing sector. Despite its important role in large-scale employment generation, this sector is neglected by the researchers as well as by the policy makers as compared to the focus given on the organised manufacturing sector. The issues of energy intensity, environment emissions and growth of unorganised manufacturing enterprises (UMEs) remain unexplored. The present chapter attempts to estimate the CO₂ emission and emission intensity (EI) across UMEs on the basis of NSSO Unit Level data of 62nd, 67th and 73rd rounds. It also analyses the growth of UMEs in relation to CO₂ emission and EI. The nature of the sector is very much dispersed. Our study reveals that a portion of unorganised enterprises did not use any energy in their production activities and used manually operated instruments like – handlooms, weaving machines, hand-operated oil and rice mills, etc. The main energy inputs of UMEs are electricity and fuel & lubricants. The CO₂ emission is relatively less in UMEs compared to organised manufacturing enterprises. Across the unorganised manufacturing industries, the higher CO₂ emission are observed in manufacturing of food product industry and other non-metallic mineral industry. The study found that CO₂ EI of UMEs depends on firm-level characteristics like perennial nature, establishment type, urban location and expanding growth status. However, capital intensive UMEs are more polluting.

The purpose of this paper is to examine the classic characteristics of highly cited articles (HCAs) of top-ranked library and information science (LIS) journals and get acquainted with the high-quality works in specific areas of LIS for distinguishing what gets cited and who the prolific authors are.

The HCAs published across the top four LIS journals were downloaded, coded and a database was developed with basic metadata elements for analysis using bibliometric indicators. Lotka’s Inverse Square Law of Scientific Productivity was applied to assess the author’s productivity of HCA. The content analysis method was also used to find out the emerging areas of research that have sought high citations.

Inferences were drawn for the proposed five number of research questions pertaining to individual productivity, collaboration patterns country and institutional productivity, impactful areas of research. The Netherland found to be the potential player among all the affiliating countries of authors and Loet Leydesdorff tops the list among the prolific authors. It is observed that Lotka’s Classical Law also fits the HCA data set in LIS. “Research impact measurement and research collaboration,” “Social networking” and “Research metrics and citation-based studies” are found to be the emerging areas of LIS research.

Researchers may find a way what gets cited in specific areas of LIS literature and why along with who are the prolific authors.

This study is important from the perspective of the growing research field of the LIS discipline to identify the papers that have influenced others papers as per citation count, spot the active and more impactful topics in LIS research.

This study aims to analyze the structure and trend in employment in the Indian economy between 1980-8081 and 2015-2016.

Use of India KLEMS data set. Estimate growth rate of employment and discuss employment prospects using “Point” employment elasticity.

Whilst India’s GDP growth rate has been quite impressive since the reforms of 1991, the rate of employment growth, especially in the recent period of 2003-2015, has been quite slow (1 per cent) with low employment elasticity (0.1). The pattern of employment growth has also been imbalanced with slow rate of employment growth in manufacturing and rapid growth rate in the construction sector. India now also has low labour force participation rate and a large share of informal employment in the economy.

The limitation is the lack of reliable data on employment for the recent period.

With overall low employment elasticity, India would have to explore sectors where more employment opportunities could be created.

India has to create not only more jobs but also “good” jobs.

The India KLEMS data provide a time series for employment, which has been used in this paper to find “Point” elasticity instead of arc elasticity of employment and is an improvement over existing employment elasticity estimates.

This study aims to examine the investment-cash flow sensitivity (ICFS) and the impact of environmental, social and governance (ESG) on ICFS of manufacturing firms in India. Furthermore, it explores the role of group affiliation in such ESG–ICFS nexus.

The paper uses the generalized method of moments regression to analyze the data with a sample of 222 manufacturing firms from 2012 to 2022.

The paper reveals that Indian manufacturing firms mainly depend on internal cash flow for their investment decision, and ESG footprint reduces such sensitivity of investment-cash flow. Furthermore, group-affiliated firms have greater ICFS, and the impact of ESG on ICFS is more noticeable in group-affiliated firms than in standalone counterparts.

This paper provides valuable insights into current literature, with implications that extend to economies, firms, managers and investors. To the authors’ knowledge, this paper examining the impact of ESG on ICFS amidst group affiliation is first-of-its-kind.

AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees.

The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM.

This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”.

This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory.

Call it the curse, the myth, the legend, the dacoits, the flora and fauna, the River, and the valley remains untouched and pristine and is home to many endemic species and is blessed with stark but beautiful landscape, the surreal world of an era gone by, the ravage of the ravines, which have been preserved, protected, and conserved, albeit for the ‘ancient curses,’ and yes being a home to the ‘dreaded outlaws’ and ‘modern dacoits.’ Whether it was the fear for one’s life considering it was a dacoit area or also known as ‘gun powder area,’ or the curses that none wanted to garner to themselves, Chambal is slowly but steadily ebbing out of the dark shadows that it once was. The river itself passes through Madhya Pradesh, Rajasthan, and Uttar Pradesh, before joining the river Yamuna. Declared a Protected Area (PA) in 1978, the nomenclature of dark tourism will be ubiquitous to the Chambal River valley, considering that the area is dotted by a multitude of forts, and heritage destinations, that have frozen in the sands of time; either on account of the stories that meander, as the river does, with its deep ravines, ravines that invite the adventurer. It is the last bastion for the nation’s endangered wildlife, viz., Gharial, Maggar, Turtles, Otter, and the fresh water Dolphin, which has encouraged the Government to get the local community to be part of the special purpose vehicle promoting tourism. The authors known for their contribution to community-based ecotourism (CBE) have visited the Chambal area and worked with the communities and bureaucracy to ensure conclusions.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.