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The aim of the paper is to investigate Goodwin’s growth cycle in the Indian organised manufacturing industries.

The methodology is based on bi-variate differential equation, econometrics model like log-linear regression and Autoregressive Distributed Lag model. An empirical investigation is conducted on data from the Annual Survey of Industries from 1980 to 2018 time period.

The results indicate that though the original Goodwin model estimates deviated from data estimates, its modified (neo-Goodwin) model are found to be equivalent to the data estimates. Moreover, in contrast to the original model, the capital accumulation rate (investment to profit ratio) is not assumed to be unitary in the modified Goodwin model. Furthermore, the labour market-led and cost effect conditions of the Goodwin cycle are empirically verified by investigating the interdependency between employment rate and wage share. Lastly, the short- and long-run Goodwin cycles are observed to be moving in anti-clockwise direction in the employment rate and wage share bi-dimensional plane, thus confirming the existence of profit-led distribution where wage share continuously reducing with high employment.

This study opens the discussion on application of capitalistic model in the emerging economy and also suggests to incorporate some theoretical models like Kaldorian, Keynesian, Kaleckian or Schumpetrian into the Goodwin cycle.

This is the first paper which empirically examines the capitalistic nature of Indian organised manufacturing industries through the lens of Goodwin growth cycle and then extend it to the Neo-Goodwin model by relaxing one of the unrealistic assumption regarding unitary investment to profit ratio.

This book chapter examines how the evolution of the tourist and hospitality industries has been impacted by digital technologies. Digitalisation makes it possible to implement resilient infrastructure in all applications to achieve sustainability. The hospitality and tourism sector is known to be information-rich due to its diverse commercial interactions with shareholders and constant evolution of managerial processes, modification of tourist and hotel services, advancements in technology and an intensely competitive atmosphere. It has been recognised that growth of tourism and hospitality industry becomes impossible without the applications of modern digital technologies that enable integration and communication, boost service quality and transmit a sizeable amount of information. In the context of the tourism sector, the establishment of resilient infrastructure that relies on digital technologies is vital in order to obtain optimal consumer feedback pertaining to the provision of high-quality service. The utilisation of digital technology has demonstrated its capacity to enhance hospitality services through the facilitation of real-time data-driven decision-making. The objective of this study is to emphasise the importance and practical uses of the internet of things (IoT), artificial intelligence (AI), cloud computing and big data in the context of consumer quality and satisfaction. This study also addresses the significance of various technologies and their applications in attaining consumer quality and pleasure in the digital realm. Additionally, it might help company owners, managers and marketers in any field to achieve and improve high business performance by executing the right plans to use AI that fulfil the demands and expectations of both customers and staff.

India is an abode of several ancient heritage and sacred cities. However, limited heritage sites have been appropriately documented to date. Hence, limited information is available on the historical development, existing settings and challenges of ancient heritage cities. Puri, a prime sacred and heritage city, is selected to demonstrate the historical and spatial development and current issues of ancient Indian heritage cities.

Site visits, documentation of the existing settings and interviews with stakeholders and historians were conducted to understand this city's evolution and existing issues. Also, content analysis is administered to rank the stakeholders’ perceptions of the current challenges in the town.

The results reveal that the scarcity of resources, encroachment of public spaces, insufficient infrastructure and lack of maintenance of heritage structures are the major challenges for Puri. Also, the absence of collaboration between the involved agencies and heritage site-specific guidelines hinders the city’s redevelopment.

This research discusses the development phases and current issues of a prime heritage city in India. This knowledge will help urban planners and policymakers formulate policies for Puri's holistic development.

This is the first comprehensive research on historical development, current issues, and the reasons for any heritage city in Odisha state of India. This paper will help the academic literature to understand the chronological growth pattern in ancient, medieval, colonial, and post-colonial periods, as well as current issues and changing heritage imageability of Indian heritage sites of a similar nature.

Banks play a vital role in the economy. Investigating their competitive environment is crucial to ensuring economic stability and development. The FinTech disruption has risks and opportunities for incumbent banks, and it can be valuable to investigate its effects on banking performance. Therefore, the aim of this study is to assess whether investment in FinTech is associated with better performance of Indian banks during 2012–2018.

To do this, a sample of Indian banks was investigated between 2012 and 2018 using k-means and hierarchical cluster analysis, ANOVA, and pairwise comparison tests.

Results of the analysis strongly suggest that investment in FinTech is associated with better banking performance. Higher FinTech investments, represented by mobile transaction volume, are associated with higher efficiency scores and accounting-based performance. In particular, banks that invest in FinTech and have relatively low non-performing loans have a 7.7% higher Return on Employment (ROE) than banks with exceptionally low FinTech use and no significant investment in smart branches.

Therefore, it can be recommended that Indian banks adopt a forward-looking strategic approach when making investment decisions regarding new technologies. Failing to adapt to the FinTech disruption may result in poor value creation prospects in the long run.

To the best of the authors' knowledge, this is the first study that analyses. We are not aware of any similar study on whether investment in FinTech is associated with better performance of the Indian banks during 2012–2018.

New business density (NBD) is the ratio of the number of newly registered liability corporations to the working-age population per year. NBD is critical to assessing a country's business environment. The present work endeavors to discover and gauge the contribution of 28 potential socio-economic enablers of NBD for 2006–2021 across developed and developing economies separately and to make a comparative assessment between those two regions.

Using World Bank data, the study first performs exploratory data analysis (EDA). Then, it deploys a deep learning (DL)-based regression framework by utilizing a deep neural network (DNN) to perform predictive modeling of NBD for developed and developing nations. Subsequently, we use two explainable artificial intelligence (XAI) techniques, Shapley values and a partial dependence plot, to unveil the influence patterns of chosen enablers. Finally, the results from the DL method are validated with the explainable boosting machine (EBM) method.

This research analyzes the role of 28 potential socio-economic enablers of NBD in developed and developing countries. This research finds that the NBD in developed countries is predominantly governed by the contribution of manufacturing and service sectors to GDP. In contrast, the propensity for research and development and ease of doing business control the NBD of developing nations. The research findings also indicate four common enablers – business disclosure, ease of doing business, employment in industry and startup procedures for developed and developing countries.

NBD is directly linked to any nation's economic affairs. Therefore, assessing the NBD enablers is of paramount significance for channelizing capital for new business formation. It will guide investment firms and entrepreneurs in discovering the factors that significantly impact the NBD dynamics across different regions of the globe. Entrepreneurs fraught with inevitable market uncertainties while developing a new idea into a successful new business can momentously benefit from the awareness of crucial NBD enablers, which can serve as a basis for business risk assessment.

DL-based regression framework simultaneously caters to successful predictive modeling and model explanation for practical insights about NBD at the global level. It overcomes the limitations in the present literature that assume the NBD is country- and industry-specific, and factors of the NBD cannot be generalized globally. With DL-based regression and XAI methods, we prove our research hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators. This research justifies the robustness of the findings by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.

This study aims to empirically examine the effects of smart cities on sustainable development for the period 1990–2019 for Türkiye.

The relationship between smart cities and sustainable development was analyzed with the help of the ARDL Bounds Test. In addition, the consistency of the model was tested with the FMOLS estimator. The indicators of the smart city were selected following the literature to represent smart cities, and the author created the smart city index. The study used other variables thought to impact sustainable development as secondary data.

The results show that smart cities positively and significantly impact sustainable development in Turkiye in both models during the sampling period. In addition, while real GDP, population density, and financial development variables positively affect sustainable development, population density has a negative effect on sustainable development, according to the results obtained from FMOLS estimators.

The first novelty of this study is the creation of the smart city index. The second novelty is that there are almost no studies on the effects of smart cities on sustainable development, especially for Türkiye.

This study examines the existing literature on generative artificial intelligence (Gen AI) and its impact across many sectors. This analysis explores the potential, applications, and challenges of Gen AI in driving innovation and creativity and generating ideas.

The study adopts a comprehensive literature review approach, carefully assessing current scientific articles on Gen AI published from 2022 to 2024. The analysis examines trends and insights derived from research.

The review indicates that Gen AI has significant potential to augment human creativity and innovation processes as a collaborative partner. However, it is imperative to prioritize responsible development and ethical frameworks in order to effectively tackle biases, privacy concerns, and other challenges. Gen AI is significantly transforming business models, processes, and value propositions in several industries, but with varying degrees of effect. Findings indicate also that despite the theory-driven approach to investigating Gen AI's creative and innovative potential, cutting-edge applications research prioritizes examining the possibilities of Gen AI models.

Although this review offers a picture of great possibilities, it concurrently underlines the necessity for a deep knowledge of Gen AI nuances to fully harness its capabilities. The findings indicate that continuous research and exploration efforts are required to address the challenges of Gen AI and assure its responsible and ethical implementation. Therefore, more study is needed on enhancing human-AI collaboration and defining ethical norms for varied circumstances.

This study presents a relevant analysis of Gen AI's transformational potential as an innovation catalyst. It emphasizes major potential, applications across industries, and ethical issues for responsible integration.

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

This study aims to find out how pomposia fruit powder (Syzygium cumini L.) mixed with roasted coffee (RCO) affected antioxidants, phytochemicals, lipid peroxidation inhibition and sensory attributes.

Pomposia fruits (Syzygium cumini L.) powder (PFP) was integrated with RCO at levels of 0.0%, 5.0%, 10.0%, 15.0% and 20.0%. RCO, PFP and binary mixtures containing RCO: PFP were evaluated for their total phenolics (TP), total flavonoids (TF), anthocyanin content (AC), antioxidant activity and phenolic compounds fractionation. The oxidative indices of coffee oil samples were measured throughout different storage intervals. Additionally, sensory characteristics of RCO enriched with different PFP doses were evaluated.

PFP samples exhibited the greatest TP content (1910 mg/100 g), whereas RCO samples had the lowest concentration (1090 mg/100 g). As more PFP was added, the corresponding coffee blends’ concentrations of TP, TF and AC were improved significantly. PFP possesses a radical-scavenging activity that is about 1.20 times more than RCO’s. DPPH (2,2-diphenyl-1-picrylhydrazyl) radical-scavenging activity jumped significantly from 74.13% in control (untreated) samples to 77.64%, 78.39% and 80.15% for samples enriched with 10.0%, 15.0% and 20% PFP, respectively. Significant increases in gallic acid, hesperidine, benzoic acid, chlorogenic acid, hispertin, catechol, quercetin, pyrogallol and rutin were detected when RCO was mixed with different quantities of PFP. At the end of storage trial, the coffee oil treated with 20% PFP had AV, PV and TBA values that were about 1.70, 1.95 and 1.66 times lower, respectively, than those of the control sample that had not had PFP addition. The RCO with 5.0% PFP achieved the greatest over acceptability grades.

To the best of the authors’ knowledge, this study was the first study to evaluate the effect of incorporating various level of pomposia fruit powder into RCO. The findings shows that adding different concentrations of pomposia fruit powder into RCO can indeed enhance the radical-scavenging activity of the coffee and potentially extend its shelf life.

Tech start-ups (TSs) functioning in different domains have a responsibility of ensuring that domestic knowledge and capabilities are leveraged to minimize dependence on foreign organizations. Despite the growth of the ecosystem, while numerous TSs emerge, very few of them are able to survive, and of those that survive, very few scale up. The aim of this study is to identify the factors influencing the competitiveness of technological start-ups and to study the interrelationship and interdependence of these factors.

Modified total interpretative structural modeling (m-TISM) was employed for the current research. The analysis of what factors have an effect on competitiveness, how they affect it and why they affect it should be explored. The study begins by developing the list of factors through literature search, and further it is validated by expert opinion. A hierarchical model has been developed using m-TISM and MICMAC analysis to analyze the driving and dependency power of factors at each level.

Results show that the competitiveness of TSs is affected by organizational agility and internationalization. Factors present at the bottom level, namely entrepreneurial intensity, act as a strong driver for TSs. Team member commitment, transformational leadership, strategic alliances, knowledge sharing and organizational ambidexterity are middle-level factors.

This study is among the few articles that have explored competitiveness of TSs in the Indian context.