Search results

The study attempts to look into the poverty and deprivation in slums across various social and religious groups and its bearing on the children. It not only analyses income poverty but also looks at derivation of access to basic services including water, sanitation and drainage. The purpose of this paper is to compare and contrast the income and non-income deprivation of childbearing and non-childbearing households.

The study is based on a survey of 541 sample households selected from 23 slums of Kolkata, India. The authors have adopted a mixture of cluster sampling and systematic sampling technique. The slums of Kolkata have been segregated into three regions and further segregated by overlaying the population and average monthly income of slums. Slums have been selected randomly from these stratums. Households have been selected from the slums by systematic sampling method.

The Muslim and backward caste households are more deprived with respect to income and access to basic services as compared to Hindu general (upper) caste. Deprivations with respect to income and basic services are more pronounced for households having child than for households not having child. Childbearing households are less likely to receive better water supply, sanitation and drainage services as compared to others due to their religious and residential identities. Slum children get affected by the complex political economy of basic service delivery. The study also finds that electoral competition has positive and political clientelism has negative impact on access to basic services.

The study is based on results obtained from survey in one city of India. Hence, these results cannot be generalized for India or for the developing countries taken together. Further studies across cities of developing countries are required to arrive at any generalized conclusion.

The study suggests that public policies should attempt to disentangle minorities and children from the local political economy. Otherwise, deprivation and disparity even across low income households living in slums would persist. Deprivation of child bearing households would lead to a deteriorating future for the slum children.

This paper have pave the path for new generation public policy for the urban poor and minorities.

This paper highlights the incidence of deprivation of minorities and childbearing households vis-à-vis other households in the slums. It contributes to the overall understanding on urban poverty.

It is essential to validate whether a nation's economic strength always transpires into new business capacity. The present research strives to identify the key indicators to the proxy new business ecosystem of countries and critically evaluate the similarity through the lens of advanced Fuzzy Clustering Frameworks over the years.

The authors use Fuzzy C Means, Type 2 Fuzzy C Means, Fuzzy Possibilistic C Means and Fuzzy Possibilistic Product Partition C Means Clustering algorithm to discover the inherent groupings of the considered countries in terms of intricate patterns of geospatial new business capacity during 2015–2018. Additionally, the authors propose a Particle Swarm Optimization driven Gradient Boosting Regression methodology to measure the influence of the underlying indicators for the overall surge in new business.

The Fuzzy Clustering frameworks suggest the existence of two clusters of nations across the years. Several developing countries have emerged to cater praiseworthy state of the new business ecosystem. The ease of running a business has appeared to be the most influential feature that governs the overall New Business Density.

It is of paramount practical importance to conduct a periodic review of nations' overall new business ecosystem to draw action plans to emphasize and augment the key enablers linked to new business growth. Countries found to lack new business capacity despite enjoying adequate economic strength can focus effectively on weaker dimensions.

The research proposes a robust systematic framework for new business capacity across different economies, indicating that economic strength does not necessarily transpire to equivalent new business capacity.

This paper aims to examine optimal decisions for information security investments for a firm in a fuzzy environment. Under both sequential and simultaneous attack scenarios, optimal investment of firm, optimal efforts of attackers and their economic utilities are determined.

Throughout the analysis, a single firm and two attackers for a “firm as a leader” in a sequential game setting and “firm versus attackers” in a simultaneous game setting are considered. While the firm makes investments to secure its information assets, the attackers spend their efforts to launch breaches.

It is observed that the firm needs to invest more when it announces its security investment decisions ahead of attacks. In contrast, the firm can invest relatively less when all agents are unaware of each other’s choices in advance. Further, the study reveals that attackers need to exert higher effort when no agent enjoys the privilege of being a leader.

In a novel approach, inherent system vulnerability of the firm, financial benefit of attackers from the breach and monetary loss suffered by the firm are considered, as fuzzy variables in the well-recognized Gordon – Loeb breach function, with the help of fuzzy expectation operator.

This study reports that the optimal breach effort exerted by each attacker is proportional to its obtained economic benefit for both sequential and simultaneous attack scenarios. A set of numerical experiments and sensitivity analyzes complement the analytical modeling.

In a novel approach, inherent system vulnerability of the firm, financial benefit of attackers from the breach and monetary loss suffered by the firm are considered, as fuzzy variables in the well-recognized Gordon – Loeb breach function, with the help of fuzzy expectation operator.

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

A large number of organisations are moving towards adopting Industry 4.0 (I4.0), and simultaneously, the emphasis on attaining sustainability development goals is also increasing. Hence, it is imperative to understand the interplay between I4.0 and sustainability. However, the literature addressing the same is still in infancy. Accordingly, the purpose of this study is to fill this gap in the literature by exploring the potential sustainability impacts of I4.0 on the organisations and society in terms of sustainability risks.

To gain an understanding of sustainability aspects in the I4.0 context, relevant literature is gathered using Scopus and Web-of-Science database. An in-depth review of 51 research papers is performed to determine the sustainability risks associated with I4.0.

From the study, a total of 16 sustainability risks are identified, and I4.0 sustainability risk taxonomy is developed. The proposed taxonomy extends the sustainability implications of I4.0 beyond the triple bottom line umbrella and includes the organisational perspective as well. Furthermore, the study provides future research avenues to scholars by positing five potential research questions under different risk management stages.

The study provides an understanding of sustainability risks associated with the adoption of I4.0. The findings will help practitioners streamline their production and operation processes by finding out possible solution to the sustainability risks of their smart factories in advance. The present research will act as a stepping stone towards I4.0 sustainability. The proposed research questions will assist the future researchers in extending the field of I4.0.

To the best of the authors’ knowledge, this is one of the first studies to address the topic of sustainability risks in the context of I4.0.

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.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

Websites, search engines, recommender systems, artificial intelligence and digital libraries have the potential to support serendipity for unexpected interaction with information and ideas which would lead to favored information discoveries. This paper aims to explore the current state of research into serendipity particularly related to information encountering.

This study provides bibliometric review of 166 studies on serendipity extracted from the Web of Science. Two bibliometric analysis tools HisCite and RStudio (Biblioshiny) are used on 30 years of data. Citation counts and bibliographic records of the papers are assessed using HisCite. Moreover, visualization of prominent sources, countries, keywords and the collaborative networks of authors and institutions are assessed using RStudio (Biblioshiny) software. A total of 166 papers on serendipity were found from the period 1989 to 2022, and the most influential authors, articles, journals, institutions and countries among these were determined.

The highest numbers of 11 papers were published in the year 2019. Makri and Erdelez are the most influential authors for contributing studies on serendipity. “Journal of Documentation” is the top-ranking journal. University College London is the prominent affiliation contributing highest number of studies on serendipity. The UK and the USA are the prominent nations contributing highest number of research. Authorship pattern for research on serendipity reveals involvement of single author in majority of the studies. OA Green model is the most preferred model for archiving of research articles by the authors who worked on serendipity. In addition, majority of the research outputs have received a citation ranging from 0 to 50.

To the best of the authors’ knowledge, this paper may be the first bibliometric analysis on serendipity research using bibliometric tools in library and information science studies. The paper would definitely open new avenues for other serendipity researchers.

In the paper, the authors study the simultaneous influence of incentive compatibility and individual rationality (IR) on a multi-echelon supply chain (SC) under uncertainty. The authors study the impact of contract sequence on coordination strategies of a serial three-echelon SC consisting of a supplier, a manufacturer and a retailer in an uncertain environment.

The authors develop a game-theoretic framework of a serial decentralized three-echelon SC. Under a decentralized setting, the supplier and the manufacturer can choose from two contract types namely, wholesale price (WP) and linear two-part tariff (LTT) and it leads to four different cases of contract sequence.

The study show that SC coordination is possible when both the supplier and the manufacturer choose LTT contract. This study not only identifies the influence of contract sequence on profit distribution among SC agents, but also establishes cut-off policies for all SC agents for each contract sequence. This study also examine the influence of chosen contract sequence on optimal profit distribution among SC agents.

Three-echelon SC coordination under uncertain environment depends upon the contract sequence chosen by SC agents.

This study results will be helpful to managers of various SCs to take operational decisions under uncertain situations.

The main contribution of this study is that it explores the possibility of coordination by supply contracts for three-echelon SC in a fuzzy environment.

Entrepreneurship, Strategic management, Management information systems.

Undergraduate and graduate capstone course in entrepreneurship, strategic management or management information systems courses.

This case study of a young technology firm, Codezin Technology Solutions, helps to analyze the challenges faced by such firms in emerging markets. Such markets are characterized by rapid turbulence in the market characteristics. The authors seek to analyze the role of disruptive regulatory changes, resulting in the growth of new startups, in affecting the growth and expansion of such young firms. Codezin was established in 2009 as a bootstrap company, to provide low-cost IT services to Indian small and medium scale enterprises (SMEs). Despite some initial success, it began to run into losses due to poor coordination and improper planning. After a period of struggle, the company stabilized its revenue from services business and expanded to mobile solutions, digital marketing, etc. But then the government of India announced the Startup India initiative at the beginning of 2016 to boost new ventures. Codezin did not qualify as per the government rules and thus failed to use the various incentives offered. Hence, it needs to determine a new strategy to compete with the onslaught of freshly funded startups but with a relative lack of market experience.

With the case discussion, the students will gain rich insights on technology businesses aimed at SMEs and the impact of changes in the regulatory regime in emerging markets like India. Further, they get to step into the shoes of the co-founders and choose between diversification vs new market development strategies, spurred by market disturbances and thinning competitive advantage.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 3: Entrepreneurship.