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This paper aims to present a low-cost, edge-fed, windmill-shaped, notch-band eliminator, circular monopole antenna which is practically loaded with a complementary split ring resonator (CSRR) in the middle of the radiating conductor and also uses a partial ground to obtain wide-band performance.

To compensate for the reduced value of gain and reflection coefficient because of the full (complete) ground plane at the bottom of the substrate, the antenna is further loaded with a partial ground and a CSRR. The reduction in the length of ground near the feed line improves the impedance bandwidth, and introduced CSRR results in improved gain with an additional resonance spike. This results in a peak gain 3.895dBi at the designed frequency 2.45 GHz. The extending of three arms in the circular patch not only led to an increase of peak gain by 4.044dBi but also eliminated the notch band and improved the fractional bandwidth 1.65–2.92 GHz.

The work reports a –10dB bandwidth from 1.63 GHz to 2.91 GHz, which covers traditional coverage applications and new specific uses applications such as narrow LTE bands for future internet of things (NB-IoT) machine-to-machine communications 1.8/1.9/2.1/2.3/2.5/2.6 GHz, industry, automation and business-critical cases (2.1/2.3/2.6 GHz), industrial, society and medical applications such as Wi-MAX (3.5 GHz), Wi-Fi3 (2.45 GHz), GSM (1.9 GHz), public safety band, Bluetooth (2.40–2.485 GHz), Zigbee (2.40–2.48Ghz), industrial scientific medical (ISM) band (2.4–2.5 GHz), WCDMA (1.9, 2.1 GHz), 3 G (2.1 GHz), 4 G LTE (2.1–2.5 GHz) and other personal communication services applications. The estimated RLC electrical equivalent circuit is also presented at the end.

Because of full coverage of Bluetooth, Zigbee, WiFi3 and ISM band, the proposed fabricated antenna is suitable for low power, low data rate and wireless/wired short-range IoT-enabled medical applications.

The antenna is fabricated on a piece (66.4 mm × 66.4 mm × 1.6 mm) of low-cost low profile FR-4 epoxy substrate (0.54 λ_g × 0.54 λ_g) with a dielectric constant of 4.4, a loss tangent of 0.02 and a thickness of 1.6 mm. The antenna reflection coefficient, impedance and VSWR are tested on the Keysight technology (N9917A) vector network analyzer, and the radiation pattern is measured in an anechoic chamber.

This paper aims to present the design development and measurement of two aerodynamic slotted X-bands back-to-back planer substrate-integrated rectangular waveguide (SIRWG/SIW) to Microstrip (MS) line transition for satellite and RADAR applications. It facilitates the realization of nonplanar (waveguide-based) circuits into planar form for easy integration with other planar (microstrip) devices, circuits and systems. This paper describes the design of a SIW to microstrip transition. The transition is broadband covering the frequency range of 8–12 GHz. The design and interconnection of microwave components like filters, power dividers, resonators, satellite dishes, sensors, transmitters and transponders are further aided by these transitions. A common planar interconnect is designed with better reflection coefficient/return loss (RL) (S₁₁/S₂₂ ≤ 10 dB), transmission coefficient/insertion loss (IL) (S₁₂/S₂₁: 0–3.0 dB) and ultra-wideband bandwidth on low profile FR-4 substrate for X-band and Ku-band functioning to interconnect modern era MIC/MMIC circuits, components and devices.

Two series of metal via (6 via/row) have been used so that all surface current and electric field vectors are confined within the metallic via-wall in SIW length. Introduced aerodynamic slots in tapered portions achieve excellent impedance matching and tapered junctions with SIW are mitered for fine tuning to achieve minimum reflections and improved transmissions at X-band center frequency.

Using this method, the measured IL and RLs are found in concord with simulated results in full X-band (8.22–12.4 GHz). RLC T-equivalent and p-equivalent electrical circuits of the proposed design are presented at the end.

The measurement of the prototype has been carried out by an available low-cost X-band microwave bench and with a Keysight E4416A power meter in the microwave laboratory.

The transition is fabricated on FR-4 substrate with compact size 14 mm × 21.35 mm × 1.6 mm and hence economical with IL lie within limits 0.6–1 dB and RL is lower than −10 dB in bandwidth 7.05–17.10 GHz. Because of such outstanding fractional bandwidth (FBW: 100.5%), the transition could also be useful for Ku-band with IL close to 1.6 dB.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

The purpose of this paper is to identify various factors influencing additive manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them.

The methodology includes three phases, namely, identification of factors through systematic literature review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify it by deriving the interrelationship between the factors using interpretive structural modeling (ISM), followed with the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis.

This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors, namely, manufacturing flexibility, operational excellence and firm competitiveness are placed at the top level of the model, which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom-level factors.

Additional factors may be considered, which are important for AM implementation from different industry contexts. The variations from different industry contexts and geographical locations can foster the theoretical robustness of the model.

The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors.

This paper contributes by identification of AM implementation factors based on in-depth literature review as per SLR methodology and validation of these factors from a variety of industries and developing hierarchical model by integrative ISM-MICMAC approach.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

This paper aims to analyze the characteristics of stochastic volatility processes in globally listed private equity (LPE) markets, which are represented by nine global, regional and style indices, and reveals transmissions in the conditional variances between the different markets, based on weekly data covering the period January 2011 to December 2020.

The study uses the generalized autoregressive conditional heteroscedasticity [GARCH(p, q)] model and its exponential GARCH (EGARCH) and GARCH-in-mean extensions.

The estimates of the volatility models GARCH, EGARCH and GARCH-in-mean GARCH-M for testing the stylized properties persistence, asymmetry, mean reversion and risk premium lead to very different results, depending on the respective LPE index.

The knowledge of conditional volatilities of LPE returns as well as the detection of volatility transmissions between the different LPE markets under investigation serve to support asset allocation decisions with respect to risk management or portfolio allocation. Hence, the findings are important for all kinds of investors and asset managers who consider investments in LPE.

The authors present a novel study that examines the conditional variance for globally LPE markets by using LPX indices, offering valuable insight into this growing asset class.

This study aims to analyse the conditional volatility of the Vietnam Index (Ho Chi Minh City) and the Hanoi Exchange Index (Hanoi) with a specific focus on their application to risk management tools such as Expected Shortfall (ES).

First, the author tests both indices for long memory in their returns and squared returns. Second, the author applies several generalised autoregressive conditional heteroskedasticity (GARCH) models to account for asymmetry and long memory effects in conditional volatility. Finally, the author back tests the GARCH models’ forecasts for Value-at-Risk (VaR) and ES.

The author does not find long memory in returns, but does find long memory in the squared returns. The results suggest differences in both indices for the asymmetric impact of negative and positive news on volatility and the persistence of shocks (long memory). Long memory models perform best when estimating risk measures for both series.

Short-time horizons to estimate the variance should be avoided. A combination of long memory GARCH models with skewed Student’s t-distribution is recommended to forecast VaR and ES.

Up to now, no analysis has examined asymmetry and long memory effects jointly. Moreover, studies on Vietnamese stock market volatility do not take ES into consideration. This study attempts to overcome this gap. The author contributes by offering more insight into the Vietnamese stock market properties and shows the necessity of considering ES in risk management. The findings of this study are important to domestic and foreign practitioners, particularly for risk management, as well as banks and researchers investigating international markets.

The paper aims to investigate the determinants of China’s daily intervention in the foreign exchange market since the 2005 reform aimed at moving the Renminbi (RMB) exchange rate regime towards greater flexibility.

The paper uses bivariate probit models to test whether China’s intervention decision is driven by three sets of factors, comprising Model I (basic model), Model II and Model III.

Evidence from the models suggests that medium-term Chinese interventions tend to be leaning-against-the-wind, whereas long-term interventions are leaning-with-the-wind. Furthermore, by analyzing exchange rate volatility, this paper finds that intervention is used by the Chinese central bank to ensure that there are no big swings in the RMB exchange rate.

The paper will be of value to other researchers attempting to understand the policy of the central bank and, in particular, the factors that can lead to interventions during periods of financial crisis.

The purpose of this paper is to examine the extent to which Indian consumers of different demographic groups vary in terms of shopping mall visits (frequency of visit, hours spent in the mall, and number of shops visited) and purchase behaviour (total money spent, number of shops purchased from and number of items purchased).

The study used a self-administered survey of 400 Indian mall shoppers to examine Indian shoppers’ behaviour with respect to visiting and buying behaviour. Descriptive analyses and χ² tests were conducted to identify patterns and capture the significant relationships in shopping behaviour across different demographic segments.

The results show that shoppers of different age cohorts and from different household sizes behave differently from one another in a significant manner. In terms of gender, however, men and women tend to behave in a similar manner in terms of visit frequency, time and money spent per visit. The study also provides insight into where the differences occur and between which specific groups.

Data comes from one major city of India which limits the generalizability of the results.

For mall managers and retailers, the study findings indicate that the stores that serve recreational needs should focus more on younger segments, where men and women share similar buying patterns. Findings from this study could also be used for segmentation exercises and to build strategies to convert footfall into actual purchase, especially within the rapidly growing Indian mall market.

The study adds value to the body of retail literature and provides empirical evidence from the rapidly developing Indian market. The study also provides insight into where differences occur and between which specific groups. By highlighting the differences in greater detail, the study benefits retailers in general and specifically, mall managers.

The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.

Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.

It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.

This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.

The humanitarian supply chain (HSC) area is rich with conceptual frameworks with a focus on the importance of information and digital technology (IDT) applications. These frameworks have a limited scope in investigating and prioritizing barriers to IDT adoption in HSCs. The present study thus identifies and prioritizes the barriers to IDT adoption in organizations involved in HSCs.

By using a literature review allied with expert discussions and a fuzzy analytic hierarchy process (F-AHP), the study identifies and prioritizes a comprehensive set of barriers that organizations involved in HSCs may consider to improve IDT adoption.

The study investigates five main barriers (strategic, organizational, technological, financial and human) interlocked with 25 sub-barriers impacting the level of IDT adoption in organizations involved in HSCs. The findings indicate that strategic barriers (SBs) are of greatest importance, followed by organizational, technological, financial and human barriers. The findings indicate the difference in ranking barriers influencing the adoption of IDTs in HSCs compared to the commercial supply chain.

Although a three-step method adopted for this study is rigorous in terms of the way this research is conducted, it is essential to report that prioritization is based on the subjective opinions of the experts.

The findings aim to assist policymakers and practitioners in developing effective strategies to improve IDT adoption in organizations engaged in HSCs. Moreover, the prioritization of barriers provides a systematic way to overcome any barriers to improve HSC performance.

This study is first of its kind that investigates and prioritizes the barriers to IDT adoption in HSCs.

Supply chain is the area that requires effective and integrated means of communication, shared risk, collaboration and orchestration in order to work successfully and the cloud computing has lot to offer to this domain. Cloud computing has appeared as a modern paradigm in supply chain networks for creating intelligent industries of future. The purpose of this paper is to propose a framework that can transform supply chain stakeholders toward Industry 4.0.

Cloud computing is attributed with increasing competitiveness by focusing on cost reduction, greater elasticity, flexibility and maximum utilization of resources which results in successfully achieving business goals. This paper proposes a Hybrid Supply Chain Cloud model, which integrates the infrastructure, the resources and the configurations of platforms.

This research paper is aimed at proposing a hybrid cloud that essentially helps in integrating the supply chain network with the flexibility and efficiency. It is important that a supply chain network adds value to ensure customer satisfaction and this can be best achieved by collaborating it with hybrid cloud.

This research provides a consistent central management and comprehensive view of all computing resources, which gives organizations the knowledge they need to optimize workload placement.

The findings derived from this research aim to facilitate policy makers and practitioners to develop effective courses of action in current and future supply chain management. Therefore, upon implementation, this model can provide long-term benefits for the organizations by improving the overall efficiency of its supply chain network.

The proposed hybrid cloud will provide deep level of integration in Industry 4.0 situation and thereby brought up portable comprehensive infrastructure based on resources and required configuration in real-time environment.

The purpose of this study is to determine if brand passion shapes attitudinal brand loyalty while driving a series of important brand-related outcomes (i.e. brand advocacy, social media following, sense of community, willingness to pay a premium price and alternative devaluation). These aspects are explored for sports apparel brands after considering the perceptions of Iranian consumers.

This study is based on the analysis of survey data gathered online and face-to-face from a sample of Iranian consumers of sports apparel brands that were analysed using partial least square path modelling.

The key empirical findings obtained confirm that brand passion underpins attitudinal brand loyalty and several important brand-related outcomes. Furthermore, the findings show that attitudinal brand loyalty explains the impact of brand passion on most of the outcomes considered, except for social media following.

This study advances knowledge of brand passion by illustrating its “power” as a strong nuance of relationships between consumers and brands. In particular, this study highlights the importance of brand passion in shaping attitudinal brand loyalty, as well as a driver of several outcomes of theoretical and managerial relevance.

By establishing strategies aimed at enhancing brand passion, brand managers can increase attitudinal brand loyalty, attain important goals such as brand advocacy, premium price and social media following, as well as the devaluation of competing brands.

This study uses a unidimensional theorisation of brand passion to increase the understanding of its role as predictor of attitudinal brand loyalty and driver of relevant outcomes. It also examines the mediating effect of attitudinal brand loyalty, thus illustrating important conceptual links between brand passion and brand loyalty in the context of sports apparel brands in a growing economy (Iran).

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

The business enterprises are increasingly focusing on buying and supplying of products and services in a manner to reduce the adverse impacts on the environment, society, and economy. In view of the above, the concept of sustainable supply chain management (SSCM) has received attention of the industry and academia due to its importance on environmental, social and corporate responsibility through economic performance. The paper aims to discuss these issues.

The structured literature review attempts to map the various theories in the SSCM literature from the perspectives of economic performance, environmental dimensions, and social values and ethics.

As supply management is vital for enhancing organizational competitiveness, the present work attempts to investigate the theoretical perspectives in SSCM to develop an understanding of the current research activities and future potentials.

This work aims to gain a number of valid insights for the practitioners and the researchers. It also focuses on the perspectives of governance mechanisms for successful implementation SSCM practices in the business enterprises.

As the theory building initiatives with implications on the conceptualization of SSCM is limited in literature, this work has also been able to identify the trends and relevant research gaps to define the potential areas for future research.

This study aims to investigate the impact of sustainable supply chain practices on sustainability performance in North American and Canadian firms in a business-to-business (B2B) context, specifically focusing on the mediating role of emerging technologies. It aims to deepen the understanding of this complex relationship, contributing to both theoretical knowledge and practical applications.

This study collected data from supply chain managers in the USA and Canada using a mixed-methods approach that includes partial least squares structural equation modeling (PLS-SEM), necessary condition analysis (NCA) and importance-performance map analysis (IPMA). PLS-SEM was utilized to model the relationships between sustainable practices, emerging technologies and sustainability performance. NCA identified the essential conditions required for sustainability performance, while IPMA was used to assess the importance and performance of different constructs, helping to pinpoint areas where the managerial focus can yield the most significant improvements.

This study reveals that sustainable supply chain practices (SSCP) alone do not directly lead to enhanced sustainability performance. SSCP includes product design, procurement, investment recovery and social sustainability. Sustainability performance includes economic, environmental and social performance. Instead, adopting specific emerging technologies, particularly artificial intelligence, wearable devices and virtual reality, is crucial. A significant threshold identified is these technologies’ 80% adoption rate for substantial performance improvements. Furthermore, this study distinguishes the varying impacts of different technologies on economic, social and environmental aspects of sustainability.

This research offers new insights by showing that emerging technologies fully mediate the relationship between SSCP and performance. It expands on existing literature by detailing the specific impacts of various technologies, moving beyond the generalized approach seen in prior research. Specific impacts of emerging digital technologies on SSCP and performance remain underexplored in a B2B environment, and this research aims to address this gap.

The purpose of this paper is to review phytochemical potential of acacia and its associated health advantages. Acacia a moderate-sized, deciduous tree and recognised as health-promoting species because of availability of essential bioactive components. The bioactive compounds such as tannins, flavonoids, alkaloids, fatty acids and polysaccharides (gums) present in the plant parts of acacia, namely, bark, leaves, flowers, fruits, twigs and seeds, have medicinal value and thus are used to overlay the formulations of plant-based drugs and value-added foods.

Major well-known bibliometric information sources such as Web of Science, Scopus, Mendeley and Google Scholar were searched with keywords such as “nutrition value of acacia”, “bioactive compounds”, “health benefits”, “processing and safety” were chosen to obtain a database of 1,428 papers. The search considered papers in the English language from the past 18 years of publication in journals (2004–2022). The article selection process consisted of the screening of titles and abstracts, based on inclusion and exclusion criteria. Articles that did not have acacia components as a study objective were taken into consideration for exclusion. A final database of 87 scientific sources was made after sorting and classifying them according to different criteria based on topic relevance, country of origin and year of publication. Articles with other random descriptors were also searched to complement the discussion of the results obtained.

The literature reflected that acacia contains all necessary phytochemicals like polyphenols, flavonoids, terpenoids, glucosinolates, alkaloids and carotenoids along with essential macro, micro-nutrients. Furthermore, processing methods such as soaking, cooking, roasting and dehusking significantly reduced the anti-nutritional factors present in acacia seeds of different species. This review also focused on the processing methods that are used to eliminate or lower down the anti-nutritional factors from the seeds. Previous findings related to acacia plant parts with respect to food development are explored and mentioned.

This review emphasised mainly on recent studies that had been reported on ethnomedical acacia plants therapeutically, commercially and exponentially for further studies to increase the utilisation in food processing.

Collaboration methods are unique strategies that can help organizations hedge against external and internal supply chain risks without stressing their relationships with supply chain partners. However, selecting the most appropriate collaboration method from a given set of strategies is a multifaceted challenge. This paper aims to address this issue.

The decision maker's dilemma of fighting data uncertainty in input parameters to check the efficacy of a given collaboration or mitigation approach is tackled by the integration of Grey theory with the technique for order of preference by similarity to ideal solution (TOPSIS) method. The proposed technique is applied and tested for an Indian diesel generator-set manufacturer to identify the most apposite set of sustainable collaboration strategies.

The results showed that when a firm is bidding for different horizontal collaboration strategies across its supply chain system technology and resource-sharing-centered collaboration strategies are the prominent option. In the case of the company's vertical collaboration deployment, the focus should be kept on information sharing to achieve impactful collaboration. The outcome of the analysis helped the Indian manufacturer to adopt transparent order and production information sharing with its regional distributors and core suppliers within its supply chain.

This study demonstrates from a methodological perspective the successful application of the Grey-TOPSIS approach that effectively captures data uncertainty. It also integrates sustainability parameters in collaboration strategy criteria selections.

Crude oil, gold and interest rates are some of the key indicators of the health of domestic as well as global economy. The purpose of the study is to find the shock volatility and price volatility effects of gold and crude oil market on interest rates in India.

This study finds the mutual and directional association of the volatility of gold, crude oil and interest rates in India. The bi-variate GARCH models (Diagonal VEC GARCH and BEKK GARCH) are applied on the sample data of gold price, crude oil price and yield (interest rate) gathered from November 30, 2015 to November 16, 2020 (weekly basis) to investigate the volatility association including the volatility spillover effect in the three markets.

The main findings of the study focus on having a long-term conditional correlation between gold and interest rates, but there is no evidence of volatility spillover from gold and crude oil on the interest rates. The findings of the study are of great importance especially to the policymakers, as they state that the fluctuations in prices of gold and crude oil do not adversely impact the interest rates in India. Therefore, the fluctuations in prices of gold and crude may generally impact the economy, but it has nothing to do with interest rate in particular. This implies that domestic and foreign investments in the country will not be affected by gold and crude oil that are largely driven by interest rates in the country.

Gold and crude oil are two very important commodities that have their importance not only for domestic affairs but also for international business. They veritably influence the economy including forex exchange for any nation. In addition to this, the researchers believe the findings will provide insights to policymakers, stakeholders and investors.

Gold and crude oil undoubtedly influence the exchange rates but their impact on the interest rates in an economy is not definite and remains ambiguous owing to the mixed findings of the studies. The lack of studies related to the impact of gold and crude oil on the interest rates, despite them being essentials for the health of any economy is the main motivation of this study. This study is novel as it investigates the volatility impact of crude oil and gold on interest rates and contributes to the existing literature with its findings.

Kaleidofin was co-founded in 2017 by Puneet Gupta and Sucharita Mukherjee; former CFO and CEO of IFMR (Institute for Financial Management and Research) Holdings Pvt Ltd. As part of their roles at IFMR, Gupta and Mukherjee focused on designing products and developing technology to push for financial inclusion. In their field interactions, the co-founders had an epiphany of the challenges faced by people while trying to save towards important life goals. They saw an opportunity in the large segment of financially under-served people in India and quit their jobs to start Kaleidofin. Kaleidofin was conceptualised as a digital platform that offers customised financial solutions to help customers meet their life goals. The start-up partnered with mutual fund companies for solutions on one hand and network partners (NGOs, microfinance organizations, cooperative banks) on the other for access to their existing customers.

Kaleidofin grew from 50 customers in January 2018 to 15,000 customers by March 2019. Aiming to grow to 1 million customers in the next 30 months Kaleidofin faces a dilemma about its future course. The start-up could continue to grow by expanding its current target segment which is the low-income households and preserve its vision at the risk of increasing costs. The second option would be to look at other potential target segments, such as, middle-income households and risk diluting their vision. The case study highlights the unique customer-centric model of Kaleidofin and the need for start-ups to understand the value proposition of their products/services.

This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.

The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.

This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.

The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.

To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.

In recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based system. The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm (FPA) using concepts of fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

The fuzzy logic-based parameter adaptation in the FPA is proposed. In addition, type-2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics, which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method, and, in reality, the effectiveness of the interval type-2 fuzzy inference system (IT2 FIS) has shown to provide improved results as matched to type-1 fuzzy inference system (T1 FIS) in some latest work.

One case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature. For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statistical analysis which validates the advantages of the interval type-2 fuzzy FPA. The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.

The main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type-2 fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

Understanding of quick commerce (q-commerce) and its relevance in modern retail.

Analysis of market entry strategies and competitive landscapes.

Insights into operational challenges and solutions in rapid delivery services.

Exploration of consumer behavior shifts and business adaptation in response to global events.

The case examines Zepto’s innovative approach in the quick commerce sector, emphasizing its strategies, challenges and the impact of the pandemic on its operations and growth.

This case study is suitable for undergraduate-level students, particularly in courses related to business management, e-commerce, marketing and supply chain management.

Teaching notes are available for educators only.

CSS 11: Strategy.

The purpose of this study is to assess the lean performance of a tyre manufacturing firm in India. The key objective is to find key strategic and operational decision‐making dimensions for developing effective lean manufacturing environment in a tyre manufacturing organization.

This research is empirical in nature where the opinions of a group of experts of an Indian tyre manufacturing firm were consulted to formulate an interpretive structural model (ISM) of the critical success factors of lean manufacturing implementation in a tyre manufacturing organization. The authors have developed an Excel‐based template for quantitative assessment of lean performance indicators on the basis of feedback from the operational staff.

This research work suggests that financial capability of an organization drives the top‐management commitment for incorporating lean manufacturing practices in a tyre manufacturing organization. Organizational culture and human resource management are the important enablers for developing change management paradigm, which in turn leads to performance improvement. This study also reveals that over‐processing as well as excessive defects are the most detrimental wastes in radial tyre manufacturing, which accounts for high manufacturing cost of radial tyre manufacturing in India.

Since this study is carried out in a single (case) organization, a relatively small sample size restricts the outcome from being considered for generic industrial application. This study none the less is useful for practicing managers and academicians for the development of lean manufacturing strategy in context with the tyre industry since it encompasses insightful views of experienced lower to upper middle level managers.

This paper provides some key enablers for the successful implementation of lean tools in Indian tyre manufacturing, where lean practices are still in the early stage and little literature is available in this context with tyre manufacturing. Also an attempt has been made to develop a simple Excel‐based template for lean assessment in the tyre industry. This template can be used in other industries by simply modifying the key attributes.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

This paper investigates the causality among gold prices, crude oil prices, bitcoin and stock prices by using daily data from January 2014 to December 2021. The study also examines the data during the COVID-19 outbreak from January 2020 to December 2021.

To estimate the long- and short-run causality, this study considers the nonlinear autoregressive distributed lag (NARDL) cointegration test.

The analysis found the existence of an asymmetric long-run cointegration among selected assets. Findings indicate that positive changes in bitcoin do not affect stock market in the long term. Changes in crude oil prices have a significant impact on stock prices. Moreover, it is observed that variations in the stock prices trigger a negative impact on gold prices. During the COVID-19 period, the study notices the presence of an asymmetric long-term cointegration between selected assets except bitcoin. Besides, findings revealed that negative price adjustments in gold lead to significant positive shocks in stock market.

These results provide critical information for policy performers and researchers to develop new strategies. Policy regulators can also consider the potential effects of the COVID-19 outbreak while developing strategies for investment decisions.

The purpose of this paper is to examine the pricing efficiency of the Malaysian crude palm oil (CPO) market before and after the structural break. This study uses the daily closing price of CPO and CPO futures (CPO-F) for the period ranging from June 2009 to August 2016 while taking structural breaks into account.

In this study, symmetric and asymmetric long-run relationship model are employed, such as the Johansen cointegration, VECM, TAR and M-TAR models, to examine the impact of structural breaks on the pricing efficiency of the Malaysian CPO market.

This finding establish that Malaysian CPO price is efficient before and after the structural break. The consistent efficiency of the Malaysian CPO market supports the trading of the CPO-F in Globex and the use of Malaysian CPO pricing as the reference price. This study establishes that a structural break in the Malaysian CPO price series does not affect the pricing efficiency of the market.

This study shows that using Malaysian CPO price as a reference price is sustainable even in the event of a structural break. Therefore, market participants in the Malaysian CPO market have less to worry about the CPO price as it supports the weak form of efficiency. Price deviation in the short run may not lead to arbitrage profit as transaction cost may not be covered.

This study implies that if there is distortion in the price due to shocks, both manufacturers and producers need to hedge their positions in the futures market (subject to their positions in the underlying market). By entering into the futures market, pricing is locked in advance; hence, price risk is eliminated. Such a distortion could also affect the efficiency of the CPO price, therefore this study also addresses the issue of efficiency of the local CPO market.

Previous studies on Malaysian CPO pricing efficiency did not take the effect of structural break into consideration, making it difficult for these studies to show consistency in the efficiency of the Malaysian CPO market.

BigBasket.com, an online supermarket established in December 2011 in Bangalore, India, had become one of the major players in the Indian online grocery market by the end of March 2016.₁ Run by Innovative Retail Concepts Private Limited, BigBasket.com was operating in more than 23 cities across the country in 2016. The online grocery market in India was in a stage of growth and transformation, fuelled by India's large urban population who sought a lifestyle of convenience and ease. It had also attracted many entrepreneurs who competed fiercely with each other in a market characterised by thin margins. Intense competition ensured that only a few companies were able to survive and sustain themselves. One of these companies was Big Basket, which succeeded in spite of the competition, attracting Series D_a funding worth USD 150_b million from the United Arab Emirates-based Abraaj Group in March 2016.₂

The purpose of this paper is to summarize the scientific information of various qualities of bael fruit juice used in traditional system of medicine for variety of purposes. Utilization of bael fruit juice in day-to-day life has great nutritional, therapeutic, and commercial importance. Bael fruit contains nutrients like vitamins (riboflavin), minerals, trace elements, energy and phytochemicals, including flavonoids, polyphenols and antioxidants, that have been shown to have varied health benefits. In past few decades, bael has been extensively studied for its medicinal properties by advanced scientific techniques, and a variety of bioactive compounds like marmelosin, tannins, alkaloids, coumarins, steroids, rutacine, y-sitosterol, psoralin, xanthotoxin, scopolotein, aegelemine, aegeline, marmeline, fragrine, dictamine, cinnamide and different derivatives of cinnamide have been isolated from its fruit juice.

The medicinal value of bael fruit is very high when the harvests just begin to ripen. As a result, it has a high demand as alternative medicine for curing the diseases like diabetes, high cholesterol, peptic ulcer, inflammation, diarrhea and dysentery, constipation, respiratory infection. Furthermore, the bael fruit juice has anticancer, cardio protective, antibacterial, antifungal, radio protective, antipyretic, analgesic, antioxidant, antiviral, anthelmintic and anti-inflammatory, hepatoprotective, wound healing properties. The ripe fruit juice is aromatic, has cooling and laxative effects, and arrests secretion or bleeding.

The unripe or half-ripe fruit juice is good for digestion, useful in preventing or curing scurvy, and it strengthens the stomach action. It helps in the healing of ulcerated intestinal surfaces and has appreciable activity against intestinal pathogenic organisms. The present review summarizes the scientific information of various qualities of bael fruit juice used in traditional system of medicine for a variety of purposes.

It is quite evident from this review that bael is an important medicinal herb and extensively used in Ayurveda, Siddha and other medicinal systems. Bael fruit juice is an excellent source of water and natural sugar and is important principally for containing vitamins, minerals, phytochemicals, antioxidants, pigments, energy, organic acids, dietary fiber and other food components, which are the key factors in the medicinal value of this plant. Moreover, mechanisms of action of a few bioactive compounds have been identified so far.

To assess the state of supply chain management (SCM) research in India and to understand the research trends and methodologies used. The present study also aims to create a taxonomy of the subject areas researched in India.

The present study employed the systematic literature review methodology. Literature from 395 peer journal papers in 67 leading journals over a 20-year period (2000–2020 Quarter-1) was comprehensively reviewed and assessed.

SCM research in India started around the year 2000. The quantum of research was low (single digit) until 2010. There has been steady growth over the last decade, and over 50% of the total papers up until now has been published in the last four years. The present study created a three-tiered taxonomy of the subject areas and classified the papers as per it. The first tier (level-1) has seven categories (SCM strategy, network design, SCM processes and integration, IT systems, skills, performance measurement and others). A perusal of the newly created taxonomy revealed that, except for a few areas under level-1 categories (such as SCM processes and SCM strategy), the other level-1 categories have not seen much research. Similarly, there is little or no research in a large number of level-2 categories (such as outsourcing strategy, channel strategy, demand management, demand fulfillment, customer relationship management, integrated supply chain planning, new product development, returns, supply chain orientation, performance monitoring, performance improvement, SCM adoption process, SCM implementation issues and quantified benefits of SCM). Methodologically, the rigor of SCM research in India needs improvement.

A comprehensive taxonomy of SCM subject areas researched in India at three cascading levels was created for the first time in the present study. The taxonomy will help provide researchers with a clear understanding of the structure of the subject areas and help in identifying areas where research has been carried out and the subject areas where gaps exist for future research to proceed. The present study also provides an overview of the methodological rigor of SCM research in India and points out some of the limitations that researchers should avoid in future studies.

The purpose of this paper is to examine the impact of the future of trading on volatility as well as the efficiency of the stock market of BRIC (Brazil, Russia, India and China) countries. This study also investigates the presence of day‐of‐the‐week effect in BRIC countries' stock market.

This study uses closing prices of IBrx‐50 for Brazil, RTSI for Russia, Nifty for India and CSI300 for China to represent the stock market of BRIC countries. The Run and ACF tests are used to see impact on market efficiency. GARCH M model is used to see the impact on volatility and day‐of‐the week effect.

The insignificant coefficient of variance in the conditional mean equation of GARCH M implies that the market doesn't provide higher returns during the high volatility period. The results of the Run test showed that the Russian stock market became efficient after introduction of future trading. However, ACF showed no effect of introduction of future trading on autoregressiveness of stock returns. The result of GARCH M indicates that future trading led to reduction in the volatility of the Indian stock market. There are some evidences of presence of day‐of‐the‐week effect in the Indian stock market.

This paper will help regulators to form appropriate policies as the market would have to pay a certain price, such as loss of market efficiency, for the sake of market stabilization. This will also help investors to make investment decisions, especially investing in these indices as the existence of the significant day‐of‐the‐week effect and the inefficiency in the stock market would be very useful for developing investment strategies.

This paper will be useful for both investors and regulators in decision making.

The purpose of this paper is to focus on studying the concept of “Smart Organization” and providing a comprehensive framework for the various factors as barriers for the smart organization, identifying and classifying the key criterion of these factors based on their direct and indirect relationships.

In this paper an extensive literature survey and experts’ opinion have been used to identify major barriers of smart organization. These barriers are then modeled using interpretative structural modeling (ISM) methodology. The model so developed has been further improved and an integrated model has been developed using fuzzy-MICMAC.

Various barriers of smart organization have been identified and a structural model has been developed for barriers using the ISM methodology. The critical barriers have been found out by fuzzy-MICMAC analysis. The driver power and dependence graph has been plotted for barriers. The barriers are classified into four categories which are, autonomous, linkage, dependent and independent according to their driver power and dependence. From the ISM model and the integrated model, and from further discussions with the experts, it has been found that the barriers “(B1) organizational structure” and “(B6) Managerial actions” are the two most important barriers, every other barrier is directly or indirectly driven by these.

The basis of developing the ISM model, i.e, the structural self-interaction matrix is based on experts’ opinion, therefore the result may get influenced if there is any biasing in judging the barriers. The future research scope for this paper will be to test the model generated in this paper. The testing of the model can be done by applying structural equation modeling technique, it has the capability of testing the hypothetical model. Further a framework of smart organizations can be created to find out the smartness of different organizations.

The paper can be used by organizations in understanding the barriers in becoming “smart” on the basis of their inter-relationships. This model can help manufacturing organization of North India in understanding the barriers which needs to be worked upon and the inter-relationship among these factors. This model-based study may be helpful in understanding and implementing the practices of smart organization by removing the possible critical barriers.

This is the first study to identify the barriers of smart organizations and to develop a model of these barriers using ISM and fuzzy-MICMAC.

Building a robust cold chain logistics system boosts the company’s profits in various ways. Any cold chain logistics company needs well-organised and efficient management of cold chain logistics to produce high-quality products, ensure that the product reaches the customer without any changes to the quality, and do so promptly. This paper aims to identify factors influencing cold chain logistics performance design. These factors are further helpful in analysing the behaviour intentions of stakeholders on increasing the cold chain logistic performance.

The authors conducted a thorough literature review to identify the variables that affect the performance of the cold chain logistics design. The factors were identified using exploratory factor analysis and empirically analysed using confirmatory factor analysis. The study also used structural equation modelling (SEM) to examine cold chain logistics performance determinants. Data was collected from 380 respondents working in the cold chain.

This study selected the factors influencing CCL performance, including five main factors and 22 sub-factors. Distribution, warehouse inventory storage, quality, demand, and technology affect the CCL’s performance. The results confirmed the theoretical model and proved that the factors significantly positively impact CCL performance.

Future studies should focus on actual case studies to confirm the usefulness of the parameters found, examine how they affect performance growth, provide important insights into how to improve overall business performance and assist in identifying crucial research hotspots.

The study provides insight into issues regarding performance development in cold chain logistics for various stakeholders associated with the cold chain logistics industry, including practical managers, academics, and consultants. It also argues in favour of giving problems with CCL performance a higher priority. Policymakers interested in the service sector, like the Indian Department of Commerce and MSMEs, make up a modest additional audience for this work.

Indian meat industry can be organised by implementing this methodology. This work benefits the government to get more transparent transaction and data digitalisation, which comes into account of GST.

There is a lack of significant quantitative literature suggesting modification strategies for factors affecting processed meat and chicken products in storage and transportation levels in India. Thus, this work tried to fill this gap and add the food chain logistics literature that helps practitioners and scholars enhance the food supply chain in developing countries.The framework developed for this study is where its originality lies. A detailed examination of cold chain logistics is included in the paper.

In recent years, economic, environmental and social sustainability has become one of the fastest-growing research fields. The number of primary and secondary papers addressing the triple bottom line is growing significantly, and the supply chain (SC) management discipline is in the same wave. Therefore, this paper aims to propose a novel tertiary systematic methodology to explore, aggregate, categorise and analyse the findings provided by secondary studies.

A novel tertiary systematic literature review approach, including 94 secondary studies, is proposed and used to analyse sustainable SC literature. The papers have been analysed using a research protocol, including descriptive and content analysis criteria.

This tertiary study does not only provide an overview of the literature on the topic of sustainability in SCs but also goes further, drawing up a categorisation of main research areas and research perspectives adopted by previous researchers. The paper also presents a rank of research gaps and an updated and a prioritised agenda.

This paper provides a novel interpretation of the research topics addressed by the secondary studies and presents a new classification of the literature gaps and their evolution. Finally, a dynamic research compass for both academicians and practitioners is presented.

Airtel, the leading mobile operator in India was going to launch the “Airtel Zero” platform that would charge service providers and OTT providers on the internet for mobile data traffic but would allow end consumers free access to the web sites that were signed up for the platform. The case revolves around the questions of pricing these data services to the service providers in a market where the price to one set of customers (the end consumer) was not independent of the price to another set of customers (the OTT service providers) - typical of two sided markets. Issues of net neutrality and competition have been considered alongside.