Sustainable development goals (SDGs), adopted by the United Nations in 2015, are gaining importance in all the industrial sectors. The attainment of SDGs will lead to simultaneous betterment of human lives, environment and economic prosperity. Therefore, the adoption and implementation of SDGs in the supply chain (SC) activities is the need of the hour. In this regard, there is a need to analyse the extant SC literature focussing on SDGs. This study reviews the articles on SDGs in SC published in last 6 years (2015–2021). A total of 144 articles are shortlisted for the analysis. Bibliometric and network analyses are performed to create an intellectual map of the field and to visualise the network among authors and keywords, respectively. Various emerging research themes are identified using bibliometric coupling analysis. The content analysis of the articles has been performed by considering different activities and stages of SC. Articles are also mapped on various dimensions of SDGs. The strategies associated with adopting sustainable practices in SC to achieve SDGs have also been elucidated. Finally, future research propositions have been presented which can help in achieving SDGs in the SC. © 2022 ERP Environment and John Wiley & Sons Ltd.

In this study, we estimate the order execution probability of a limit-order book (LOB) and analyze its determinants using high-frequency LOB data from the National Stock Exchange (NSE) of India. For this purpose, we propose an algorithm that estimates the LOB execution time. Using a survival function with log-normal distribution, this study analyzes the significant determinants of the limit-order execution times. The average execution probability is found to be higher for stocks belonging to the information technology and telecom sectors. The limit-order execution probability increases with a larger bid-ask spread, lower limit-order size, and deeper opposite order book. On the other hand, multiple factors, including price aggressiveness, inferior price, limit-order size, and spread, have a direct impact on execution times. The findings could help traders understand various factors influencing the probability of execution and execution time of LOBs. This study is unique in that it models limit-order execution using high-frequency tick-by-tick trading data for emerging markets, such as the NSE of India. © 2022 The Authors.

The novel quantile connectedness network method is used to investigate the vulnerability of emerging stock markets to global shocks in the normal, bear and bull markets. The size of the system-wide shock for an emerging market is doubled, while its own shock is halved in the bear and bull markets relative to the normal market and vice versa. As the size of the systemic shock increases in the bear and bull markets, which leads to an increase in the bilateral shock for emerging markets. Although the dollar index emerged as a risk factor only in the normal market, oil is not a risk factor for the emerging market bloc, irrespective of the state of the market. However, the US stock market is a major risk factor for emerging markets in all kinds of market conditions, although the degree of the shock spillover is more pronounced in the normal market than in the bear and bull markets. The robustness of the vulnerability is verified in a time-varying framework. Policy implications are also discussed. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

The study examines the relationship between pandemic-induced financial distress and mental health of entrepreneurs in India. A cross-sectional research design was used, and a survey was conducted among 816 small-scale entrepreneurs of diverse business entities. Structural equation modeling was performed to analyze the data. Both economic hardship and financial threat reported significant positive relationships with depression, anxiety, and stress. Financial wellbeing was found to be negatively related with depression, anxiety, and stress. The study stands among pioneers who have investigated the mental health issues among entrepreneurs during the COVID-19 pandemic. The study provides holistic implications by recommending a collective mechanism that involves individuals, governments, and institutions, for helping small business entrepreneurs cope with the situation, avoid trauma, and have a positive mental health. Future studies can focus on longitudinal data collection to provide better accuracy and consistency. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

This study uses aspect-level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site Twitter from where a total of 4961 tweets about 10 Patanjali branded products have been extracted and analysed. Along with the aspect-level sentiment analysis, an opinion-tagged corpora has also been developed. Machine learning approaches - support vector machine (SVM), decision tree, and naïve bayes - have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. The authors first identify customer preferences and/or opinions about a product or brand by analyisng online customer reviews as they express them on the social media platform Twitter by using aspectlevel sentiment analysis. The authors also address the limitations of scarcity of opinion tagged data required to train supervised classifiers to perform sentiment analysis by developing tagged corpora. © 2022, IGI Global.

This paper examines the interdependence between green bonds and financial markets in the time-frequency domain by utilizing the multivariate wavelet approach and dynamic connectedness through combining Ensemble Empirical Mode Decomposition (EEMD) with Diebold and Yilmaz (2012) spillover framework. The findings of wavelet multiple correlations indicate that the benefits of diversification opportunities are more evident in the short run. The evidence of wavelet multiple cross-correlations reveals that green bonds and financial markets are highly integrated in the long run. The results of the static connectedness framework explain that the direction and magnitude of spillover behave differently across markets. The world stock market is the net spillover transmitter, while the corporate bond market is the net spillover receiver among the selected markets. The green bond market is receiving more but transmitted less volatility in the present study. The evidence on dynamic connectedness measured by the rolling window approach shows that the interconnection between green bonds and financial markets is volatile over time. These pieces of evidence provide implications to global investors having a strong position in the green bonds market in terms of risk management and portfolio decisions. © 2022 Elsevier B.V.

This study discuss the relation between gold demands and employees' salaries. It also examines the linkages between the board of director's remuneration and gold demands. We investigate the association between two forms of gold, i.e., gold jewellery and gold bars & coins between both categories of incomes. There are two sources of income, one of them is from organized sources of income, and another is from unorganized sources of income. However, approximately 92% of the workforce falls under the unorganized sectors, and 8% fall under the organized sectors. Our study is based on the only income of corporate sectors (i.e. formal sectors) which fall under the organized income group. In this study, the demand function is investigated in terms of corporate incomes and the price of the gold. This study provides analytical and empirical linkages between gold demands and corporate income during 2010–2019. It is an unbalanced panel data with 386,312 quarterly board of directors' remuneration from 2424 listed firms. We take quarterly employees' salaries paid by 2223 listed firms to their employees. We test the unit root test to check the stationarity of data. This paper uses the Two-Stage Least Squares Estimation and Error Correction Models to test the research hypotheses. We find a positive relation between gold jewellery and income and no significant association between the gold bars & coins and income. © 2022 Elsevier Ltd

Purpose: This study aims to examine the impact of green operation (measured using the energy intensity of its operations) on the value of corporate firms in stock markets. The authors also examine the channel of such an impact and its implication on a firm's financing choices. Design/methodology/approach: The authors conduct various univariate and multivariate regression analyses on a panel of all non-financial Indian firms listed on the National Stock Exchange from 2010 through 2018. The authors use the sensitivity of investments to the cash flows model to test the financial constraints hypothesis. Findings: The authors’ analysis shows a positive relationship between energy efficiency (firms that consume a lesser amount of energy per unit of sale) and the value of firms in the stock market. The authors empirically attribute this greater valuation to the lesser volatility of stock returns, measured by the standard deviation of daily stock returns. Finally, the authors observe that investments in energy-efficient firms are less sensitive to their internal cash flows. Practical implications: The results suggest that less green firms face greater constraints in accessing finance from external sources and, therefore, depend more on internal than external capital to finance their investments. Hence, managers of such firms can ease their financing pressures by making their operations greener. Originality/value: In this study, the authors examine the implications of green operations on the financing choices of firms. This aspect of going green is important because managers will have enough incentives to invest in green technologies as that would increase their access to external finance and, hence, decrease their financial constraints. © 2022, Emerald Publishing Limited.

Purpose: The purpose of the study is to identify the key antecedents relating to the interaction design of the e-groceryretail mobile applications and offer innovative marketing interventions to facilitate consumer–brand interaction and generate continuous usage intention. Design/methodology/approach: Data were collected from the subjects using a personally administered questionnaire by adopting a non-probability method. The target respondents of this study were individuals who are users of smartphone and have purchased groceries through mobile applications at least once in two months. On the basis of responses received, a sequential predictive analytic method that includes structural equation modelling (SEM) and artificial neural network (ANN) techniques were employed. Findings: The findings of the study highlighted the critical role of collaboration design in harbouring satisfaction and maintaining a regular clientele for e-grocery applications by confirming the presence of complementary mediation. Such validated proposition and tested research model backed by significant methodological rigour advances the research based on post-acceptance behavioural affordances in interactive marketing literature. Originality/value: The study endeavours to understand users' post-acceptance behaviour by analysing the contemporary factors relating to the interaction design of the platform in terms of mobile application attributes that would drive user patronage intentions. Further, the study highlights the pioneering role of collaboration design for e-grocery retailers, as the recent alliances among strategic players to achieve synergistic business leadership have proven to be a game-changing evolution in the industry. © 2022, Emerald Publishing Limited.

We investigated the Granger causal relationship between the consumption of power both at the aggregate and sectoral level and economic growth in India using the frequency domain approach, which would help policy makers seek the efficient allocation of electricity via proper policy initiatives at different frequencies. We find that at the aggregate level, unidirectional causality runs from the total power consumption to economic growth, starting from the second up to the seventh quarter. In the sectoral context, the results are different. Since there is no causality between industrial power consumption and economic growth; therefore, an energy conservation policy can thus be implemented for the industrial sector. Moreover, since a bidirectional causality exists after 15 quarters for the commercial sector, a short-term policy but not an energy conservation policy could also be initiated for this sector. In the industrial and agricultural sectors, a promotional policy should be initiated because a unidirectional causality exists from sectoral power consumption to economic growth. Therefore, different and sector-specific policies would be more appropriate than a single policy for all power sectors in India in order to orient the efficient utilisation of power towards better economic development. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

The article investigates mindfulness mechanisms and leadership attributes as reflected by performing artists. This qualitative study analyses major categories to develop a theoretical framework of mindful leadership development employing performing arts. The present study uses interpretivism to explore respondents’ experiences and unique phenomena. The grounded theory (GT) methodology aided in exploring grounded data and theory construction. Abstract core category ‘immense concentration and self-control’ captured the abstract and inclusive meaning as understood by the participants in this study. The qualitative coding analysis helped explore the data-driven abstract phenomena. Results of the present study exhibited that all performing artists require high concentration and self-control to exemplify mesmerizing Koodiyattam performing arts. Theoretical reflections on the relationships between the relevant categories are provided in depth. The connections of five major categories between mindfulness mechanisms, leadership attributes, motivation components, values and beliefs, and personality dispositions are particularly intriguing. © 2022 Fortune Institute of International Business.

This study investigated the factors that influence the safe disposal intention and behavior of consumers, considering the mobile phone as an example. Specific hypotheses linking the factors that influence safe disposal intention and behavior with the support of extant literature on theories of reasoned action and planned behavior were developed. A questionnaire was designed based on the following factors: awareness, convenience, subjective norms, producer interventions, and regulations that influence the intentions and behavior of consumers in the safe disposal of e-waste. The data was collected by conducting a cross-sectional survey in India. The collected data was analyzed for descriptive statistics and measurement properties and further tested the hypotheses using partial least squares-structural equations modeling (PLS-SEM). All the measures used in the study were found to have satisfactory reliability and validity testing. The findings of hypotheses testing suggest that the factors such as awareness (Std. coefficient = 0.109 with p value < 0.05 level), convenience (Std. coefficient = 0.341 with p value < 0.01 level), subjective norms (Std. coefficient = 0.242 with p value < 0.01 level), producer interventions (Std. coefficient = 0.228 with p value < 0.01 level), and regulations (Std. coefficient = 0.148 with p value < 0.01 level) were found to significantly impact safe disposal intentions. However, the direct effect of regulation on safe disposal behavior was found to be non-significant (Std. coefficient = 0.091). Therefore, the significant finding of our study is that without the intention of behaving in a certain manner, the direct regulations may not influence the consumers to behave as intended. Our study’s results emphasize two areas that may trigger the consumer’s intentions for safe disposal of e-waste. First, the social norms, and second, the importance of producer responsibility in creating the required reverse logistics infrastructure and clear communication to customers for the safe disposal of e-waste. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Purpose: In recent years, customer engagement (CE) with brands, which has been shown to yield enhanced firm sales, competitive advantage and stock returns, has risen to occupy a prominent position in brand management research and practice. Correspondingly, scholars have explored CE’s conceptualization, operationalization and its nomological networks as informed by different theoretical perspectives. However, in spite of important advances, the knowledge structure of the overall corpus of CE research remains tenuous. Therefore, the purpose of this paper is to explore the intellectual structure of CE research. Design/methodology/approach: Based on this gap, this study deploys bibliometric and network analysis to map CE’s literature-based landscape. Using bibliometric analysis, important CE-publishing journals, authors and influential CE articles (2005–2020) are uncovered. Using network analysis, prominent CE themes are also unearthed. Findings: The results document key CE-publishing journals and authors and their respective contributions to the literature. Five CE themes are also identified, including CE measurement/methods, online CE, CE’s value co-creating capacity, CE conceptualization and customer/consumer brand engagement. Further, an agenda for future CE research is provided based on the presented network analysis results. Practical implications: The reported findings generate important implications for brand managers. For example, the identified critical role of online (vs offline) CE offers a range of strategic opportunities, as outlined. Originality/value: This paper offers a pioneering bibliometric and network analysis of the CE literature, thus mapping the field. From the identified CE themes, important avenues for further CE research are also identified. © 2021, Emerald Publishing Limited.

In this preface, we investigate the past, study the present, and look for the future of financial modeling, risk management of energy and environmental instruments, and derivatives based on articles selected in this special issue (SI). We also summarize the significant findings of those articles and identify the research trends. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

This research tries to analyse the herding behaviour in Bombay Stock Exchange (BSE) listed firms in three sectors, namely manufacturing, services (other than financial services), and the real estate and construction sectors. It tries to exhibit the existence of herd behaviour in Indian firms to follow the mean capital structure of their respective sector. OLS estimation with robust and White, Rogers, Newey-West and Driscoll-Kraay standard error was used to examine the existence of herding behaviour in Indian corporates. The finding suggests that strong herding behaviour exists in all the three sectors under study. The study adds to the existing knowledge base on micro, small, medium and large enterprises. Copyright © 2022 Inderscience Enterprises Ltd.

Cardiovascular disease (CVD) or heart disease is one of the most fatal diseases of the world that has been observed through-out the last decade. The prediction of CVD in majority of cases depends on a set of combination of clinical and pathological data represented by either numerical or categorical variables. The categorical medical data may inherently embed prior medical information during its process of categorisation. Whereas the numerical data are flexible for accurate measurements and reading. Hence it is necessary to asses the impact of categorical and numerical features for CVD prediction. In this work, an exhaustive analysis of numerical, categorical and combination of both types of features have been done in context of state-of-the-art machine learning algorithms. The work has compared the boosting algorithms such as Gradient Boosting, Extreme Gradient Boosting (XGBoost), AdaBoost, CatBoost and additionally artificial neural networks, random forest, support vector machines (SVM), decision tree and logistic regression. A soft voting ensemble mechanism with learning algorithms has also been implemented to predict CVD. The current work has used a publicly available and widely used benchmark dataset: Cleveland heart disease dataset (UCI repository). It uses ten different performance metrics which consistently demonstrate that the categorical features outperforms the numerical and combined features. It is further observed that the ensemble learning of SVM + AdaBoost classifiers with categorical features produces optimum performance of CVD prediction. © 2022 Elsevier Ltd

Using the panel dataset of 16 Asian economies for the period 1990 to 2019, we examine the impact of equity market development on the consumption of renewable energy by endogenizing foreign direct investment (FDI), trade openness and economic growth. The novel techniques of panel quantile regression (PQR) developed by Canay (2011), Powell (2015 and 2016) and Machado and Silva (2018) are employed to obtain robust findings for the equity market-renewable energy nexus. Interestingly, our numerical outcomes explain the insignificant impact of equity markets on renewable energy consumption in Asian region. The findings further describe that expansion in trade activities and enhancement in economic growth can significantly reduce the consumption of energy through technical effect. In addition, PQR results reveal that equity market development encourages renewable energy projects at high (0.70) quantile group of countries where equity markets are relatively more developed. FDI encourages the consumption of renewable energy through the promotion of investment activities at lower group of Asian countries where capital is relatively scarce. Given these evidences, our research offers significant value to empirical literature and also provides important suggestions for policy formulations. © 2021

Purpose: The purpose of this study is to examine the role of personality traits (PTs) of individual investors on their investment intention (II). Further, to study the mediating role of overconfidence (OC) bias and financial literacy (FL) on the relationship between PTs and II. Design/methodology/approach: The present study uses the quantitative approach for the data collection from the sample of 327 Indian investors investing in the stock market. The questionnaire was divided into segments to assess the investor’s PTs, OC, FL and II. The PT has been measured using the Big Five Personality Traits. Confirmatory factor analysis was used to test the reliability and validity of the constructs. The hypothesis was tested using structural equation modeling. Findings: Findings of the study show that the PTs of an individual investor are associated with FL and II but insignificant with OC bias. Further, the FL and OC bias have a positive and significant influence on II. In addition, the mediation analysis showed that FL partly mediates the relationship between PTs and II. Practical implications: The present study is helpful for financial companies, government, personal finance advisors and individual investors; they can keep in mind the behavior-related traits that can influence the investment decisions and design the portfolio accordingly. The policy-makers can implement programs on FL to enhance investment decisions in India. Originality/value: This paper is unique that covers the mediating role of psychological bias, i.e. OC bias and FL, between the PTs and II of an Indian investor. © 2022, Emerald Publishing Limited.

Purpose: This study aims to analyze the state of knowledge on the relationship between leadership and the firm’s supply chain. The study identifies and examines the existing literature, unveils research gaps and suggests future research directions. Design/methodology/approach: Adopting a systematic review process, a total of 110 articles published in top-ranked academic journals (A* and A category as per ABDC-2019 list) were analyzed. Descriptive, cluster, thematic and regression analyses of citations were performed to garner insights. Findings: The review outcome shows an upward trend of articles studying the influence of leadership in the supply chain. With the highest number of articles, developed countries and manufacturing companies have been the research contexts of the research studies. Clustering reveals eight significant areas where the leader’s involvement in the supply chain is discussed, with several sub-themes emerging within each cluster. Finally, the regression analysis of citations shows that only the journal’s quality matters the most in receiving the highest citation for the articles. Research limitations/implications: As this study considered only A* and A-ranked journals of the ABDC-2019 list, there is a risk of excluding some relevant articles. Originality/value: While the current literature deliberates on recent trends in the supply chain, such as the application of Industry 4.0 practices, this review revolves around the classical theme of leadership and demonstrates its importance in the supply chain. The study is among the first to conduct a bibliometric analysis of articles deliberating on leadership and supply chain issues by grouping the articles into clusters and themes. In the end, the clusters and themes were conceptualized into the “House of Supply Chain Leadership,” of which leadership forms the foundation. © 2022, Emerald Publishing Limited.

The appropriate identification of materials for manufacturing process is essential for sustainable fabrication of products. The material selection is observed as complex process due to involvement of several processing parameters and results. The material selection process comprising of several decision parameters and criteria can be effectively managed using multi criteria decision making (MCDM) approaches. Metal additive manufacturing (MAM) processes has the potential to transform the overall manufacturing processes. It facilitates distributed manufacturing and on-demand production while delivering reduced cost, carbon footprint and energy consumption. Thus, considering this viewpoint, the present study aimed to identify the appropriate material for MAM using MCDM techniques. Information entropy method (IEM) and Combinative distance-based assessment (CODAS) was utilized to determine the priority order of materials. The significant material properties were used as the material criteria for the analysis. The decision-making techniques were deployed using real data of materials. Based on the analysis, aluminum alloy, AlSi12Cu2Fe, Tool steel ‘H13? and aluminum alloy AlSi10Mg were found to be the top ranked materials for metal based additive manufacturing. The implications for industrial practitioners have been discussed based on the analysis to assist the industry practitioners and additive manufacturing experts for selection of appropriate material for fabrication. © 2022