Search results

This study aims to examine newcomers experiencing maladjustment due to cognitive diversity, specifically, how maladjustment affects their turnover intentions; the mediating role of reported workplace bullying; and the buffering effect of perceived inclusive practices in the hospitality sector.

The authors collected time-lagged data from 403 respondents and analyzed the data through hierarchical regression analyses using statistical package for the social sciences (SPSS) 25.0.

Role ambiguity, low self-efficacy and social exclusion could each lead to newcomers’ reported workplace bullying (NRB). Perceived inclusive practices buffered the impacts of role ambiguity and social exclusion. NRB negatively mediated the relationships between role ambiguity and NRB; and social exclusion and NRB.

Hospitality practitioners should specify work procedures to minimize role ambiguities and record service processes to correct mistakes, reward veterans who help newcomers improve self-efficacy, invite newcomers to develop inclusive practices and review employee comments on third-party platforms to understand factors responsible for turnover intention.

This study contextualized cognitive diversity into newcomers’ maladjustment-bullying-turnover model in China’s hospitality industry. It highlighted the buffering effect of perceived inclusive practices in the relationships between maladjustment and reported bullying and turnover intentions among newcomers and confirmed the important role of self-efficacy in addressing adverse work events.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

This study focuses on listed and over-the-counter (OTC) companies in the Taiwan Stock Exchange. It found that an increase in the ownership proportion of institutional investors (INs), including foreign investors, investment trusts, and dealers can enhance the informativeness of stock prices. The relationship between these factors follows an inverted U-shaped pattern, indicating that excessively high ownership ratios can actually lead to a decrease in the informativeness of stock prices. Additionally, increasing the ownership proportions of foreign investors and investment trusts can reduce the risk of stock price collapse, while dealers show no significant relationship in this regard. This study also reveals that the technical variable of the price deviation rate is an important explanatory factor for post-collapse returns. It is positively correlated with the magnitude of the price decline after a collapse, meaning that stocks with weaker pre-collapse performance experience larger post-collapse declines. When the data during the 2020 pandemic period are excluded, changes in foreign ownership ratios show a significant positive correlation with postcrash returns in both the long and short term. The significant correlation in the short term may be due to a high proportion of foreign ownership. Any reduction in this could put pressure on stock prices, and retail investors may follow suit and sell-off, using foreign investors as a reference. The significant correlation in the long term might be due to foreign investors themselves possibly also trying to avoid the pressure that their own short-term sell-offs could exert on stock prices. The changes in the ownership ratios of investment trusts and dealers indicate that medium and long-term changes have a significant impact on postcrash returns, while the changes in the major players' ownership show no significant correlation. When data from 2020 are included in the analysis, the significance of all INs decreases.

This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.

The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights.

The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered.

As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

The objective of this paper is to investigate the effect of working capital efficiency (WCE) and its components on the composite financial performance of a sample of Indian firms.

Our sample includes 796 non-financial listed firms from 2015–16 to 2021–22. Sample firms’ profitability, liquidity, solvency, cash flow management, and financial and operational leverage have been used to classify them into companies with high composite financial performance (HCFP) and with low composite financial performance (LCFP) by using K-Means Clustering technique. A composite financial performance score (CFPS) of 1 has been assigned to HCFP and 0 to LCFP. We have used logistic regression models with fixed effect to estimate the effect of cash conversion cycle (CCC) and its components, i.e. inventory days, accounts receivable days and accounts payable days on CFPS in the presence of control variables such as growth, leverage, firm size, and age.

The study finds that CCC and inventory days are inversely associated with CFPS. This finding shows that the firms’ WCE leads to superior financial performance on a composite basis.

The research findings are based on samples drawn from the population of the listed Indian non-financial companies. Since the operation, financial practices, working capital policies, and management styles of firms vary greatly among nations, the results of this study should be extended to firms in other countries after taking into account the degree of resemblance to the sample firms.

The findings of this study hold significant value for industry practitioners, as they provide guidance in determining the optimal allocation of funds for working capital and devising strategies for effectively managing inventory levels, credit sales, and vendor payments in order to increase the overall value of the company. This study aims to help investors in building their investment portfolios by identifying companies with superior composite financial performance. Investors can enhance the construction of their investment portfolios by strategically selecting companies that demonstrate superior overall performance.

The results of our study will help companies improve their WCM strategies to enhance their overall value, and their significance increases manifold during economic downturns. Business firms that perform well by efficiently managing their working capital have a multiplier effect on the economy and society at large in the form of GDP contribution, labor income, taxes to the government, investment in capital assets, and payments to suppliers.

To understand the impact of WCE on firms’ performance, the extant working capital literature focuses on some specific characteristics such as profitability, valuation, solvency, and liquidity. The limitation of employing a single parameter is its inability to present the comprehensive performance evaluation of firms. This study is among the earliest studies that focus on the holistic evaluation of WCE's impact on the composite performance of a company.

The purpose of the study was to produce a healthier, convenient and traditional ready-to-eat (RTE) snack option with increased nutritional value, using spent hen meat, dietary fibre (DF) and simple technological methods. The product was designed to be stable without refrigeration and be easily adoptable by local self-help groups, rural women and youth and entrepreneurs in urban and semi-urban areas.

Conventional binder used for making snacks, i.e. rice flour was partially replaced by different sources of antioxidant DFs, i.e. oat flour (T₁ – 10%), finger millet flour (T₂ – 5%) and amaranth flour (T₃ –15%) to prepare spent hen snack sticks (SHSS). The snacks were then packaged in low density polyethylene (LDPE) pouches and evaluated for their storage stability at ambient temperature for a period of 35 days. Their physico-chemical, sensory and microbiological quality was evaluated at a regular interval of 7 days. The proximate composition of developed SHSS was compared to commercially available snack products (chakli/murukku – snacks without meat).

The fibre-enriched SHSS showed significant improvement in nutritive value, as they contained more fibre (p = 0.001) and protein (p = 0.029) than control SHSS. When compared to commercially available snack product SHSS showed three-fold significant increase in protein (p = 0.000) and ash content (p = 0.001) and only 11%–12% total fat as compared to 31% fat in the market-available product. The most acceptable treatment in terms of overall sensory quality and nutritional aspects was T₃; however, T₂ was more shelf-stable during the storage period. The study showed that fibre-enriched snacks can be stored at ambient temperature for up to 35 days without substantial loss in physico-chemical, sensory and microbial quality. Hence, substituting rice flour with DFs can lead to the development of products with better sensory attributes and improved functionality.

The simplicity of the product in terms of composition, machinery and low production costs makes it an easily adoptable one by small-scale entrepreneurs, especially those belonging to semi-urban areas.

Incorporation of spent hen meat, a relatively cheap but abundant source of protein, in RTE products can serve as an effective way to alleviate protein malnutrition, whereas addition of fibre further improves the functionality of the product. The methodology can be easily taken up by small-scale entrepreneurs and create a market for snack-based functional meat products.

This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail.

This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary.

The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin.

The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks.

The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.