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Protein-energy wasting is a common complication among patients with kidney failure undergoing dialysis. This study aims to develop a homemade oral nutrition supplement (ONS) to fulfill the energy and protein requirements of these patients.

Three formulations of homemade ONS were developed using soybean milk, whey protein isolates and canola oil. Two of these formulations were flavored with pineapple and honeydew juices, respectively. The energy and macronutrient contents were determined using proximate analyses, and mineral contents were determined using inductively coupled plasma optical emission spectroscopy. The acceptance of homemade ONS for five attributes, namely color, taste, odor, consistency and overall acceptability, was assessed using the nine-point hedonic scale.

The homemade ONS provided 198–212 kcal and 8.4–9.6 g protein per 100 mL, which were comparable to commercial products. Similarly, the sodium (45–65 mg/100 mL) and phosphorus (56–66 mg/100 mL) contents were on par with commercial products. However, the potassium content of homemade ONS was higher, ranging from 141 to 155 mg per 100 mL. The sensory evaluation indicated that the formulation added with honeydew juice had a similar degree of acceptance as the commercial ONS, while formulations containing pineapple juice and without added fruit juice were less favored.

A few studies have investigated the development of food products for individuals with kidney failure on dialysis. However, to the best of the authors’ knowledge, this is the first study to focus on developing a homemade ONS specifically tailored to meet the unique nutritional needs of hemodialysis patients. In addition, this research included a comprehensive assessment of the beverage’s nutritional content and sensory attributes.

Devotes the entire journal issue to managing human behaviour in US industries, with examples drawn from the airline industry, trading industry, publishing industry, metal products industry, motor vehicle and parts industry, information technology industry, food industry, the airline industry in a turbulent environment, the automotive sales industry, and specialist retailing industry. Outlines the main features of each industry and the environment in which it is operating. Provides examples, insights and quotes from Chief Executive Officers, managers and employees on their organization’s recipe for success. Mentions the effect technology has had in some industries. Talks about skilled and semi‐skilled workers, worker empowerment and the formation of teams. Addresses also the issue of change and the training that is required to deal with it in different industry sectors. Discusses remuneration packages and incentives offered to motivate employees. Notes the importance of customers in the face of increased competition. Extracts from each industry sector the various human resource practices that companies employ to manage their employees effectively ‐ revealing that there is a wide diversity in approach and what is right for one industry sector would not work in another. Offers some advice for managers, but, overall, fails to summarize what constitutes effective means of managing human behaviour.

Grounded in agency and clan theories, this study aims to examine how, when and why joint liability works as a control mechanism to reduce opportunism among tea supplier groups in China.

Survey data from 82 supplier groups (three respondents per group) were collected.

Joint liability is related positively to peer monitoring (as mediator) and negatively to opportunism, whereas the mediated relationship is moderated positively by group leaders’ perceived legitimate authority and negatively by reciprocity and shared norms.

Opportunism is operationalized as the use of illegal pesticides, the violation of manufacturer–supplier contractual agreements and joint liability, as suppliers’ liability of having the whole group’s seasonal production is rejected by the manufacturer if a single act of opportunism is detected in the group.

Our study demonstrates how and under what conditions the joint-liability mechanism is linked with the reduction of multi-suppliers’ opportunism. We pave the way for future applications of the control mechanism to fields related to inter-organizational governance. Most importantly, we apply Ouchi’s clan theory (1979, 1980) to conceptualize manufacturer–supplier and supplier–supplier relationships in China and provide first-hand evidence to validate its applicability and generalizability to the context. The study also offers insights on network influences in inter-organizational relationships (Gu et al., 2010; Wathne and Heide, 2004) and confirms the important roles of network factors in inter-organizational relationships. In particular, peer monitoring operates as a mediator and normative factors operate as facilitators (moderators) for the joint liability to work as a mechanism to control opportunism in this relationship context.

This study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect.

The authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect.

The empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect.

These findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty.

Most previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.

On December 20, 2021, Viya, a social media influencer (SMI) with the largest number of followers in China, was exposed for having evaded RMB 643 million in taxes during 2019 and 2020. Consequently, she was fined a total of RMB 1.341 billion by the tax authorities. While the strict government regulations demonstrated in the Viya event may build confidence in the consumers for future purchases, the exposure of issues and problems through implementation of the stronger government regulations may warn consumers off. Thus, the main objective of this paper is to examine the effect of government regulations on consumers' usage intentions of live streaming e-commerce by taking the Viya event as an example.

The authors consider both the positive effect of consumers' perceived benefits of the government regulations and the negative effect of their perceived risks of the Viya event on the usage intentions of live streaming e-commerce. After collecting 314 subjects with diverse gender, ages, education levels and income profiles, the data are processed by partial least squares-based structural equation modeling (PLS-SEM) and SmartPLS software.

The results demonstrate that strict government regulations can build trust in consumers of live streaming e-commerce by increasing the perceived benefits of restricting the behavior of SMIs. Among the potential perceived risks (social risk, safety risk and psychological risk), the safety risk is supported to have a negative effect on consumers' trust in live streaming e-commerce platforms. Besides, the authors have also identified different types of usage intentions in live streaming e-commerce, i.e. watching intention and purchase intention, and have empirical support for the positive relationships between the consumers' trust in live streaming e-commerce platforms and different usage intentions.

The authors' findings contribute to the application of commitment-trust theory, institutional theory and organizational control theory in the context of the live streaming e-commerce industry. Particularly, the authors use the Viya event as an example to quantitatively examine the effects of strict government regulations, which enriches the existing literature on this topic.

Breast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.

Breast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.

The performance analysis was done for both segmentation and classification. From the analysis, the accuracy of the proposed IAP-CSA-based fuzzy was 41.9% improved than the fuzzy classifier, 2.80% improved than PSO, WOA, and CSA, and 2.32% improved than GWO-based fuzzy classifiers. Additionally, the accuracy of the developed IAP-CSA-fuzzy was 9.54% better than NN, 35.8% better than SVM, and 41.9% better than the existing fuzzy classifier. Hence, it is concluded that the implemented breast cancer detection model was efficient in determining the normal, benign and malignant images.

This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm (IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images, and this is the first work that utilizes this method.